goldenbeld pedestrian car crashes a 2006 04

Modelling and exploring pedestrian-car KSI crashes

Charles Goldenbeld, Chris de Blois, Frits Bijleveld & Alex van Gent

C o n f i d e n t i a l A-2006-4

A-2006-4 Confidential Charles Goldenbeld, Chris de Blois, Frits Bijleveld & Alex van Gent Leidschendam, 2007 SWOV Institute for Road Safety Research, The Netherlands

Modelling and exploring pedestrian-car KSI crashes

Trial use of various theoretical and explanatory models

This publication contains confidential information SWOV Institute for Road Safety Research P.O. Box 1090 2260 BB Leidschendam The Netherlands Telephone +31 70 317 33 33 Telefax +31 70 320 12 61 E-mail [email protected] Internet www.swov.nl

Report documentation Number: A-2006-4 Confidential Title: Modelling and exploring pedestrian-car KSI crashes Subtitle: Trial use of various theoretical and explanatory models Author(s): Charles Goldenbeld, Chris de Blois, Frits Bijleveld & Alex van Gent Project leader: Paul Wesemann Project number SWOV: 40.103 Keywords: car, pedestrian, crash, risk, model, state space, time series Contents of the project: The present report describes an attempt to understand and model

the time series of exposure, crashes and risk for the accident type pedestrian-car KSI crashes.

Number of pages: 172 + 55 Published by: SWOV, Leidschendam, 2007

SWOV publication A-2006-4 Confidential 3 SWOV Institute for Road Safety Research - Leidschendam, the Netherlands

Summary

Since 2003 the SWOV Institute for Road Safety Research has been developing a road safety assessment department which aims to find better explanations for past numbers of road deaths and injuries. This report is one in a series of assessments written in the Assessments and Outlooks programme. The assessments aim to evaluate road safety developments over time and to identify influencing factors for different crash types. This report studies the development of the pedestrian-car crash risk over time. The knowledge gathered in the assessments will provide the basis for the long term prognoses for road safety that will be made in the Outlooks which are made in the Assessments and Outlooks programme. The Assessments and Outlooks programme formulates the problem as follows: "How can the development of road safety in the Netherlands be explained with the use of knowledge of traffic developments, societal trends and special interventions?" To answer this question SWOV has subdivided road safety into a number of crash types. For each of these crash types, a study has been undertaken (1) to describe the development of the crash rate over time and identify the specific conditions or circumstances that were involved in changes of the rate, (2) to find out which interventions and developments have likely or evidently influenced the occurrence of these crashes, (3) to model the development of the crash rate over time. The present report presents the study of one particular crash type: crashes between pedestrians and cars where serious injury to the pedestrian occurs (so-called pedestrian-car KSI crashes, where KSI is an abbreviation of "Killed or Seriously Injured"). The study used a general approach to modelling crash risk over time. For this approach the following steps were taken: a descriptive crash data analysis, literature scan, modelling crash risk, explorations and analyses, assessment and recommendations. The report describes how various theoretical and explanatory models were tried to investigate pedestrian-car crash risk over time. The models used are state space models and linear regression models. Pedestrian-car risk has already shown a strong decrease in the early and mid-seventies when the level of implementation of formal road safety measures directed at pedestrian safety has been quite low. The decrease in pedestrian-car risk which occurred in the 1970s cannot be explained by road safety measures, and more likely has to be explained in terms of generational learning or informal social protective mechanisms. For several subdivisions of pedestrian-car crashes, the most frequently found trend break years have been 1986 and 1991. The trend break in pedestrian-car risk in 1991 is most likely due to combined influences of the introduction of the public transport pass for students and economic recession. The study has resulted in the recommendation that, as was done by SWOV, in a number of subsequent steps (descriptive analysis of the development of

4 SWOV publication A-2006-4 Confidential SWOV Institute for Road Safety Research - Leidschendam, the Netherlands

crashes over time, literature scan, regression analysis) all hypotheses about development of both risk and exposure are formulated before the state space models are designed. It is preferable that as much as possible knowledge is generated before the start of state space modelling since the state space models can be sensitive to particular assumptions which preferably should be derived from prevailing knowledge.


Contents

Foreword 9

1. Introduction 11 1.1. Problem description 11 1.2. Outline of the report 11 1.3. Boundaries and definitions 12

1.3.1. Subdivision of total road safety in crash types 12 1.3.2. Crashes and casualties 14 1.3.3. Registered or adjusted crash numbers as basis for models 15 1.3.4. Definition of risk 18 1.3.5. Time horizon and time step 21

1.4. Approach 22 1.4.1. Stepwise approach 22 1.4.2. Hypothesis handling 23 1.4.3. A new tool for estimating model parameters 24

2. Descriptive analysis of crash data 28 2.1. Outline 28 2.2. Method 28

2.2.1. General approach 28 2.2.2. Method of analysis of casualties 29 2.2.3. Method of selecting subdivisions 29 2.2.4. Exposure and risk 32

2.3. Data 33 2.3.1. Data on casualties 33 2.3.2. Possible measures of pedestrian exposure 34 2.3.3. Possible measures of car exposure 40 2.3.4. Selection of exposure measure 41

2.4. Results 43 2.4.1. General 44 2.4.2. Relevant subdivisions 66 2.4.3. Exposure and risk 67

2.5. Overview 72 2.5.1. Main conclusions 72 2.5.2. Hypotheses 73

3. Developments and interventions in 1970-2003 77 3.1. Outline 77 3.2. Types of influence factors 77 3.3. Results of literature scan 78

3.3.1. Main literature sources 78 3.3.2. Risks of different travel modes in the seventies 79 3.3.3. Mobility and lifestyle developments 82 3.3.4. Main safety interventions 84 3.3.5. Specific road safety measures 86 3.3.6. Other developments 95

3.4. Overview 105 3.4.1. Main conclusions 105 3.4.2. Hypotheses 106


4. Trends in crash risk 1978-2003 109 4.1. Outline 109 4.2. Method 109

4.2.1. Model description 109 4.2.2. Model estimation 111 4.2.3. Seasonal data 113

4.3. Results 113 4.3.1. The overall model of pedestrian-car risk 114 4.3.2. Development of the trend component of risk for

specific subdivisions 116 4.3.3. Development of the seasonal component of risk for

specific subdivisions 123 4.4. Overview 137

4.4.1. Review of hypotheses 137 4.4.2. Main conclusions 139

5. Further explorations 141 5.1. Outline 141 5.2. Method 142

5.2.1. Linear regression model 142 5.2.2. Variables 143 5.2.3. Stepwise approach to model construction 145

5.3. Results 146 5.3.1. Model 1: registered number of victims

1976-2004 by month 146 5.3.2. Model 2: adjusted number of victims

1976-2004 by month 148 5.3.3. Model 3: registered number of 0-11 aged victims

1976-2004 by month 151 5.4. Overview 153

5.4.1. Review of hypotheses 153 5.4.2. Main conclusions and new hypotheses 154

6. Discussion and recommendations 158 6.1. Overview of hypotheses 158 6.2. Main conclusions 161 6.3. Recommendations 162

6.3.1. Steps towards explanatory models 162 6.3.2. Choice of exposure measure 164 6.3.3. Prospects of comparative analyses 164 6.3.4. Other recommendations 165

References 166

Appendix 1 Database sources 173

Appendix 2 Overview of results of descriptive analysis 175

Appendix 3 Scan of interventions 183

Appendix 4 Ranking of interventions 187

Appendix 5 Smoothed model states 'Total' 189


Appendix 6 Smoothed model states 'Male - Female' 190

Appendix 7 Smoothed model states 'Age groups' 191

Appendix 8 Smoothed model states 'Inside – Outside urban area' 194

Appendix 9 Smoothed model states 'Working day - Weekend' 195

Appendix 10 Smoothed model states 'Road section - Intersection' 196

Appendix 11 Smoothed model states 'Day - Night' 197

Appendix 12 Smoothed model states 'Road and weather conditions' 198

Appendix 13 Smoothed model predictions 'Total' 200

Appendix 14 Smoothed model predictions 'Male - Female' 201

Appendix 15 Smoothed model predictions 'Age groups' 202

Appendix 16 Smoothed model predictions 'Inside – Outside urban area' 205

Appendix 17 Smoothed model predictions 'Working day - Weekend' 206

Appendix 18 Smoothed model predictions 'Road section - Intersection' 207

Appendix 19 Smoothed model predictions 'Day - Night' 208

Appendix 20 Smoothed model predictions 'Road and weather conditions' 209

Appendix 21 Results linear regression analyses 211

Appendix 22 Overview hypotheses 223


Foreword

We thank the following persons in helping for commenting on drafts of the present report and providing suggestions for improvement: − Jacques Commandeur − Paul Wesemann − Henk Stipdonk − Rob Eenink − Divera Twisk − Boudewijn van Kampen − Atze Dijkstra − Peter Polak Our appreciation goes to Niels Bos for preparing and delivering data for some of the analyses. We owe Paul Wesemann our gratitude for managing the project as part of which this report was completed. Leander Hepp deserved our thankfulness for his work on the descriptive analysis of crash data.


1. Introduction

1.1. Problem description

Since 2003 the SWOV Institute for Road Safety Research has been developing a road safety assessment department which aims to find better explanations for past numbers of road deaths and injuries. This report is one in a series of assessments written in the Assessments and Outlooks programme. The assessments aim to assess road safety developments over time and to identify influencing factors. The reports in this series contain the assessments of different crash types. This report studies the development of the pedestrian-car crash risk over time. The knowledge gathered in the assessments will provide the basis for the long term prognoses for road safety that will be made in the Outlooks which are made in the Assessments and Outlooks programme. For the Assessments and Outlooks programme, the problem can be formulated as follows: "How can the development of road safety in the Netherlands be explained with the use of knowledge of traffic developments, societal trends and special interventions?" In order to answer this question, SWOV has subdivided road safety into a number of important crash types. For each of these accidents types, a study has been done (1) to describe the development of crash risk over time and identify the specific conditions or circumstances which were involved in changes of risk, (2) to find out which interventions and developments have likely or evidently influenced the occurrence of these crashes, (3) to model the development of crash risk over time. The present report aims to answer these questions for one particular type of crash, i.e. crashes between pedestrians and cars where serious injury to the pedestrian occurs (so-called pedestrian-car KSI accidents where KSI is an abbreviator for "Killed or Seriously Injured"). In the following section, we provide the reader with an outline of the report. In view of the length of the report extra attention is given to those report sections that provide main conclusions or an overview of findings. In the following sections, a brief description of the general approach to understanding and explaining crash types over time is given. First, Section 1.3 describes the boundaries to this study and some important definitions used. Second, Section 1.4 contains a description of the approach followed.

1.2. Outline of the report

This report is structured as follows. Chapters 3 to 5 describe four different research activities (accident analysis, literature study, state space-based time series modelling, exploratory modelling), which are all related to the central aim to acquire knowledge about the development of pedestrian-car risk. Chapter 2 describes results of specific accidents analyses to explore the specific conditions or circumstances that are related to changes of risk. Chapter 3 describes developments and interventions which likely or evidently have influenced pedestrian-car KSI crashes. Chapter 4 presents the results of models of pedestrian-car crash risk over time. Chapter 5


contains the results of additional explorative analyses, which served to test the effect of economy and weather, to evaluate the possible consequences of using registered instead of underregistration-adjusted casualty numbers, and to evaluate the possible additional value of the separately analysis of 0-11 years old victims. In Chapter 6 we finish with a general discussion of results and with recommendations for further work in this area. More specifically, in this closing chapter, the knowledge of developments and interventions, of development of risk and of specific conditions that are related to changes in risk, is combined to arrive at final conclusions, interpretations and remaining questions. This introductory chapter describes the role of each of the four research activities in the total approach (Sections 1.3 and 1.4) aimed at a better understanding of the development of pedestrian-car risk. Although all four research activities have been undertaken to improve knowledge about pedestrian-car risk, the different methods used have led to diversity in information. Very often the availability or quality of the information was not enough to use it in final modelling of time series of pedestrian-risk. Nonetheless we have also included global or less precise quantitative information in the report in order to be complete in our description of the outcomes of each research activity and of the state of present knowledge. The diversity of information stresses the importance of a clear structure for the report. In order to achieve such a clear structure, Chapters 3 to 5 all have an introduction section, a method and results section, and an overview section that summarizes main results. The reader should note that the overview sections in Chapters 3 to 5 contain hypotheses that were derived from the accident data analysis or literature study. In essence, these hypotheses tie the different research activities (analysis, literature study, modelling) together since the results of each research activity provide feedback to change, accept, or reject an hypothesis. Appendix 22 provides an overview of all hypotheses and the feedback on these hypotheses from each of the four separate research activities.

1.3. Boundaries and definitions

This section describes the most important boundaries and definitions used in this study. Section 1.3.1 describes the subdivision of total road safety in a number of main crash types that were selected for separate study, and the arguments for making this division. Section 1.3.2 defines pedestrian-car KSI crashes and casualties. Section 1.3.3 explains why we used registered numbers instead of adjusted crash numbers as basis for modelling. Section 1.3.4 provides the definition of risk applied in this report.

1.3.1. Subdivision of total road safety in crash types

As we have mentioned, the present report aims to understand how the development of pedestrian-car crashes and pedestrian-car crash risk has evolved over time and which interventions and developments influenced this development. The report is part of a wider series of reports that focus on different types of crashes, e.g. single car crashes, crashes between heavy vehicles and cars etc. In this section we describe the criteria that led to a subdivision of total crashes into particular crash types for further study.


For any analysis of the development of crash risk, the following dimensions are basic: travel mode and age of victim, road type and time of crash. Since crash risk differs strongly per travel mode and since for each separate travel mode both the number of crashes and crash risk again differ strongly dependent upon collision opponent, it was decided at the very start of the project to use travel mode of victim and of opponent as main dimensions for a classification of different risk groups. A practical advantage of this decision was that exposure data for different travel modes were available for modelling. The other major variables, age and road type played no role in the initial grouping of possible risk groups, but they were used as discriminatory variables in various descriptive analyses and in some modelling efforts. Once it was decided that the major division in risk groups would be based on the travel modes of both primary victims and collision partners, the question remained which particular combinations of travel modes were to be chosen for further modelling efforts. Based on ten travel modes (pedestrian, bicyclist, moped, sloped, motorbike, car, van, truck, bus, other) the total accident population can be subdivided into 110 crash types: 10 travel modes times 10 travel modes (=100) + 10 single vehicle crash types. Of these 110 crash types ten crash types were identified that together covered 75% of all traffic casualties.

Crash type with serious injury

Size

Risk

Growth

Lethality (fatalities/injured)

Total score

Car-car ++++ ++++

Bike-car +++ (+) +++(+)

Moped-car +++ ++ +++++

Car single +++ ++ + ++++++

Moped single + ++ +++

Pedestrian-car ++ + + ++++

Motorbike-car + + + +++

Motorbike single + + ++

Bicycle-truck ++ ++

Car-truck + + ++ ++++

Car-(mini)van + ++ +++

Table 1.1. Evaluation of ten crash types.

These ten crash subtypes were assessed in terms of the following criteria: size, risk, growth over time, and lethality. Size was decided to be the most important of the four criteria. Therefore, size was scored 0 to 4 plusses and the other criteria 0 to 2 plusses. Risk was computed as the number of victims over 1995-2003 divided by the number of kilometres travelled by the victim travel mode over the same period. Lethality was defined as the number of fatalities divided by the number of seriously injured. Table 1.1 shows the results of this assessment.


The criteria 'size' and 'growth over time' both have to do with the present or expected magnitude of the problem. The criteria 'risk' and 'lethality' are stronger related to the perception of 'risk' or ´danger' and often are partly indicative of public or social acceptability of the problem. Generally speaking, crash types with high risk and high lethality raise more emotion and more motivation to do something about them than crashes with low risks or low lethality. On the basis of Table 1.1 it was decided to focus the first efforts of studying crashes and crash risk over time on six crash types: car-car, moped-car, car single, pedestrian-car, car-truck, and bicycle-car. Although some of these crash types will likely share common influence factors (e.g. changes in infrastructure), a separate analysis of development of crashes and crash risk for each of these crash types seems justified. First, a shared influence factor does not necessarily mean that this influence factor has been equally effective in changing the number of crashes for each separate crash type. Second, a separate analysis is also advisable on the grounds that development of mobility for each of these crash types will differ. Since the number of annual fatal crashes was rather low for some of the crash types in Table 1.1 and since it can be assumed that dynamics of causation are to a large extent the same for crashes with fatal consequences and crashes with serious injury consequences, it was decided to focus the modelling on all crashes leading to death or serious injury (so-called killed or seriously injured 'KSI' crashes).

1.3.2. Crashes and casualties

In Section 1.3.2.1 a definition of pedestrian-car KSI crashes and casualties is given. Then, Section 1.3.2.2 motivates our choice to use the casualty numbers in the data and model analyses and not the crash numbers.

1.3.2.1. Definition of pedestrian-car KSI crashes and casualties

In this report, pedestrian-car KSI crashes concern crashes between a pedestrian and a (passenger) car, where the pedestrian is killed or seriously injured. A pedestrian-car KSI casualty is a pedestrian who is killed or seriously injured because of a collision with a car. To better understand this crash and casualty type we define the two transport modes involved and the accident type using definitions from UNECE / ECMT / EUROSTAT (2003): Pedestrian (involved in an accident): Any person in an injury accident other than a passenger or driver. Included as pedestrians are "occupants or persons pushing or pulling a child's carriage, an invalid chair, or any other small vehicle without an engine. Also included are persons pushing a cycle, moped, roller-skating, skateboarding, skiing or using similar devices. Driver (involved in an injury accident): Any person involved in an injury accident who was driving a road vehicle at the time of the accident.


Passenger (involved in an injury accident): Any person involved in an injury accident, other than a driver, who was in or on a road vehicle or in the process of getting in or out a road vehicle. Passenger car A road motor vehicle, other than an motorcycle, intended for the carriage of passengers and designed to seat no more than nine persons (including the driver). The term "passenger car" therefore covers microcars (need no permit to be driven), taxis and hired passenger cars, provided that they have fewer than ten seats. This category may also include pick-ups. Road vehicle: A vehicle running on wheels and intended for use on roads. Road motor vehicle: A road vehicle fitted with an engine whence it derives it sole means of propulsion, which is normally used for carrying persons or goods or for drawing, on the road, vehicles used for the carriage of persons or goods. Accident between road vehicle and pedestrian: Any injury accident involving one or more road vehicle and one or more pedestrian. Included are accidents irrespective of whether a pedestrian as involved in the first or a later phase of the accident and whether a pedestrian was injured on or off the road. According to above definitions a horse rider is no road vehicle and thus a horse rider who is involved in an accident is also categorized as a pedestrian.

1.3.2.2. Choice between crashes and casualties

From 1976 to 2003 the ratio between pedestrian-car casualties KSI and pedestrian-car crashes KSI has steadily increased from 1.03 to 1.05, with an average value of 1.04 and a maximum value of 1.08 in 2002. This means that on each 100 pedestrian-car KSI crashes there is an average of 104 pedestrian casualties. So, on the average in at most 4% of the pedestrian-car KSI crash cases more than one pedestrian was killed or seriously injured. Because of this small proportion, the weak annual increase (0.07%), and because more properties are known of pedestrian-car casualties, in this report only the casualty numbers are investigated and not the crash numbers.

1.3.3. Registered or adjusted crash numbers as basis for models

Besides the questions regarding crash types to be studied and regarding the inclusion of horse riders in the definition of pedestrian, we had to do with the question whether to model registered crash data or to model crash numbers that were adjusted for under registration. Not all road accident victims are recorded by the police. SWOV developed extrapolation methods to compare the data from different sources with each other. This process has led to the extrapolated, ‘real' numbers.


Until late nineties, it was assumed that the numbers of traffic fatalities and fatal accidents as recorded by police were accurate. Then the results of a CBS study were published, covering the year 1996, indicating that even these numbers were incomplete. In the study, data from three different sources were compared: police registration, cause of death statistics, and juridical data concerning deaths. An extra 7% to 8% road fatalities complying with the definition used by the police (i.e. died as result of a traffic accident, on a Dutch public road, and within the 30-day limit) were detected. This study also showed that completeness of the original police registration of fatalities differs with respect to type of road user. Under registration of fatally injured cyclists is the highest (about 15%), while for most types of motor vehicle fatality under registration is clearly less then average. In practice, the less complete registered numbers of fatalities are often still used for time series and other analyses, since the real numbers only represent the years since 1996, and do not cover all possible distributions. It has long been known that the registered number of hospitalised based on police registration is far lower than the real number of in-patients. This fact was established by at least two independent sources: regular enquiries and data from the Dutch national hospital data registration, called LMR (an continuous registration, owned by Prismant, an organisation working in the field of public health). To establish the real number of hospitalised, SWOV has carried out several studies in which data from both police registration and LMR were statistically linked (matched). Based on these studies, SWOV developed a method to calculate the real number of hospitalised, based on both the LMR data and the registered number. The method is used by AVV to determine the real number of injuries. Average completeness of the police registration appears about 60%. Here also, we see large differences with respect to type of road user, as well as collision type. Casualties from traffic accidents including motor vehicles, are far better registered than those including non-motor vehicles. For the present research no data were available on the registration level of pedestrian-car crashes. The 'next best' solution is to look at registration level of fatalities and in-patients for general travel modus moped. Figure 1.1 presents the development of registration level for pedestrian fatalities and in-patients over time.


40

45

50

55

60

65

70

75

80

85

90

95

100

1989 1992 1995 1998 2001 2004

Perc

enta

ge re

gist

rate

d

Registration % in-patients pedestrians Registration % fatalities pedestrians

Figure 1.1. Registration level of fatalities and in-patients for travel modus pedestrian (Source: SWOV-AVV)

As can be seen in Figure 1.1, the registration level for pedestrian fatalities hovers around between 83% and 93%. The registration level of pedestrian in-patients in the period 1989-2003 has decreased from above 70% in 1990-1991 to below 60% in the years 2000 to 2003. It should be noted, however, that pedestrian-car crashes leading to casualties (i.e. in-patients) very likely have a higher registration level than the below 60 % for all pedestrian in-patients. With pedestrian-car crashed there is always the involvement of an (insured) car and nearly always the involvement of an insurance company that requires official recording of the crash. Those crashes where an insurance company is involved, such as is the case for nearly all pedestrian-car crashes, have a registration level that is very likely about the same as the in-patient registration level for car occupants, i.e. on the average about 80%. For the present research we decided to model registered KSI crash numbers rather than adjusted KSI crash numbers. Several considerations played a role in this decision. First, registration levels for pedestrian-car crashes are not available; the next best option would be to use registration levels for pedestrian victims. Second, registration levels are not available for possibly relevant subdivisions (e.g. inside and outside urban areas, on intersections or road sections). Third, the year-to-year variations in registration level for both fatalities and in-patients in the period 1989-2003 did not seem high enough to warrant special remedial action. And fourth and maybe most important, there is a lack of knowledge about the confidence bounds of the registration level, especially for specific crash types. If, for example, we would choose to use registration levels as an extra explanatory variable in our models, we would need to have year-to-year information about the certainty or reliability of the registration levels. In practice, only for a few specific years, these reliability intervals were estimated. Doing this we still have to take into account the possible influence of this decision of not adjusting for registration level on model results. For example, when modelling risk on the basis of registered crash numbers a declining


trend in risk which is in fact caused by declining registration level could be attributed to some explanatory variable, e.g. the building of infrastructure. To get an idea of this possible influence, in Chapter 5 an exploratory analysis on the basis of adjusted numbers is carried out. The results of this analysis are compared to the results of an analysis on the basis of registered numbers.

1.3.4. Definition of risk

Subsection 1.3.4.1 contains a discussion of the concepts of 'risk' and 'exposure'. Subsection 1.3.4.2 descibes how 'risk' is made operational in the consecutive chapters of this report.

1.3.4.1. Exposure and risk

Our basic conceptual model of road unsafety starts from the assumption that the number of casualties is the product of (casualty) risk and exposure to risk. Both risk and exposure are unobservable, latent variables. In general, exposure is expressed in some measure of mobility (e.g. the number of kilometres travelled by vehicles or persons). Casualty risk is the number of casualties per unit of exposure. It should be noted here that risk and exposure are concepts which are closely related. The way of expressing exposure also determines the expression of risk and vice versa. If 'exposure' is viewed as 'exposure to risk' (e.g. Hakkert, Braimaister and Van Schagen, 2002) the general exposure to risk is a sum of different types of exposures to risks. Total exposure can be subdivided into low risk exposure and high risk exposure, for example: − kilometres travelled by drivers wearing the safety belt and by drivers not

wearing the safety belt, − kilometres travelled on motorways and on 80 km/hr roads, − kilometres travelled during daylight or in the night, − kilometres travelled by experienced drivers and by inexperienced drivers,

etc. From this subdivision of exposure according to several risk levels, it becomes clear that risk is in fact determined by the balance between low risk and high risk exposure. So, with regard to the present task of constructing a reliable model of crash risk, we will have to find and take into account the relevant subdivisions of exposure. The relevant subdivisions can be described as those subdivisions which define subgroups that have different risk level or different development of risk level and represent a sufficient large number of crashes or casualties. The foregoing also illustrates that whereas in theory we may distinguish between risk and exposure as separate entities, from another viewpoint, it is quite easy, and even elegant, to see these entities as interchangeable faces of one reality. If we just use ‘risk’ and ‘exposure to risk’ as our basic terms of reference we have not very precisely defined our basic construct. A more precise, formalised definition could have a structure like the following: exposure to risk is exposure of persons with characteristics x in combination with vehicles or objects with characteristics y to situations with characteristics z.


Such a definition would clarify the conceptual universe or the theoretical population of the construct ‘exposure’. Likewise, the concept of risk could then be formally dissected as the total of behaviour, person, and environment characteristics that influences the outcomes of the combination of persons, vehicles, objects and situations. Such precise formalised definitions are actually non-existent in the road safety literature since most of the data to match the various combinations as specified in the definitions would be lacking. Basically, the road safety literature provides us with rather 'simple' definitions that match the level of available data. Thus, exposure is often generally described as both the amount and way of travel, and risk is expressed as a road safety outcome (often expressed in number of accidents or injured) divided by exposure. With these simple definitions of risk and exposure, there are several examples that show how thin the dividing line is between these two concepts. For example, should we speak of a change in risk or exposure or both when a 1-lane road changes into a 2-lane road, when drivers choose to drive on safer roads, when children are being supervised in walking to a school, or when children are being brought in car to a school? If parents in the Netherlands increasingly bring their children to school with a car, at the time of school start there will be more cars driving near the school. The changed exposure of one group of children (being brought by car) may at the same time be associated with increased risk for another group of children (walking to school). In the present study, we made use of population numbers as exposure measure for pedestrian-car crashes. Although this may not be the best theoretical measure of exposure, we had methodological and practical reasons for choosing this measure. In Section 2.3.4 we will provide our arguments for this choice. Although there may be sound arguments for choosing this exposure measure for the modelling efforts in the current study, it is clear from the start that this measure of exposure does not capture all developments in exposure that are relevant for car-pedestrian crashes. The population numbers do not tell us anything about specific exposure developments that may affect the occurrence of pedestrian-car crashes, such as: whether children are more frequently being brought to schools with cars, whether people in general are walking more frequently, whether walking in general occurs more frequently in car-free spaces due to new city and area planning developments etc. This drawback is not particularly associated with the choice of population numbers. If we would have chosen another exposure measure such as pedestrian kilometres, or car kilometres, or number of trips we would still have a rather simple measure of exposure that only captures part of the exposure that is theoretically relevant when we think of all the ways in which exposure may play a role. The question then becomes what are the consequences of using simple exposure data to model pedestrian-car risk and crashes. If we use an exposure measure that does not reflect all relevant changes in exposure that may affect pedestrian-car crashes, this means that part of the (positive or negative) exposure influence will be included in the risk term in the model. If crashes go down because of exposure developments that are not reflected in our exposure measure, this will be reflected in the ‘risk’ term


of the model. For some reasons this at first glance fundamental problem is not so serious as it may seem. First, even though risk and exposure may be distinguished for the purpose of model fitting, in theory it seems easier to see these concepts as overlapping or even interchangeable then as separate. Second, for the predictive and explanatory characteristics of the model it is not that important whether a particular structural development is being reflected in the risk term or the exposure term; the model itself arrives at the same function whether the safety developments is included in the risk or the exposure term. Third, risk or exposure in themselves are not the prime focus of interest; they are only means to an end, namely understanding and predicting the development of crashes. Finally, we possess additional evidence from research and analysis that allows us to build a ‘meaning context’ for the outcomes of the model and to look beyond the ‘face value’ of the model outcomes.

1.3.4.2. Operational definitions of risk

Since in different chapters and sections of this report risk has been quantified in different ways, it is worthwhile to be clear about some basic meanings of the risk concept as used in the report. In our descriptive analyses (Chapter 2) and in some of our exploratory models (Chapter 5) analyses have been done in which risk was quantified as number of casualties divided by population size. This is a simple quantification method that is based on the idealistic assumption that available casualty and exposure data are reliable and trustworthy and that therefore estimates based on direct calculations of these numbers are useful. In the state space models (Chapter 4), we present data on what is technically called 'the smoothed level of risk'. The smoothed level of risk can be understood as the risk estimated on the basis of casualty and exposure data that are disposed of 'noise' and moreover is corrected for dynamic uncertainty in the casualty and exposure data. In contrast with earlier risk calculations, the risk in the state space model is calculated on modelled, 'purified' data rather than on the observations themselves. Based on how much exposure and casualty data vary over time and how much the relationship between these two time series varies over time, the state space model replaces observed data with estimated 'true' data and only then fits a risk curve through these purified data. In a theoretical sense, the state space models provide us with the theoretically 'best' risk estimates. To summarize, in the state space models (Chapter 4) casualty and exposure data are disposed of (observation) error and then risk is computed as the corrected number of casualties divided by the corrected exposure, where exposure is approximated by the population size. In our hypotheses we use the term risk in a very generic way (i.e. risk is number of casualties per unit of exposure) without specifying the quantification method. In that way the hypotheses could be investigated by analyses using different quantification methods. As we have stated before, the terms of 'risk' and 'exposure to risk' are theoretically closely 'interwoven'. In the world of state space modelling, the crash and exposure data are operationally also interwoven since the


parameter estimation procedure of state space modelling uses the uncertainty properties of the interrelationship between both time series to arrive at new purified data. This interweaving does not mean that we are totally unable to separate changes in mobility participation by specific road user groups and changes in 'pure' risk. With changes in 'pure' risk we refer to changes in risk that derive from changes in behaviour or in situations with no change in mobility participation by different road user groups. Blois, Goldenbeld and Bijleveld (2007) showed that state space models are able to provide fairly clear evidence as when a change in risk is to be attributed to a change in pure risk or to mobility change.

1.3.5. Time horizon and time step

The time horizon of this study is 1976-2004. From mid seventies till 2004 accident numbers tended to decrease. To maximize the possibility to improve our insight in risk development and the underlying factors, we chose the longest time horizon for which reliable accident data were available, i.e. from 1976 until 2003. For these years we described the development of accident data in time and scanned literature to find the possible underlying influence factors. To estimate risk, however, we also needed exposure data, which in our case were data on population size by gender and age. These data were available from 1978. Therefore, risk analyses consider the period 1978-2003. The 2004 accident data have long during 2005 been subject to a discussion about their reliability. This discussion started because an amazing strong fall of the number of killed occurred, while there seemed to be no clear causes for this fall. For many crash types also the number of KSI crashed declined strongly. To find the causes of the strong fall of the number of killed a special research was performed (Stipdonk, 2005). This study came with the following main explanations: − 2004 was a favourably elapsed year with favourable circumstances for

road safety. − Speculative favourable circumstances are the stagnation of mobility

growth and the wet and warm summer. − 2002 and 2003 were relatively unfavourable years. To improve our understanding of the possible causes we decided to include 2004 in the descriptive and explorative analyses, but not in the final model analysis. In this report several time steps are used: year, season (or: quarter), and month. In the descriptive analysis we considered yearly data fine enough to give overall insight in the development of risk, exposure, and number of casualties. For the state space model analysis, however, the use of seasonal data had important advantages, which are: − Because of more observations the introduction of an intervention variable

has less influence on the chance to find another intervention. This was found to be a major drawback of the use of annual data in the moped-car study (Blois, Goldenbeld, and Bijleveld, 2007).

− The study of seasonal effects provides us with new, possibly very important insights. Among others, the effects of seasonal variables, like weather and economy, on the annual average are better estimated.


In the explorative analyses we used monthly data in order to better be able to investigate the possible contribution of economic and weather variables to changes in pedestrian-car KSI casualty risk, which was the main research question for these explorative analyses. The next section will describe in more detail the approach followed.

1.4. Approach

Section 1.4.1 presents the stepwise approach followed. Then, Section 1.4.2 gives a description of how we used hypotheses to formulate our conclusions. Section 1.4.3 describes the (new) tool which we used for estimating model parameters. In this latter section, also a comparison of this tool with other tools as linear regression and the Box-Jenkins approach is made. Furthermore, in this section a short overview of literature on state space analysis applied to road safety analysis is given.

1.4.1. Stepwise approach

The approach to understand the development of the number of pedestrian-car KSI casualties and pedestrian-car KSI casualty risk over time has the following steps: 1. Descriptive data analysis

a. on the basis of national statistics describe the development of pedestrian mobility and car mobility, select a measure for the exposure to the danger of pedestrian-car crashes, and finally describe pedestrian-car KSI casualties and pedestrian-car KSI risk;

b. identify by further data analysis conditions or circumstances that may be related to changes in casualty risk;

c. formulate hypotheses on the development of pedestrian-car casualty risk and the possible underlying influence factors;

2. Literature scan a. based on the available research literature describe developments

and interventions that evidently or likely have influenced pedestrian-car KSI casualties;

b. make best effect estimates for each of these developments and interventions;

c. compare the results of the literature scan with the hypotheses from the descriptive data analysis and, if possible, formulate new hypotheses;

3. Modelling casualty risk a. model pedestrian-car KSI casualty risk and pedestrian-car exposure

over time and find the possible breaks in the trend and seasonal of risk;

b. model pedestrian-car KSI casualty risk for the conditions or circumstances which the descriptive analysis (1b) identified as being related to changes in casualty risk over time and find the possible breaks in the trend and seasonal of risk;

c. compare the results of the model analysis with the hypotheses and best effect estimates from the descriptive data analysis and the literature scan and, if possible, formulate new hypotheses;

4. Further explorations


a. dependent of the extent to which the hypotheses have been tested in the previous steps state additional research questions to further test hypotheses;

b. select appropriate (exploratory) analysis technique for these research questions and perform explorative analyses;

c. compare the results of the exploratory analysis with the hypotheses from the previous steps and, if possible, formulate new hypotheses;

5. Synthesis a. give an overview of hypotheses and test results from the descriptive

analysis, literature scan, model analysis, or exploratory analysis; b. state the final conclusions about the development of pedestrian-car

KSI casualty risk and the underlying influence factors; c. give recommendations for further research.

Hypotheses

Descriptive analysis I

Literature scan II

Model analysis III

Exploratory analysis IV

New hypotheses are framed and/or old hypotheses are modified.Hypotheses are input for the next step of the analysis. Synthesis

Figure 1.2. Approach to modelling casualty risk.

Figure 1.2 presents the steps in our approach to constructing models of pedestrian-car KSI casualty risk.

1.4.2. Hypothesis handling

With the aim of summarizing the most relevant findings of each chapter, each of the following chapters (2 to 5) is concluded with the formulation of new hypotheses or the modification of old hypotheses from a previous chapter. We cannot claim that the hypotheses' validity is tested, implying that they are proven true or false. What we can claim is that outcomes of the several analyses carried out do or do not support their plausibility. To make it easy to relate a hypothesis to the period of years to which it can be applied and to the analysis on which it is based, the following coding is used: Hy1-y2.n.x, in which − H stands for Hypothesis;


− y1 and y2 for the start and end of the periods: 79-79, 80-85, 86-90, 91-91, 92-99, 00-00, 01-03, 04-04;

− n for the serial number per period; − x for the letter code of analysis: A = descriptive analysis; B = literature

scan; C = state space analysis; D = linear regression analysis. For example: H92-99.2.B is the second hypothesis which concerns the period 1992-1999. It is based on the results of the literature scan. Modification of a hypothesis of a previous chapter only changes the letter code: from A to B, or B to C, etc. If a new hypothesis on a period is introduced, then the serial number is changed as well.

1.4.3. A new tool for estimating model parameters

The SWOV research program 2002-2006 aims to understand how crash risk changes over time as a results of specific developments in road safety or in society. In the previous section we have described the theoretical basis of our models. Modelling crash risk requires theory but also statistical techniques to estimate model parameters. The present research makes use of time series analysis with state space models, also called structural time series analysis. In the field of road safety, this is a relatively new statistical tool that has not been applied very much. In other fields, such as control system design, state space models have been more generally applied. Some recent publications have highlighted the possible advantages of this new technique for time series analysis in general (Koopman, 2000; Durbin and Koopman, 2001; Commandeur and Koopman, 2007) and, specifically, in the field of road safety (Bijleveld, 1999; Bijleveld and Commandeur, 2005). Also, new software and increased calculative powers of personal computers make this technique more feasible than ten years ago. The basic task of all statistical modelling is to separate 'true' values from 'error' values, or to separate 'signal' from 'noise'. In our view, the main advantage of state space models is that they are better able than any other current statistical technique to take into account that data are flawed with measurement error and that relationships between model variables over time may vary as result of dynamic uncertainty. If the available data itself is limited, uncertain or somehow flawed, state space models cannot miraculously transform these data into 'truth' or 'certainty', but it can represent these data in a way that does not fool us by capitalizing on chance outcomes. The following subsections explain the advantages of structural time series approach over linear regression (Section 1.4.3.1) and traditional time series analysis (Section 1.4.3.2). Section 1.4.3.3 gives a short overview of literature on state space analysis applied to road safety analysis. Chapter 4 of this report describes how state space model were used to describe and explain the time series of pedestrian-car KSI casualties.

1.4.3.1. Structural time series analysis versus linear regression

The present report contains some traditional types of linear regression analysis in which the number of casualties are the predicted or dependent variable and specific interventions or developments are the predictor or


independent variables. In general, linear regression can be used as a tool to describe relationships between variables and it provides insight into how strongly variables are interrelated. Linear regression analysis is a rather straightforward type of statistical method that though attractive in its simplicity is too simple in certain respects. The development of crashes or crash risk over time is expressed in a time series, i.e. a sequence of observations taken on a variable or a combination of variables at a successive points in time. When linear regression is used to model the development of casualties or risk over time, the regression model makes use of time series data. The theoretical drawback of using linear regression to model time series data is that this technique does not take into account possible time dependencies of data. Thus from a theoretical viewpoint alone, linear regression is not the best tool to represent data that are a times series. A problem with applying linear regression to time series is that often the model residuals are not independently, homogeneously, randomly distributed, which is an important condition for the application of standard statistical tests like the F-test for model significance and the t-test for the significance of the regression weight. Classical linear regression does not take into account measuring errors in the observations. Consequently, these errors may affect the model predictions and the estimation of regression weights. Linear regression analysis can be regarded as a special case of state space analysis. In linear regression without explanatory variables the level (or: intercept) and slope are fixed, while in state space analysis they can vary over time. In an explanatory model, we try to explain as much variation of the time series as possible by way of the explanatory variables. This means that we ultimately aim at a linear model without time dependence. The power of state space analysis is that it explicitly takes into account the time dependencies between observations and therefore the model residuals usually are much closer to independent, homogeneous, random values than in linear regression (Commandeur and Koopman, 2007). As such statistical tests for model significance and for the significance of the regression weights are much more reliable. Furthermore, it can easily deal with missing data and observation errors. Unexplained variability is explicitly modelled by the level and slope components. So, in the case of uncertainty: − in the observations, − in the explanatory variables, or − about the relevance of explanatory variables, which all are valid in road safety, state space analysis must clearly be preferred over linear regression. Under the condition that its restrictions are recognized linear regression can be useful for road safety analysis. Linear regression is a fast method which produces easily understood results: the estimated model coefficients give clear insight in the possible interrelation of variables, whereas the model residuals tell a lot about the shortcomings of the specific regression model. This opens the possibility to efficiently improve the model. If most of the relevant explanatory factors are known and well documented, linear


regression can prove its value. In the case of road safety analysis with its time dependencies, its many uncertainties and its overlapping influence factors, we must be careful when interpreting linear regression results. Because of its properties as described above, in Chapter 5 linear regression is used to carefully explore the road to an explanatory model for pedestrian-car road unsafety.

1.4.3.2. Structural time series analysis versus Box-Jenkins

The best models of time series data are provided by specific time series analyses. The objectives of a time series analyses are: 1. to understand the structure of the time series, i.e. to understand how it

depends on time, itself and other time series variables; 2. to forecast future values of the time series. In the social sciences, the dominant model for time series analysis in the last 20 years has been the so-called ARIMA-model of Box and Jenkins. Box-Jenkins is a procedure which uses a variable's past behaviour to select the best forecasting model from a general class of models. It assumes that any time series pattern can be represented by one of three categories of models. These categories include: − Autoregressive models: forecasts of a variable based on linear function of

its past values; − Moving Average models: forecasts based on linear combination of past

errors; − Autoregressive-Moving Average models: combination of the previous two

categories. Box-Jenkins and state space methods for time series analysis differ in several ways (Commandeur and Koopman, 2007). First, trend and seasonal are explicitly modelled in state space models, whereas they the Box-Jenkins approach treats these components more like a nuisance, to be removed from the series before any analysis can be performed. In other words, state space models provide an explicit structural framework for the decomposition of a time series where trend and seasonal variables are treated as theoretically interesting in their own right. In contrast to ARIMA-models, state space models explicitly decompose a time series in a number of separate, clearly definable components (i.e. trend, seasonal and irregular) and do not make assumptions about the stationarity of the data. Second, the application of Box-Jenkins ARIMA models is based on the assumption that the (differenced) time series is stationary, an assumption which often conflicts with the reality of time series in the economic or social field. In state space methods no stationarity of the series is required. This means that in practice state space models are very flexible. It is relatively easy to introduce changes in the structure of the model over time. In contrast the Box-Jenkins model has the rather stringent assumption that the dynamic properties of the time series are constant over the whole period, an assumption which is often not tenable for road safety time series (Commandeur, 2005).


A third difference pertains to the handling of missing data, stochastic explanatory variables, and multivariate data. While this is relative easily done in state space models, this is difficult in ARIMA modelling. In a nutshell, the great advantage of state space modelling is that it is based on a structural analysis of the time series problem. The different components which together make up the time series such as trend, season, cyclical movements, or calendar effects, together with explanatory variables and interventions can be modelled separately before they are 'tied together' in a state space model where they are simultaneously analysed (Koopman, 2000). In comparison, the much used, traditional Box-Jenkins method is more like 'a black box'. The chosen model depends upon sometimes arbitrary choices concerning the number of unit roots in the autoregressive part of the ARIMA model and the number of maximally relevant lags in the model.

1.4.3.3. Literature on state space analysis applied to road safety analysis

In the late eighties state space techniques started to be used for the analysis of time series in the field of road safety. Harvey and Durbin (1986) used state space time series modelling to assess the effects on casualty rates of the seat belt law introduced in the UK in 1983. Similarly, Bos and Bijleveld (1991) applied state space techniques to analyse the effect of seat belt wearing in the Netherlands. Bijleveld and Commandeur (2004/2005) illustrated the use of state space techniques for the evaluation of developments in Dutch road safety by applying state space analysis to single car accidents in the Netherlands in 1985-2003. They first set up the so-called Latent Risk Model (LRM) for describing car exposure and accident risk and then incorporated two explanatory variables, the proportion of wet weather days and the proportion of drink driving, in the model. Bijleveld and Commandeur (2004) used two different methods for including the explanatory variables in the model resulting in conflicting outcomes. In the first method the explanatory variables are related to total accident risk. With this method the result is found that single car accident risk decreases if the proportion of wet weather days increases, whereas no relation was found between drink driving and risk. In the second method a subdivision was made between car exposure and accident risk during wet weather and exposure and risk during dry weather. Exposure in wet weather was modelled as total exposure times an exposure correction factor and risk in wet weather was likewise modelled as total risk times a risk correction factor. The latter factor represents the effect of the proportion of wet weather days. The other explanatory variable, the proportion of drink driving, was similarly dealt with. With this second method, Bijleveld and Commandeur found that risk in wet weather circumstances is 3.4 times higher than in average circumstances, whereas risk in drink driving conditions is 2.7 times higher. This result is certainly closer to reality than the result found by using the first method.


2. Descriptive analysis of crash data

2.1. Outline

Preferably models of casualty risk should be based on substantive knowledge regarding how casualties and risk have evolved over time. Such knowledge allows us to specify possible parameters and trend breaks in the model. Part of that knowledge can be generated from the research literature; another part can be acquired by taking a closer look at the road safety data itself. The objective of this chapter is to study road safety data for pedestrian-car casualties with simple descriptive techniques in order to form a first image of possible conditions and factors that influence this kind of casualties. This chapter describes the development of pedestrian-car KSI victims over 1976-2004. By way of figures, tables, and simple analysis techniques, trends and trend breaks are identified. To improve our understanding of what factors may have contributed to those trends and trend breaks, the development of pedestrian-car KSI victims under specific conditions and circumstances (time, location, age and gender, etc.) is investigated as well.

Section 2.2 describes the method followed and Section 2.3 the data. Section 2.4 presents the various results. In this section trends and trend breaks in pedestrian-car KSI casualties are looked at from various subdivisions such as location, season, day of week, gender, age etc. time. Finally, Section 2.5 gives an overview of results and concludes with the formulation of hypotheses.

2.2. Method

This section describes the method followed for the descriptive data analysis. First, Section 2.2.1 contains a general description of the method. Then, in Section 2.2.2 the method of analysis of casualties is presented. Section 2.2.3 contains a description of the method of selecting subdivisions. Finally, Section 2.2.4 deals with the selection of exposure measures and the estimation of risk.

2.2.1. General approach

First, we analysed data on pedestrian-car KSI casualties in order to get insight in the trends and trend breaks for different subdivisions (see Sections 2.4.1 and 2.4.2). The most relevant subdivisions, i.e. specific circumstances with significant differences in trends or trend breaks in the subgroups defined by those circumstances, were selected (Section 2.4.2) for further analysis. For each subdivision an appropriate and reliable exposure measure was searched for and if an appropriate exposure measure was found, data on this measure was collected and risk was computed (Section 2.4.3). Otherwise, we skipped this step. Finally, also trends in risk and exposure were analysed. Figure 2.1 depicts the procedure followed and the results from each step.


Analyze crashes and/or casualtiesfor several subdivisions

Determine relevant subdivisions

Analyze exposure and risk

Select exposure measure for eachrelevant subdivision (if possible)

description of trends and trend breaksin crashes and/or casualties

subdivisions with significant differencesIn trends or trend breaks

appropriate exposure measure for eachrelevant subdivision

description of trends and trend breaksin exposure and risk

Figure 2.1. Stepwise procedure for the investigation of specific conditions related to changes in accidents and risk.

2.2.2. Method of analysis of casualties

In order to describe trends and find trend breaks, annual pedestrian-car KSI casualties (Killed or Seriously Injured) in the Netherlands are analysed. We investigated whether there is difference between trends: − in the number of killed and the number of seriously injured pedestrians. − in the number of KSI casualties of closely related collision types, e.g.

bicycle-car. When looking for explanations of trends and trend breaks, it can be useful to know to what extent trends or trend breaks are different for other collision types.

− in the number of pedestrian-car KSI casualties in several subdivision, e.g. inside vs. outside urban area, intersections vs. road sections, subdivision by age (group) etc.

These analyses give insight in the years in which trend breaks appeared and under what circumstances they appeared. For these trend break years and these circumstances we can try and find the underlying influence factors on the basis of literature study (see Chapter 3). An important part of the data analysis is the identification of trend breaks. A trend break is a significant change of level and/or slope of the trend. The method which was applied for the determination of the significance of a level change is described in the next section.

2.2.3. Method of selecting subdivisions

The analysis of casualties can be used to study how trends of casualties over time evolve differently for various subdivisions. It is relevant to make a subdivision if − trends or trend breaks differ significantly between two or more of the main

subgroups of the subdivision;


− there are relevant explanatory variables or influence factors (see Section 3.3) which operate on one subset, i.e. one or more subgroups defined by subdivisions.

Trends differ significantly between subgroups if trends have (1) a different sign, (2) a different degree of decline, (3) are different in shape, or (4) have trend breaks that are different in size or type. Figure 2.2 gives some examples of these four cases.

A trend break has three possible appearances: change of level, change of slope, or change of level and slope. These are depicted in Figure 2.3. A subdivision is defined to be relevant if two or more subgroups which relevantly contribute to the total number of pedestrian-car KSI casualties, i.e. more than 10%, are different with respect to − significant changes in the number of KSI casualties, i.e. in two or more

years there is difference between the sign of 95% significant changes or regarding the presence of a 99% significant difference;

− overall trend of the number of KSI casualties, i.e. there is a difference of more than one percent point in the average annual decline percentage;

− the supposed occurrence or the estimated magnitude of an explanatory variable or influence factor;

− risk development. The next section explains how 95% significant changes in the number of KSI victims between two consecutive years are determined.

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Figure 2.3. Types of trend breaks.

2.2.3.1. Method of determining the significance of level change

In general, the occurrence of road crashes can be modelled as a Poisson process. Under the assumption that the crash (or victims) numbers in two consecutive years are two independent variables with the same Poisson distribution, the standard deviation of the difference between the crash numbers can be estimated as the square root of the sum of the numbers. This is because the variance of the difference between two independent Poisson distributed variables is equal to the sum of the variances of the variables, which is equal to the sum of the means of the variables (because the variance of a Poisson variable is equal to the mean). Then, the standard deviation is equal to the square root of the sum of the means. In general, one can say that the probability that the deviation of a stochastic variable from its mean is larger than two times its standard deviation is small (about 5%). Thus, if the difference between two consecutive observations, which has zero mean under the above assumptions, is larger than two times the standard deviation computed as above described, then it is not likely that the observations are draws from the same probability distribution. Then apparently, between the observations something in the road system (man, vehicle, road/environment) has changed. In that case the change is called significant in this chapter. In other chapters of this report the word 'significant' may be used in another sense. An important assumption is that the observed crash numbers in two consecutive years are independent draws from the same distribution. This means that the possible covariance term of the variance of the difference is neglected. This notion directly clarifies the need for time series analysis:


taking into account the possible covariance between two consecutive observations prevents the potential overestimation or underestimation of the significance of a change in the number of crashes. Probably, the covariance between two consecutive observations is positive. In that case, the standard deviation will be underestimated. Because the significance of a change is estimated by dividing the change itself by the standard deviation, the significance of a change from one year to another will probably be overestimated.

2.2.4. Exposure and risk

As described in Section 1.3.4, in this study road unsafety is expressed as the product of exposure and risk. Exposure is an unobservable (latent) variable for the quantification of which an operational measure is needed. Often exposure is assumed to be equal to mobility. Therefore, mobility measures are often used measures for the exposure of a vehicle type. Possible exposure measures are: − person or driver kilometres: total number of kilometres travelled by

persons or drivers. For the analysis of crash risk driver kilometres is the right exposure measure, for the analysis of casualty risk it is person kilometres.

− vehicle kilometres: total number of kilometres travelled by vehicles, generally subdivided by vehicle type. In practice, vehicle kilometres is equal to driver kilometres. It is the data source which determines whether kilometres are called driver kilometres or vehicle kilometres.

− vehicle fleet: total number of vehicles, by vehicle type. − driving licences: total number of driving licences. − time in traffic: total time in traffic of persons. − number of trips: total number of trips by persons. − road length: total length of the road network. − drivers population: total number of possible drivers (people above 18), by

age (group) and gender. − population: total number of people, by age (group) and gender. − traffic intensity: number of vehicles per time unit. This can be computed

as the average over different counting locations and different time intervals.

Exposure measures can be selected on the basis of the following criteria: − Representativeness: The measure must be representative of the

exposure. A good exposure measure has the same trend as the latent exposure itself. Ideally, the exposure measure is equal to latent exposure without systematic error, but in practice it is equal to latent exposure plus some random error.

− Data availability: quantitative data on the exposure measure must be available.

− Data validity: the data on the exposure measure must be sufficiently reliable.

− Comparability: if several crash types are to be compared, risk based on the exposure measure is preferably comparable to risk of the other crash types.


− Possibility of subdividing: preferably the exposure measure canbe subdivided according to specific characteristics and circumstances, as gender, age, road type, vehicle type, etc.

After the exposure measure has been selected, casualty risk can be estimated as the ratio between the number of casualties and exposure. The next step is to analyse trends and trend breaks in exposure and risk. When no exposure data are available, e.g. for some subdivision, a way to find out whether explanatory variables have affected the number of crashes (or victims) of a risk group (collision type) is to make a comparison with another subdivision of the same risk group or another risk group. Such a subdivision or risk group is called the reference group. If the reference group is chosen such that trends in exposure are the same for the risk group under consideration and the reference group, the ratio between their casualty numbers is proportional to the ratio between their risk levels. This latter ratio is called the comparative risk. Furthermore, if the reference group is chosen such that a specific explanatory variable with a given start in time could not have affected its trend, the observation of a trend break in comparative risk in that given year is a clear indication of an effect of the explanatory variable on the risk group considered. Because we used a well available exposure measure for pedestrian-car KSI casualties and because we restricted ourselves somewhat in our search for explanations, there was no need to employ this concept of comparative risk. However, we think it might well be used in further research on this subject.

2.3. Data

2.3.1. Data on casualties

Data used for this analysis was extracted from the SWOV internet databases. Source of these data is the Transport Research Centre (AVV) of the Ministry of Transport, Public Works and Water Management. Two databases were consulted: − Database based on three discontinued systems (EVIS, TVIS and

'Accidents and Network') for the period 1976-2003; − 'BRON' for 2004. AVV is migrating from the old database to the new one and is converting the old database to the new format. At the moment of this analysis data from 2001 on was available in BRON. For this analysis, the BRON 2004 data was added to the 1976-2003 data from the 'Accidents and Network' database only if the BRON format was the same as the old format or could be converted to the old format. Furthermore, checks were carried out by comparing the 2001-2003 data from both databases. In Section 1.3.2.2, we motivated our choice to investigate the casualty numbers and not the crash numbers. All available data on exposure concern national totals. Generally, exposure of foreigners in the Netherlands is not known very well.


From 1983 the crash category 'straight road (intersection)' was added to the database for crashes on road sections which occurred under the influence of an intersection. This category was included in the aggregation 'Intersection'. Before 1983, these crashes were included in the aggregation 'Road section'. This change caused a downward jump in the road section casualties and an upward jump in the intersection casualties. These jumps can be found back in the description of the subdivided data in Sections 2.4.1.7, 2.4.1.9, and 2.4.1.12.

2.3.1.1. Different pedestrians

Types of pedestrians which are distinguished in the old AVV database for 1976-2003 (see Section 2.3.1) are: − pedestrian; − pedestrian on the road through an other accident; − blind person; − invalid not in vehicle; − child with toy; − pedestrian with object, wheelbarrow; − pedestrian carrying child; − pedestrian with animal; − person, unknown; − ex- or intending driver or passenger; − ex- or intending tram or bus passenger; − person probably fallen or tripped; − traffic regulator; − person carrying out work; − horse rider; − marching column; In the new database (BRON) no subdivision of pedestrian victims is made. Another victim category in BRON, ‘other objects’, contains three of the above types of pedestrian victims (pedestrian with wheelbarrow, horse rider, and marching column) and one new type (pedestrian with bike). The other types that are distinguished in the old database but not in BRON were assumed to be added to the general pedestrian victim category in BRON. For the overlapping years in the databases, i.e. 2001-2004 at the time of analysis, we compared the pedestrian-car KSI victims numbers using the pedestrian category in 'Accidents and Network' and in BRON. This comparison showed that the BRON numbers differ less than 1% from the old database's numbers. On the basis of this result we decided to compute the 2004 victim number by just using the 'pedestrian' category in BRON and add it to our series over 1976-2003.

2.3.2. Possible measures of pedestrian exposure

2.3.2.1. Pedestrian kilometres 1985-2004

Pedestrian kilometres is a commonly used exposure measure, so as to compute risk as the number of victims per km. Pedestrian kilometres are available from the National Travel Survey (CBS) for 1985-2004. Before 1994


the 0-11 aged children were not included in the National Travel Survey. From the SWOV internet database, which contains results of this survey, it is easy to make subdivisions with regard to gender, age, motive, province, and day of week. Since 1999 baby carriages and skaters or skeelers are distinguished as separate pedestrian types. Figure 2.4 presents the change in annual pedestrian kilometres in the period 1985-2003. The 12+ pedestrian kilometres steadily increase from 2,562 million kilometres in 1986 to 3,000 million kilometres in 1994. Then, there is a steady decrease to 2,699 million kilometres in 2000, followed by increase in 2001, decrease in 2002 and 2003, and finally increase to 2,812 million kilometres in 2004. The total pedestrian kilometres, including the 0-11 aged, show the same development on a higher level: the 2004 number of total pedestrian kilometres is 3,283 million. As we can see in Figure 2.4 the pedestrian kilometres show the same overall increasing trend as the population figures. Whereas the development of population over time is represented by a more or less straight line, pedestrian kilometres show a wave-like development.

0

0,5

1

1,5

2

2,5

3

3,5

4

1985 1990 1995 2000

pede

stria

n ki

lom

etre

s (1

000

mill

ion)

0

2

4

6

8

10

12

14

16

18

20

popu

latio

n (m

illio

n)

Total kms Kms > 11 Total population Population > 11

Figure 2.4. Pedestrian kilometres and population 1985-2004. Source: CBS

The use of pedestrian kilometres, possibly combined with car kilometres, as exposure measure for pedestrian-car casualties has some drawbacks. First, 0-11 aged children are not included in the figures until 1994. Secondly, the estimated error in the surveyed and adjusted pedestrian kilometres is relatively large. From the survey it seems that many of the respondents live on railway stations, meaning that part of them fails to report short walking distances as from home to the railway station. Furthermore, the change of the survey design in 1999 has introduced some error in the pedestrian kilometres by 0-11 aged, as clearly can be seen in Figure 2.4. Thirdly, more and more pedestrian kilometres are travelled on locations, like shopping areas, where no other traffic is allowed. If we use pedestrian kilometres as exposure measure, we must be aware of the fact that the effect of this development on pedestrian-car KSI casualties is included into the development of pedestrian-car KSI risk.Number of trips 1985-2004


Before discussing the number of trips as a possible measure of pedestrian-car exposure, it is important to define 'trip'. A 'trip' is defined as a movement of a person on the road from a specified point of origin to a specified point of destination which is made starting from one motive. A trip can be subdivided into movements of different transport modes, which are called 'trip sections' or 'trip stages'.

Home Officewalking tram train bus walking

Figure 2.5. Example of two (single) trips subdivided into trip sections or stages.

For example, consider the journey of Mr. X from home to his office as depicted by Figure 2.5: first Mr. X walks to the tram stop, then he takes the tram to the train station, where he takes the train to another train station. At the train station he takes the bus and finally he walks from the bus stop to the office. At the end of the day he travels back home in the same way. The movement from home to office is called a 'single trip' or just a 'trip' and from home to office and back a 'return trip'. The movement from home to tram stop, from tram stop to train station, etc. are called 'trip sections' or 'trip stages'.

Home Officewalking tram train bus walking

Super-market

walking

buswalking walking

tramtrain

walking

Figure 2.6. Example of three trips subdivided into trip sections.

The inclusion of 'one motive' in the definition of trip is important. Consider the example in Figure 2.6, in which Mr. X on his journey back home walks to the supermarket after taking the bus to the train station and then back to the trains station from where he takes the train home. In this case we consider Mr. X's movement from the office to the supermarket as one trip and his transfer from the supermarket to his house as another. As in the example of journey of Mr. X, most of the trips include some walking, e.g. from parking place to shops or office, from home to bus station, etc. Therefore, the total number of trips may be a good measure for exposure of pedestrians to pedestrian-car crash risk. Number of trips is available from the National Travel Survey (CBS) for 1985-2004. Before 1994 the 0-11 aged children were not included in the National Travel Survey. Number of trips is not available from the SWOV internet database, but can be derived from the less easily accessible SAS database at SWOV. From this database also subdivisions according to gender, age, etc. are available. Furthermore, it is possible to leave out types of trips which probably do not lead to exposure of pedestrians to collisions with cars, e.g. bicycle trips. Because the number of trips was not so easily distracted from the SAS database, in Figure 2.7 we show the total number of trip sections per year as


an indicative variable of the total number of trips. The number of trip sections increased from 1985 to 1990 and then more or less stayed on the same level until 1993. In 1994 there was a break, because the 0-11 aged were included in the survey. From 1994 to 1993 there was a wave-like development of repeated succession of slight decrease and slight increase.

12

13

14

15

16

17

18

19

20

1985 1990 1995 2000 2005

num

ber o

f trip

s (x

1000

milli

on)

Figure 2.7. Total number of trip sections 1985-2003. Source: National Travel Survey (CBS).

Advantage of the use of number of trips instead of pedestrian kilometres is that underreporting of short walking distances is no problem, because in fact we assume that each trip includes some walking. Also the development of more and more walking in areas where no other traffic is allowed is no problem here. However, there are also some drawbacks to the use of number of trips as exposure measure. As in pedestrian kilometres also in number of trips until 1994 the 0-11 aged children are not included. The main drawback is that walking distances can strongly differ between trips, while walking distance is an important factor influencing the probability of pedestrian-car accidents. So, if we use number of trips as exposure measure and the average walking distance per trip has changed, for example because more and more parking places have been built close to shopping and entertainment centres, then we must be aware of the fact that the effect of this change on the pedestrian-car KSI casualties is incorporated in the development of pedestrian-car KSI risk.

2.3.2.2. Population

Every inhabitant has, dependent of age, gender, type of job, hobbies etc., a natural need for walking. Therefore, the number of inhabitants (or: population) by age, gender etc. can be an appropriate exposure measure. Figure 2.8 shows the development of different age groups of population in the period 1980-2004. As can be seen in Figure 2.8 the number of 0-11 aged inhabitants steadily decreases until the late eighties and then increase


again, whereas the number of inhabitants of age 12-24 smoothly declines and the number of 40+ aged inhabitants increases.

0

500.000

1.000.000

1.500.000

2.000.000

2.500.000

3.000.000

3.500.000

4.000.000

4.500.000

5.000.000

1980 1985 1990 1995 2000

popu

latio

n

0 - 11 12 - 24 25-39 40-59 60+

Figure 2.8. Population by age. Source: CBS.

Advantage of using population as exposure measure is that population figures are very accurate. Furthermore, the time series of population figures is very long, which opens the possibility to enlarge the time horizon to 29 years (instead of 20 when using kilometres or number of trips). Another possible advantage is that pedestrian-car KSI risk on the basis of population as exposure measure makes it comparable to all kinds of health risks. Drawback is that developments as described above, as the shortening of walking distances and increasing walking kilometres in areas with no other traffic, are incorporated in risk development.

2.3.2.3. Pedestrian kilometres per inhabitant by gender and age

To get more insight in developments with respect to walking and their possible consequences for the selection of the exposure measure, in this section we describe the development of pedestrian kilometres per inhabitant by gender and age. Figure 2.9 shows the annual pedestrian kilometres per 12+ aged female, 12+ aged male, and 0-11 aged child. The overall trend over 1985-2004 is declining for these groups. As Table 2.1 shows, the male annual pedestrian kilometres per head are declining faster than the annual female pedestrian kilometres per head.


0

50

100

150

200

250

300

1985 1990 1995 2000 2005

pede

stria

n km

s / i

nhab

itant

Female 12+ Male 12+ 0 - 11

Figure 2.9. Pedestrian kilometres per inhabitant.

The annual pedestrian kilometres per 0-11 aged child are depicted separately in Figure 2.9, because they are surveyed from 1994 only. Remarkable is the sharp increase from 1998 to 1999, which we suspect to be an effect of the change of the survey design in 1999. Therefore, to compute the annual change, we split up the period 1994-2004 into the period before the suspect increase and the period after. The resulting estimated annual change of the number of pedestrian kilometres per 0-11 aged child is relatively large: -3.9 %. This would mean that 0-11 aged children have been walking less and less the past ten years.

Average annual change

1985-2004 1994-1998 1999-2004

Total 12+ -0,38%

Female 12+ -0,15%

Male 12+ -0,63%

0 - 11 -3,93% -3,90%

12 - 17 -1,40%

18 - 24 -0,66%

25 - 29 -1,76%

30 - 39 -0,88%

40 - 49 +0,71%

50 - 59 +0,04%

60 - 74 +0,35%

75+ -0,32%

Table 2.1. Average annual change of pedestrian kilometres, total 12+, by gender, and by age group.

Also for the age groups 12-17, 18-24, 25-29, and 30-39, the annual pedestrian kilometres per head have decreases over 1976-2004. For the


age groups 40-49, 50-59, and 60-74 there was slight increase and for the 75+ aged slight decrease. If we choose population number as exposure measure for pedestrian-car casualty risk, it is important to know about these changes in the annual number of pedestrian kilometres per head, because they might be incorporated into risk development. For example, a steady decline of risk computed on the basis of population numbers could be explained by the fact that people walk less and less often.

2.3.3. Possible measures of car exposure

Available data were car vehicle (or: driver) kilometres and car passenger kilometres for 1985-2003 from the National Travel Survey (CBS). From the SWOV internet database, which contains results of the survey, it is easy to make subdivisions with regard to gender, age, motive, province, and day of week. Road Statistics (CBS) provides the car vehicle kilometres for 1950-2000 with subdivisions regarding fuel type, motive, new or second-hand, and owner (person, firm).

0

20

40

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1985 1990 1995 2000

kilo

met

ers

(100

0 m

illio

n)

0

1.000

2.000

3.000

4.000

5.000

6.000

7.000

8.000

9.000

10.000

fleet

(x10

00)

Car person km Car vehicle km Car fleet

Figure 2.10. Car person kilometres, car vehicle kilometres, and car fleet 1985-2003. Source: CBS.

Figure 2.10 presents the development of the car vehicle kilometres and the car person kilometres from the National Travel Survey in the period 1985-2003. Noteworthy is the decline of car kilometres in 1991 (which might be an effect of the introduction of the public transport pass for students), followed by an increase in 1992, a decrease in 1993, and a strong increase in 1994. Figure 2.11 presents vehicle kilometres in the period 19883-1996 per road type. In the period 1983-1996 the increase of car vehicle kilometres mainly took place on the motorways. Unfortunately CBS does not supply these data for the period after 1996. It can be assumed that car kilometres have principally increased on the motorways after 1996 as well.


0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Highways (rural) Other national roads (rural) Provincial roads (rural)Other roads (rural) Urban roads

Figure 2.11. Car vehicle kilometres by road type. Source: Road Statistics (CBS).

Car fleet numbers per region are obtainable from the Motor Vehicle Statistics (CBS). As can be seen in Figure 2.10, car fleet is gradually increasing. The mean annual growth of car fleet is 2%.

2.3.4. Selection of exposure measure

In this study, exposure is exposure to pedestrian-car KSI risk. Our aim is to find a quantitative measure or proxy variable for this exposure. This proxy variable should represent the extent to which pedestrians and cars are present on the same roads and in the same time periods such that collisions between pedestrians and cars may occur. To improve understanding of what we are looking for, we will first give some examples of changes which ideally do or do not affect pedestrian-car exposure: − More car presence on motorways does not affect pedestrian-car

exposure, because there are (intended to be) no pedestrians on the motorway.

− More pedestrian presence in pedestrian areas does not affect pedestrian-car exposure, because cars are not allowed in pedestrian areas.

− More pedestrian presence on the pavement does not necessarily affect pedestrian-car exposure, because cars are not allowed to drive on the pavement.

− More road crossings by pedestrians enlarges pedestrian-car exposure. − Modification of crosswalks at grade into grade separated crossings

decreases pedestrian-car exposure. − Construction of zebra crossings, possibly with traffic signal, does not

necessarily affect pedestrian-car exposure. However, it can be expected to result into lower pedestrian-car risk.

− If larger and larger parking places are built closer and closer to the shopping areas, this affects pedestrian-car exposure.

− If more and more cars are provided with a pedestrian-friendly front, this does not affect pedestrian-car exposure (or it should be that pedestrians


feel safer and therefore more often cross the road). However, it does affect pedestrian-car KSI risk.

− Under the condition that walking behaviour of people stays unchanged, the growth of population and change of the age composition affects pedestrian-car exposure.

From these examples, we find that car kilometres cannot be included in our proxy variable for pedestrian-car exposure. This is because total car kilometres include the (many) car kilometres on the motorways, whereas car kilometres inside urban area are not reliably available. Car fleet is neither usable, because it is not necessarily proportional to car presence or car use in urban areas. Our proxy variable should neither include pedestrian kilometres, because a substantial part of these kilometres is travelled in pedestrian areas or on the pavement, where no cars are allowed. Number of trips is neither an ideal proxy variable, because pedestrian-car exposure may change at constant number of trips, for example if more parking places are built closer to the shopping areas and offices. This leaves us the population, which is neither an ideal proxy variable, because it is affected nor by changes in walking behaviour, nor by specific infrastructural measures that should affect pedestrian-car exposure, e.g. the modification of crosswalks at grade into grade separated crossings. On the basis of the above considerations, we concluded that all variables at hand are not the appropriate, ideal proxy variables for pedestrian-car exposure. To finally select the most appropriate proxy variable, we made up a balance thereby using the criteria which were presented in Section 2.2.4: − extent to which the presence of pedestrians and cars in urban areas is

represented; − availability; − accuracy: random and systematic error, e.g. because of the strong

increase of car kilometres on motorways, car kilometres increasingly and systematically overestimates car presence in urban areas;

− comparability to other collision types, where the product of vehicle type kilometres is used;

− possibility to distinguish subdivisions, e.g. by region, inside/outside urban area, season, day of week, hour of day, age, gender, etc.

We scored the possible proxy variables on the above criteria, where we translated the importance of the criteria in the maximum score: 5 for representation of presence and availability, 4 for accuracy, 3 for comparability, and 2 for possibility of subdividing. Table 2.2 contains the results of this evaluation. On the basis of the evaluation of the criteria in Table 2.2, we chose the population numbers as the best possible proxy variable for pedestrian-car exposure. Assuming a natural need of people to walk and to use cars, population tells a lot about the presence of pedestrians and cars in urban areas. Furthermore, population numbers are very accurate and the population time series reaches to far back in the past.


Proxy variable Presence pedestrians and cars in urban area

Avail-ability 1976-2004

Acc-uracy

Compar-ability

Subdiv-isions

Total

maximum score: 5 5 4 3 2

Population size ++++ +++++ ++++ + 14+

Pedestrian kms ++ +++ ++ ++ 9+

Car fleet + +++ +++ 7+

Car kms + +++ +++ ++ 9+

Urban car kms ++ ++ + 5+

Ped.kms x car kms +++ +++ ++ +++ ++ 13+

Ped.kms x urban car kms ++++ ++ + +++ 10+

No.of trips ++++ +++ +++ ++ 12+

No.of trips x urban car kms

+++++ ++ + 8+

Table 2.2. Evaluation of proxy variables for pedestrian-car exposure.

This choice of exposure measure has several drawbacks. Firstly, some infrastructural measures and developments with respect to walking behaviour that should affect pedestrian-car exposure do not influence population numbers. However, it can be argued that the chance that infrastructural measures are taken is larger if population is larger, so that there is some proportionality between our proxy variable and those measures. Secondly, population size can be subdivided according to gender, age, and region. However, the subdivisions of population size by season, day of week, hour of day, and also by inside/outside urban area do not well represent the possible influence of these subdivision criteria on pedestrian-car exposure. Finally, this choice does no good to the comparability to other collision types, for which the product of vehicle kilometres was selected as proxy variable. However, we think the disadvantages of using the pedestrian and car kilometres are too great and we take the lower comparability for granted. To conclude this section, we recommend to find a method to reliably determine the car kilometres inside urban area, which would make the use of the product of pedestrian and car kilometres (inside urban area) as a proxy variable for pedestrian-car exposure much more attractive.

2.4. Results

This section presents the results of the descriptive analysis. First, Section 2.4.1 describes the results of the analysis of the general pedestrian-car KSI casualty data. Section 2.4.2 presents the conclusions about the relevant subdivisions. Finally, in Section 2.4.3 exposure and risk are analysed for those subdivisions.


2.4.1. General

2.4.1.1. Trend over time

For most of the following analyses, the deaths and in-patients have been added together to form what we will refer to as KSI casualties. We will practically no longer refer to deaths and in-patients separately. The numerical relation between these two groups is given in Table 2.3 so as to check whether the deaths increase or decrease differently from the in-patients. We have divided the 1976-2004 period into three periods: 1976-1985, 1986-1995, and 1996-2004. If the number of deaths decreases considerably faster or slower than the number of in-patients in a particular period, we better not add them together in the analysis.

General developments (1976-1985 = 100)

Period Dead Hospital Slightly injured KSI

1976-1985 100 100 100 100

1986-1995 51 56 76 56

1996-2004 28 32 52 32

Table 2.3. Relations in the index numbers between the various casualty groups during three periods.

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1975 1980 1985 1990 1995 2000 2005

Vict

ims

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6

7

8

Cha

nge

as n

umbe

r of s

tand

ard

devi

atio

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change (in # st.dev.) 95% significance level KSI

Figure 2.12. The number of pedestrian-car KSI casualties in 1976-2003.

As can be seen in Table 2.3, the ratios are sufficiently similar to add together. By doing this we can show the development of KSI casualties (from now on to be referred to as KSIs) in Figure 2.12. In fact, five trends can be distinguished in Figure 2.12. In 1976-1978 the KSI number increased slightly, after which it decreased again slightly. In 1978-1981 there was a significant decrease (about five times the standard deviation), after which the decrease stabilized, and the KSI number increased again. In 1982-85 the KSI number decreased relatively fast. In


1985-2003 there were another four significant changes, namely: 1988-1989, 1990-1991, 1996-1997, and 2003-2004. The KSI number decreased to 473 in 2003. After this, there was a decrease of three times the standard deviation to 283 in 2004. The most remarkable years were 78-79 (strong decrease) and 2003-2004 (strong decrease).

2.4.1.2. Other crash opponents (pedestrian-moped, pedestrian-bicycle)

In order to compare the KSIs of the pedestrian-car crashes with those of other groups, the bicycle and moped as crash opponent are also shown in Figure 2.13. The number of pedestrian-bicycle KSIs decreased during the whole period by 3.6%. The pedestrian-moped KSIs had an overall decrease of 6.1%. They decreased strongly in 1977-1992, then continued to fluctuate, and then, on average, even to slightly increase in 1992-2002, after which there was a strong decrease in 2003 and 2004. The pedestrian-car KSIs, with an overall decline of 6.0%, decreased the most regularly of the three conflict groups, except in 1978-1979. The pedestrian-moped group also had its most significant decrease then, whereas this was 1983-1984 for the pedestrian-car KSIs. Finally, a striking detail is that there was a considerable decrease from 2003 to 2004 for both the pedestrian-car and pedestrian-moped, whereas there was an increase for the pedestrian-bicycle group.

0

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1975 1980 1985 1990 1995 2000 2005

vict

ims

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ped

estr

ian

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ge a

s nu

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Bicycle (change) Moped (change) Car (change) Bicycle Moped Car 95% significance level

Figure 2.13. KSI casualties among pedestrians with various crash opponents, and the differences.

The columns in Figure 2.13 give an indication of the significance of the differences. Very clear are the differences between 1979 and 1978, and 2004 and 2003. There were also significant differences for pedestrian-car KSIs in 1981-1982, 1983-1984, 1984-1985, 1986-1987, 1988-1989, 1990-1991, and 1996-1997. In all these years, the decrease in pedestrian KSIs was more than two times the standard deviation. For the other conflict groups, only the pedestrian-moped KSIs had significant differences between: 1984 and 1985, 1991 and 1992, 1999 and 2000, 2002 and 2003, 2003 and 2004.


For the comparison of the developments of crash types it is important to note that the registration of pedestrian-bicycle and pedestrian-moped crashes is less than the registration of pedestrian-car crashes. To give a rough estimate of the differences: the average monthly registration percentage of seriously injured road victims over 1984-2004 is 40% for bicyclists, 70% for mopedists, and 82% for car drivers or passengers.

2.4.1.3. The car as crash opponent, various casualties

In the previous section we compared pedestrian-car KSIs to the KSIs of two other crash types with pedestrian casualties. In this section, we compare pedestrian-car KSIs to the KSIs of two crash types with car as crash opponent. Figure 2.14 shows the KSIs of pedestrian-car, bicycle-car, and car-car. It is noteworthy that the number of pedestrian-car KSIs is by far the most constant. The car-car KSIs are by far the most irregular; in a number of years its number increased considerably, even during a number of consecutive years in the 1991-1998 period. What is also striking is that in 1978-1979 all four considered KSIs with car as crash opponent showed a considerable decrease. During 1990-1991, the other groups decreased considerably, the pedestrian-car less.

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1975 1980 1985 1990 1995 2000 2005

vict

ims

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with

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as

oppo

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chan

ge a

s nu

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r of s

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devi

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nsPedestrian (change) Bicycle (change) Car (change) Pedestrian Bicycle Car 95% significance level

Figure 2.14. KSI casualties with car as collision opponent.

Car-car had the largest number of years with significant difference with the previous year (1976, 1997, 1981-1984, 1986-1987, 1991, 1993, 1998, and 2003-2004). For bicycle-car this number of years is less (1979-1980, 1984-1985, 1987, 1989-1991, 1995, 1998, 2001, 2004) and for pedestrian-car the least (1979, 1982, 1984-1985, 1987, 1989, 1991, 1997, 2004).

2.4.1.4. Severity of injury

The general 1976-2004 development of pedestrian-car KSIs is a considerable decrease of the registered number of deaths (or: killed), in-patients (or: seriously injured), and slightly injured. See Figure 2.15, which shows the numbers of deaths on the right y-axis and of in-patients and slightly injured on the left y-axis during the 1976-2004 period.


Note that the KSIs of different injury severity are not completely comparable, because the registration level is much lower for the slightly injured (about 15% in general; SWOV, 2003) than for the in-patients (about 60%) and deaths (about 92%). Furthermore, we cannot make any statements about the slightly injured in 2004 because the data is missing.

0

500

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1.500

2.000

2.500

1975 1980 1985 1990 1995 2000 2005

vict

ims,

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ious

ly in

jure

d / s

light

ly in

jure

d

0

100

200

300

400

500

600

vict

ims,

kill

ed

Seriously Injured Slightly Injured Killed

Figure 2.15. Numbers of pedestrian-car casualties by injury severity.

Figure 2.15 clearly shows that the number of in-patients steadily decreased during this period, with the extremes in 1978-1979. There have been four increases: 1976-1977, 1980-1981, 1985-1986, and 1995-1996. The development of the slightly injured is more irregular, with several years of increase. From 1980 onwards, for a period of four years, they decreased considerably, after which they stayed more or less on the same level. This lasted until 1989 after which there was a period of overall decrease with some years of increase. In 1997-2003 the numbers of the slightly injured steadily decreased. The number of deaths was also less regular in the first period than in the last 15 years, but decreased considerably as the years went by. Extremes were 1979-1980 and 1985-1986 in which there was a considerable increase. We should mention here that those increases seem to be the result of continuing the old trend after a single low extreme in the previous year. Apart from the numbers shown in Figure 2.15, it was also useful to examine the deaths/1000 in-patients ratio and the in-patients/slightly injured ratio. This is done in Figure 2.16. We chose to divide the number of in-patients by 1000 because e.g. 55 deaths per 1000 in-patients is easier to interpret than 0.055 deaths per in-patient.


0

30

60

90

120

150

1975 1980 1985 1990 1995 2000 2005

ratio

kill

ed :

1000

ser

ious

ly in

jure

d

0,00

0,30

0,60

0,90

1,20

1,50

ratio

ser

ious

ly in

jure

d : s

light

ly in

jure

d

ratio killed : seriously injured (x 1000) ratio seriously injured : slightly injured

Figure 2.16. Ratios deaths/in-patients & in-patients/slightly injured.

Figure 2.16 shows that the ratio deaths/1000 in-patients fluctuated between 90 and 150. The ratio in-patients/slightly injured gradually decline from 0.90 downto 0.50. The former ratio fluctuates much more, because the number of deaths is much lower than the number of in-patients, which results in a variation that is relatively high compared to the mean value. The overall trend of this ratio has a decrease of not even 0.7% a year, whereas the ratio of in-patients/slightly injured decreased by more than 2.3% a year. What is further striking is that, for example, in 1982-1983 and 1992-1993 both ratios increased, implying that the injury severity increased in those years, whereas it decreased in, for example, 1978-1979 and 1986-1987.

2.4.1.5. Urban and rural

It is not surprising that there are more pedestrian KSIs on urban roads than on rural ones. After all, most pedestrian walks are urban. As Figure 2.17 shows, by far most pedestrian KSIs are on urban roads. We then examined the whole 1976-2003 period (for the year 2004 the database made no distinction between urban and rural crashes) to discover any significant differences in the urban and rural pedestrian-car KSIs. The figure below, besides the number of KSIs, also shows the number of standard deviations which a year differs from the previous year.


0

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1975 1980 1985 1990 1995 2000 2005

vict

ims

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7

8

chan

ge a

s no

. of s

tand

ard

devi

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ns

Urban area, change Rural area, change Urban area Rural area 95% significance level

Figure 2.17. KSI casualties urban/rural.

Figure 2.17 clearly shows that the number of urban KSIs (1800 in 1976 and 400 in 2003) has decreased relatively as much as the rural KSIs (400 in 1976 and 100 in 2003). The rural pedestrian-car KSIs changed significantly, i.e. standard deviation is greater than 2, in the years: 1978-1979, 1986-1987, 1987-1988, 1991-1992, and 1992-1993. Increases occurred in 1987-1988 and 1991-1992, and in the other years there were decreases. The years with significant changes of the urban pedestrian-car KSIs are: 1978-1979, 1981-1982, 1983-1984, 1984-1985, 1991-1992, and 1996-1997. These significant changes were all decreases. We can conclude that the number of urban pedestrian-car KSIs is many times larger than the rural ones. For the periods 1976-1985, 1986-1995, and 1996-2004, the distribution is about the same (85%/15%). It is further striking that on rural roads, there was a significant increase twice, in 1987-1988 and 1991-1992. All significant changes on urban roads were decreases. On those roads, there was only a considerable increase in 1985-1986, but this was only because there had previously been two consecutive significant decreases, and furthermore the increase was not considered significant.

2.4.1.6. Speed limit

In the two databases (see Section 2.3.1) there is only one small speed limit difference when the 2001-2003 data is compared. We decided to divide the various speed limits into four categories: 30, 50, 80, and 100+120 km/h. Most of the urban roads have a 50 km/h speed limit and most of the rural roads are 80 km/h. By far the most KSIs occurred on 50 and 30 km/h roads, and only a few on the 80 and 100+120 km/h roads. To make this clearer, Figure 2.18 has been added to this analysis.


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1975 1980 1985 1990 1995 2000 2005

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ns

50 km/h (change) 80 km/h (change) 30 km/h50 km/h 80 km/h 30 km/h 95% significance level

Figure 2.18. KSI casualties by speed limit.

Figure 2.18 shows that the number of KSIs on 30 km/h roads has increased and that those on 50 km/h roads have decreased. In addition, there were several sharp decreases in the 50 km/h road KSIs: 1978-1979, 1981-1982, 1983-1984, 1984-1985, 1988-1989, 1991-1992, 1996-1997, and 2003-2004. The numbers of KSIs on 80 km/h roads had significant decreases in 1978-1979, 1986-1987, 1987-1988, 1998-1999, and 2003-2004. The number of KSIs on 30 km/h roads increased significantly in 2000-2001. The increase of the number of pedestrian-car KSIs on 30 km/h roads is possibly a result of the road network layout according to the Sustainably Safe principle. In this, several 50 km/h roads in residential areas have been converted into 30 km/h roads. See Section 3.3.5.6 for more information about the construction of 30 km/h roads.

2.4.1.7. Road sections and intersections

The number of KSIs on road sections is many times greater than on intersections. You can see this clearly in Figure 2.19. It is, of course, interesting to know what the urban/rural distribution of them was. This we will show you further on. Two things are striking in Figure 2.19. The KSIs on both road sections and intersections decreased very sharply in 1978-1979, but in 1982-1983 the KSIs at road sections again decreased very sharply, whereas the number on intersections increased a lot. These 1982-1983 jumps were caused by an administrative shift of part of the road section crashes to the intersection crashes (see Section 2.3.1). After these relatively large differences, the number of KSIs steadily decreased on both road sections and intersections.


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Intersection, change Road section, change IntersectionRoad section 95% significance level

Figure 2.19. Pedestrian-car KSI casualties by road section / intersection.

2.4.1.8. Road sections and injury severity

It is interesting to know which injury severity road section KSIs have. For example, it could show that pedestrians at road sections are less often severely injured in crashes with cars than on intersections, or the other way round. That is why we included this subdivision in the analysis. In a later section of this chapter we will examine crashes on intersections. In Figure 2.20 you can see the ratios between the various injury severities of casualties at road sections. What is striking in Figure 2.20 is the fact that the deaths/in-patients ratio is more irregular than the in-patients/slightly injured ratio. This conforms expectations because the standard deviation of the number of deaths (√N) is relatively larger than that of the number of in-patients. The years 1979, 1985, 1990, and 1996 have a much lower share of the number of deaths than the surrounding years. In 1990 there were only 55 deaths per 1000 in-patients. In 1980 and 1998 the numbers of deaths per 1000 in-patients were the greatest, about 155 and 160 deaths respectively. During the last few years, the death/in-patient ratio decreased considerably to 75 deaths per 1000 in-patients in 2003. The in-patient/slightly injured ratio decreased steadily, and had few extreme years.


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ly in

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ratio dead : hospital (x 1000) ratio hospital : slightly injured

Figure 2.20. Road sections and injury severity.

2.4.1.9. Road sections and urban / rural

We expect that the number of pedestrian-car KSIs on urban road sections is larger than on rural road sections. In the 1976-2004 period 92% of the KSIs was on urban roads and 8% on rural roads. There was little difference between the portions within this period.

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Figure 2.21. Pedestrian-car KSI victims on urban and rural road sections. In the 1976-2003 period, about 82% of pedestrian-car KSIs were on urban road sections and 18% on rural ones. There has been a development of a slight increase in rural ones. The number of KSIs on urban road sections decreased steadily, with a very sharp decrease in 1981-1984. In 1982-1983 the decrease was, at least partly, caused by an administrative shift to intersection crashes (see Section 2.3.1). After these years of sharp decrease, the number of KSIs has slowly but surely decreased to 262 in


2003. The number of KSIs on rural road sections has also decreased, with two significant ones in 1978-1979 and 1992-1993, and a significant increase in 1991-1992 (see Figure 2.21).

2.4.1.10. Road sections and crash location

Finally, with regard to road sections, we examined the crash location. 35% of the KSIs were at crosswalks, but no data about the locations is available. That is why we can make only few reliable statements about them. What is striking is the fact that the number of KSIs hit at crosswalks decreased at an average of 4.9% a year, whereas the number at other locations has remained more or less the same (a decrease of 0.09% a year).

2.4.1.11. Intersections and injury severity

In this and the two following paragraphs, we have examined more closely the KSIs on intersections and subdivided them the same way as for road sections.

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dratio dead : hospital (x 1000) ratio hospital : slightly injured

Figure 2.22. Intersections and injury severity.

As was so for road sections, the pattern of the death/in-patient ration is quite irregular (see Figure 2.22). The least deaths per in-patient were in 1979, 1987, and 1994. The intersection in-patient/slightly injured ratio has shifted in favour of the slightly injured. However, during the most recent period (1997-2003) there have been more in-patients per slightly injured, to 0.58 in 2003.

2.4.1.12. Intersections and urban / rural

Just as is the case for the KSIs on road sections, one can expect that the number of pedestrian-car KSIs on urban intersections is larger than on rural intersections. In the 1976-2004 period 82% of the KSIs was on urban roads and 18% on rural roads. There was little difference between the portions within this period.


Figure 2.23 shows that during the 1976-1986 period, there was quite an irregular pattern of KSIs on urban and rural intersections. From 1986 on, the decrease was linear, and from 1992 it was quite irregular again, but decreasing less quickly than during the first period. Striking years were 1978-1979 and 1984-1985 (sharp declines) and 1982-1983 (very sharp increase). The latter increase was, at least partly, caused by an administrative shift from road section to intersection crashes (see Section 2.3.1). There is little to say about the rural pedestrian-car KSIs in the 1976-2003 period. It decreased to 7 in 2003.

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urban (change) rural (change) urban rural 95% significance level

Figure 2.23. Pedestrian-car KSI victims on urban and rural intersections.

2.4.1.13. Intersections and crash location

We have also subdivided intersection KSIs by crash location. Only 8% were on crosswalks. Because no data is known about 78% of the KSIs, it is not possible to make reliable statements. What is striking is a sharp decrease in crosswalk KSIs in 1982-1983. There was then more than a halving (more than 4 times the standard deviation).

2.4.1.14. Month and season

We first examined the pedestrian-car KSI distribution over the twelve months of the year, and then allotted them to a season. The winter months are January, February, and March; spring is April, May, and June; summer is July, August, and September; and autumn is October, November, and December. The largest group of KSIs during 1976-2004 was in autumn (28%) and the smallest group was in summer (20%). 27% was in winter and 25% in spring. When we examined the months individually, we found that July has the least and December the most. Figure 2.24 shows that there are a number of striking differences in winter, i.e. a sharp decrease in 1978-1979, and 1984-1985, and a sharp increase in 1979-1980; the latter one has to do with the sharp decrease in 1978-1979. Spring had two slightly smaller decreases in 1979-1980 and 2003-2004. Autumn had quite a considerable decrease in 1986-1987. The summer KSIs decreased steadily from 447 in 1976 to 55 in 2004.


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1975 1980 1985 1990 1995 2000 2005

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Winter (change) Spring (change) Summer (change)Autumn (change) Winter SpringSummer Autumn 95% significance level

Figure 2.24. KSI casualties by month and season.

2.4.1.15. Day of the week

Figure 2.25 shows that the most KSIs are on Saturday and the least on Monday. During the week, the numbers rise slowly after Monday until they reach their peak on Saturday, after which they decrease again. As a result of the distribution in Figure 2.25, which shows that the number of KSIs was larger at weekends than on working days, we made two subtotals: Monday-Thursday and Friday-Sunday. The results are in Figure 2.26 below. For Monday-Thursday there were significant differences in 1978-1979, 1983-1984, 1988-1989, and 2003-2004 (all significant decreases), and for Friday-Sunday these were 1978-1979, 1981-1982, 1984-1985, and 1986-1987 (also all significant decreases). The most striking decrease was in 1983-1984 for Monday-Thursday and 1978-1979 for Friday-Sunday. During the whole period, the numbers of these two groups have grown ever closer.


02468

101214161820

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ay% C

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SI 1

976-

2004

Figure 2.25. KSI casualties by day of the week.

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nsMon-Thu (change) Fri-Sun (change) Mon-Thu Fri-Sun 95% significance level

Figure 2.26. Pedestrian-car KSI victims, Monday-Thursday and Friday-Saturday.

2.4.1.16. Hour of the day

We first examined all 24 hours of the day. Then we subdivided them into night time, morning rush hour, between morning rush hour and afternoon, afternoon, evening rush hour, and evening. In Figure 2.27 we see very clearly that by far the most KSIs are in the afternoon. The least number are at night, and the number increases during the day (with the exception of 8:00-9:00 in which there is a relatively large KSI number) to a maximum during 17:00 and 18:00. Pedestrians, therefore, have a greater chance of being injured severely or being killed by a car during the evening rush hour and the period around it.


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Figure 2.27. Pedestrian-car KSI casualties by hour of the day.

We then examined the data in hour groups as given above. We must mention here that there are a number of 4-hour periods, but also periods of more than 4 hours (e.g. the period known as night time from 22.00 to 07.00). The number of KSIs for this period is indeed larger than, for example, from 7:00 to 9:00, but if you look at the contribution per hour, that of 7:00-9:00 is greater. As can be seen in Figure 2.28, there are few striking differences in the night time period; only four times is the difference more than two times the standard deviation: decreases in 1986-1987 and 1992-1993, increases in 1991-1992 and 1997-1998. In the morning rush hour there was a significant decrease three times, in 1978-1979, 1982-1983, and 2002-2003.

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22.00-7.00 (change) 7.00-9.00 (change) 9.00-13.00 (change) 22.00-7.00 7.00-9.00 9.00-13.00 95% significance level

Figure 2.28. Pedestrian-car KSI casualties by period of the day, 22:00 h -13:00 h.


Between 9.00 and 13.00 (between the morning rush hour and the afternoon period) there were three considerable decreases, in 1978-1979, 1989-1990, and 1996-1997. As shown in Figure 2.29, between 13.00 and 16.00 (afternoon period) there were five special differences: decreases in 1980-1981, 1983-1984, 1984-1985, and 2003-2004, increase in 1985-1986. Between 16.00 and 19.00 (evening rush hour) there was a very sharp KSI decrease in 1978-1979 and considerable decreases in 1983-1984 and 1991-1992.

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13.00-16.00 (change) 16.00-19.00 (change) 19.00-22.00 (change) 13.00-16.00 16.00-19.00 19.00-22.00 95% significance level

Figure 2.29. KSI casualties by period of the day, 13:00 h – 22:00 h.

2.4.1.17. Gender of victim

Over 1976-2004 most (60%) of the pedestrian-car KSIs were male. This changed in the 1976-2004 period from 61% in 1976-1980 to 57% in 2000-2004. You can see this in Figure 2.30, in which the male and female numbers get nearer and nearer to each other. In 2004 there were 186 male KSIs and 165 female KSIs. The female KSIs considerably changed in: 1979-1980, 1981-1982, 1983-1984, 1988-1989, 1993-1994, 1996-1997, 2003-2004 (all decreases), and in 2001-2002 (increase). The male KSIs showed steady decline with considerable decrease in: 1979-1980, 1984-1985, 1994-1995, 2001-2002, and 2003-2004.


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Male Female Male Female 95% significance level

Figure 2.30. Pedestrian-car KSI casualties by gender of victim.

2.4.1.18. Gender of car driver

Over 1976-2004 78% of the pedestrian-car KSIs were crashed into by a male car driver, 20% by a female car driver, and in 2% of the cases gender of the driver was not known. The male contribution changed in the 1976-2004 period from 82% in 1976-1980 to 71% in 2000-2004. Figure 2.31, in which the male and female numbers get closer to each other, illustrates this change.

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Male (change) Female (change) Male Female 95% significance level

Figure 2.31. Pedestrian-car KSI casualties by gender of car driver.

Significant changes of the male numbers, which were all decreases, occurred in 1979, 1982, 1984, 1987, 1991, 1997, and 2003. The female numbers increased strongly in 1986 and decreased considerably in 1983, 1985, and 2004. Notable is that none of the years experienced considerable change of both male and female numbers.


2.4.1.19. Age of KSI victim

We started from nine age groups and on the basis of the number of KSIs per group we regrouped them into six, which will be discussed below. Figure 2.32 shows all nine age groups. Figure 2.32 shows that the 0-11 year olds have by far the most (nearly 40%) KSIs. In second place are the oldest age groups of 60-74 years old and 75 years old and older. The smallest age group is the 25-29 year olds. Over the 1976-2004 period the share of the 0-11 years old decreased from 47% in 1976-1980 to 27% in 2000-2004. In the same period the share of the other age groups increased by one to at most five percent points per group.

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Figure 2.32. Pedestrian-car KSI casualties by age group of victim, 1976-2004.

In Figure 2.33 and Figure 2.34 below you can see the development of the (after regrouping) six age groups and the significant differences. The 0-11 years olds indeed have the largest share, but it is also the group becoming smaller the fastest in the 1976-2004 period. The decreases were especially large in 1978-1979, 1981-1982, 1983-1984, 1984-1985, 1988-1989, 1991-1992, 1996-1997, 1999-2000, 2000-2001, and 2003-2004. For the 12-17 years old these were 1988-1989 (decrease), 1989-1990 (increase), and 1999-2000 (increase) and for the 18-24 years old 1982-1983 (decrease).


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12 - 17 (change) 18 - 24 (change) 0 - 11 (change)12 - 17 18 - 24 0 - 11 95% significance level

Figure 2.33. Pedestrian-car KSI casualties for the victim age groups 0-11, 12-17, and 18-24.

For the 25-39 years old there was significant increase in 1985-1986 followed by decrease in 1986-1987. For the 40-59 years old significant decrease occurred in 1983-1984 and significant increase in 1991-1992 followed by decrease in 1992-1993. Finally, there were some significant differences with the 60 years old and older, i.e. a very sharp decrease in 1978-1979, and a slightly less sharp decrease in 1989-1990. It is again clear that the age groups with the most KSIs also have the most significant differences. We can expect this, because small numbers have relatively large standard deviations.

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25 - 39 (change) 40 - 59 (change) 60+ (change) 25 - 3940 - 59 60+ 95% significance level

Figure 2.34. Pedestrian-car KSI casualties for the victim age groups 25-39, 40-59, and 60+.


2.4.1.20. Age of car driver

Whereas the previous section showed that the 0-11 years old are the main victims in pedestrian-car crashes, Figure 2.35 below shows that over 1976-2004 the 18-24 years old (26%) and the 30-39 years old (23%) are the main car drivers in pedestrian-car crashes. The age groups 25-29 and 40-49 both contribute about 15%, the 50-59 years old about 10%, the 60-74 years old 7%, and the 75+ aged only 1%. Over 1976-2004 the share of the 18-24 years old decreased from 27% in 1976-1980 to 21% in 2000-2004 and the share of the 25-29 aged from 17% to 12%. The 30-39 years old stayed on the same level, whereas the other age groups increased their contribution by 1 to at most 4 percent points per group.

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Age car driver

% c

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s K

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Figure 2.35. Pedestrian-car KSI casualties by age group of car driver, 1976-2004.

In Figure 2.36 and Figure 2.37 below you can see the development of six age groups and the significant differences. The age groups 60-74 and 75+ have been grouped together into 60+. The age groups 18-24 and 25-29 have the sharpest annual decline (both 7%), then the age group 30-39 (6%), 40-49 and 50-59 (both 5%), and the 60+ aged have the least annual decline (3%).


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Figure 2.36. Pedestrian-car KSI casualties for the car driver age groups 18-24, 25-29, and 30-39.

As can be seen in Figure 2.36 and Figure 2.37, there are many years with considerable decrease and some with significant increase. Important years of change are (if not specified a decrease): 1979 (age groups 25-29, 30-39, 40-49, 60+), 1981 (18-24), 1981 increase (40-49), 1982 (25-29), 1984 (60+), 1985 (18-24, 30-39), 1987 (18-24), 1988 (60+), 1989 (40-49, 50-59), 1992 (18-24, 50-59), 1993 (25-29), 1997 (18-24, 25-29), 2000 (25-29), 2001 (18-24), 2004 (25-29). So, the different age groups can have quite different years of significant change.

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40 - 49 (change) 50 - 59 (change) 60+ (change) 40 - 49 50 - 59 60+ 95% significance level

Figure 2.37. Pedestrian-car KSI casualties for the car driver age groups 40-49, 50-59, and 60+.


2.4.1.21. Alcohol/no alcohol

For this variable it is only possible to refer to the 1976-2003 period. For more than 90% of the pedestrian-car KSI victims no alcohol was involved in the accident. This percentage hardly changed during the whole period. Here it is also the case that the group with the most KSIs has the most significant differences.

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No acohol Alcohol No alcohol Alcohol 95% significance level

Figure 2.38. Pedestrian-car KSI casualties by alcohol/no alcohol.

There were sharp no alcohol KSI decreases in 1981-1982, 1983-1984, 1984-1985, and in 1990-1991, but the decreases were even sharper in 1978-1979 and 1988-1989 (see Figure 2.38). There were sharp alcohol KSI decreases in 1979-1980 and 1986-1987, followed by sharp increase in 1987-1988, and again by decrease in 1988-1989, whereas there was sharp decrease in 1996-1997 as well.

2.4.1.22. Light conditions and street lighting

By far the most pedestrian KSIs occur during daytime. For this variable it is also only possible to refer to the 1976-2003 period. Figure 2.39 shows clearly that the largest KSI condition (daylight) also decreased the fastest (6.23% a year). The other condition (dark with street lighting) decreased by 5.31% a year. It also shows the sequence on the basis of the number of KSIs, which is daylight, dark without street lighting, and dark with street lighting. The significant changes (all of which were decreases) were during daytime in 1978-1979, 1981-1982, 1983-1984, 1990-1991, 1991-1992, and 1996-1997. For dark with street lighting the significant decreases were in 1978-1979, 1984-1985, 1986-1987, 1994-1995 and 2002-2003, whereas there was significant increase in 1985-1986. For dark without street lighting, there was significant decrease in 1978-1979, 1982-1983, 1986-1987, and 1995-1996, and significant increase in 1987-1988 and 2002-2003.


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daylight (change) dark - with street lighting (change)dark - no street lighting (change) daylight dark - with street lighting dark - no streets lighting 95% significance level

Figure 2.39. Pedestrian-car KSI casualties by light conditions.

2.4.1.23. Weather and road conditions

When examining this combination of variables, it is important to keep in mind that it is possible that the road surface is wet even though it is not raining. For this variable it is also only possible to refer to the 1976-2003 period. The subdivisions in this analysis are dry-dry, dry-wet, and wet-wet because these three have the largest share of the total number of KSIs. Here the first ‘dry’ or ‘wet’ refers to the weather and the second to the road condition. By far the most pedestrian-car KSI crashes (approximately 70%) occur during dry weather. The second largest group is dry-wet (13%), and the third largest group is wet-wet (12%). You can find the developments of these three in Figure 2.40.

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Figure 2.40. KSI casualties by weather and road conditions.


Figure 2.40 shows a number of significant differences in all three conditions. The most significant of those in dry-dry (all decreases) were in 1978-1979, 1984-1985, and 1987-1988. For dry-wet, the most significant differences were in 1978-1979, 1981-1982, 1988-1989, 1991-1992 (all decreases) and 1987-1988 (a very sharp increase). For wet-wet, the most significant differences were in 1978-1979, 1980-1981, 1988-1989, and 1994-1995 (decreases) and 1981-1982 and 1997-1998 (increases).

2.4.2. Relevant subdivisions

In Section 2.2.3, a subdivision is defined to be relevant if two or more subgroups which relevantly contribute to the total number of pedestrian-car KSIs, i.e. more than 10%, are different with respect to 1. significant changes in the number of KSIs, i.e. in two or more years there

is difference between the sign of 95% significant changes or regarding the presence of a 99% significant difference, or

2. overall trend of the number of KSIs, i.e. there is a difference of more than one percent point in the average annual decline percentage, or

3. the supposed occurrence or the estimated magnitude of an influence factor;

4. risk development. The third criterion we did not consider in this chapter. It is dealt with in Chapter 3, in which the possible influence factors are identified. Difference in risk development (the fourth criterion) are checked in Section 2.4.3. On the basis of the tables in Appendix 2, which give an overview of the results of the descriptive analysis, we evaluated the subdivisions considered with respect to: − the contribution of subgroups to the total number of pedestrian-car KSI

casualties, − differences in the overall trend of the sufficiently large subgroups, and − differences in the years of significant change (trend breaks). On the basis of this evaluation, we concluded the following subdivisions to be relevant: − urban / rural; − location: intersection / road section; − season: winter / spring / summer / autumn; − day of week: Mon-Thu / Fri-Sun; − hour: 7:00-19:00 hr / 19:00-7:00 hr; − gender of victim: male / female; − gender of car driver: male / female; − age of victim: 0-11 / 12-24 / 25-59 / 60+; − age of car driver: 18-24 / 25-29 / 30-39 / 40-49 / 50-59 / 60+; − weather and road condition: dry – dry / dry – wet / rain – wet. Speed limit (50 km/h / 80 km/h) is not included because this subdivision is almost equivalent to the ‘urban / rural’ subdivision. Similarly light conditions is not included, because it resembles the subdivision into day hours and night hours.


2.4.3. Exposure and risk

Possible exposure measures for pedestrian-car crash risk are described in Sections 2.3.2, 2.3.3, and 2.3.4. In latter section we concluded that for the analysis of pedestrian-car crash risk population number is the best practical exposure measure. Population number is available by gender, age, and region. The subdivisions by gender and age are considered relevant (see previous section). The other relevant subdivisions are not discriminatory regarding population number. The subdivision by region was not included in the descriptive analysis above, so that we did not determine the relevance of this subdivision with respect to differences in overall trends and trend breaks. Below we will describe trends and trend breaks of risk and exposure for the subdivisions by gender of victim, age of victim, and region.

2.4.3.1. Trends pedestrian-car KSI risk

Risk computed as the number of pedestrian-car KSI victims divided by the population number has a declining trend with an average annual decline of 6.6% (see Figure 2.41). In the periods 1976-1978 and 1979-1981 decline was less sharp (-1.5%), whereas in 1982-1985 it was sharper (-7.9%). From 1978 to 1979 risk fell deeply (-15%) and from 1985 to 1986 risk increased (+5.1%). After 1986 risk shows a steady decline until 2003. From 2003 to 2004, risk decreased by 20%.

0

20

40

60

80

100

120

140

160

180

1975 1980 1985 1990 1995 2000 2005

pede

stria

n-ca

r KSI

risk

(vic

tims

/ mill

ion

inha

bita

nts)

0

100

200

300

400

500

600

700

800

pede

stria

n-ca

r KSI

risk

(vic

tims

/ 100

0 m

illio

n pe

dest

rian

kms)

KSI risk (population) KSI risk (pedestrian kms)

Figure 2.41. Pedestrian-car KSI risk.

Figure 2.41 also shows risk computed on the basis of pedestrian kilometres. Because the 0-11 aged are not included in the National Travel Survey, which the pedestrian kilometres stem from, we could only depict this risk from 1994. As we can see in the figure risk based on pedestrian kilometres develops slightly less steep than risk based on population numbers, which is an indication of a small average annual decrease of pedestrian kilometres per inhabitant.


2.4.3.2. Pedestrian-car risk by gender of victim

Pedestrian-car KSI risk for males declines faster over 1985-2004 (7.2% per year) than for females (6.6% per year). This made that the large difference between male and female risk in the end seventies – male risk was more than 50% greater than female risk then – diminished to almost zero in 2004 (see Figure 2.42). Development of female risk is clearly more irregular than male risk, which steadily declined without large jumps.

0

20

40

60

80

100

120

140

160

180

200

1975 1980 1985 1990 1995 2000 2005

pede

stria

n K

SI ri

sk (v

ictim

s / m

illio

n in

habi

tant

s)

Male Female

Figure 2.42. Pedestrian-car KSI risk by gender.

2.4.3.3. Pedestrian-car risk by age of victim

As is shown by Figure 2.43, for all victim age groups pedestrian-car KSI risk has decreased over 1980-2004 and at the same time the differences in risk between age groups have become smaller. The age groups with highest risk are 0-11 and 75+, then 60-74, 18-24, and the rest. Noteworthy is the large difference in risk between 75+ and 60-74 aged. Both age groups include 13% of the total number of pedestrian-car KSI victims over 1976-2004. The combination of these facts shows the relevance of further subdividing the 60+ age group into 60-74 and 75+.


0

50

100

150

200

250

300

0 - 11 12 - 17 18 - 24 25 - 29 30 - 39 40 - 49 50 - 59 60 - 74 75+

age group

pede

stria

n-ca

r KSI

risk

(vic

tims

/ mill

ion

inha

bita

nts)

mean 1980-1984 mean 2000-2004

Figure 2.43. Pedestrian-car KSI risk by age of victim, average values 1980-1984 and 2000-2004.

Risk in the age groups 12-17 and 18-24 are alike, just as in the groups 25-29, 30-39, 40-49, and 50-59, which supports the regrouping into the age groups 12-25 and 25-59, as proposed in Section 2.4.2. Figure 2.44 shows the development of pedestrian-car KSI risk over 1978-2004 for the age groups 0-11, 12-24, 25-59, 60-74, and 75+. Also this figure makes clear that for all distinguished age groups risk has declined. For the age groups with higher risk decline has been stronger, leading to smaller differences between the age groups.

0

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100

150

200

250

300

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450

1975 1980 1985 1990 1995 2000 2005

pede

stria

n-ca

r KSI

risk

(vic

tims

/ mill

ion

inha

bita

nts)

0 - 11 12 - 24 25 - 59 60 - 74 75+

Figure 2.44. Pedestrian-car KSI risk by age.


2.4.3.4. Pedestrian-car risk and exposure by region

Figure 2.45 and Table 2.4 show that there can be large differences in pedestrian-car KSI risk and in the annual decline of this risk among provinces. Figure 2.45 clearly illustrates the difference between the new province Flevoland with relatively small population density and old provinces with high population density as Noord-Holland, Zuid-Holland, Limburg, and Utrecht. The designers of the roads in Flevoland specially took into account road safety, which is unmistakably visible in this figure.

0

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160

pede

stria

n-ca

r KSI

risk

(vic

tims

/ mill

ion

inha

bita

nts)

mean 1980-1984 mean 2000-2004

Figure 2.45. Pedestrian-car KSI risk by province, average values 1980-1984 and 2000-2004.

Province Average annual decline

Groningen -5,0%

Friesland -8,4%

Drenthe -5,8%

Overijssel -6,5%

Gelderland -6,2%

Utrecht -6,3%

Noord-Holland -7,0%

Zuid-Holland -6,5%

Zeeland -4,6%

Noord-Brabant -6,5%

Limburg -7,6%

Flevoland -7,6%

Table 2.4. Average annual change of pedestrian-car KSI risk per province over 1978-2004.


2.4.3.5. Contributions of pedestrian victims and car opponents of different gender and age

Some subgroups are relatively often KSI victim in a pedestrian-car crash, while other subgroups are relatively often the car opponent in the crash. In Figure 2.46 for males, females, and the five age groups (see Sections 2.4.2 and 2.4.3.3) the average relative part (as a victim) in the total number of pedestrian-car KSI victims over 1985-2004 is plotted against the average relative part in the total population. For comparison, the same is done for the subgroups' part in the total number of KSI victims with car as opponent. As argued in Section 2.3.4, the part in the total population, i.e. the population per subgroup, is considered as a relative measure of exposure to pedestrian-car KSI casualty risk.

0

20

40

60

80

0 20 40 60 80% population

% K

SI v

ictim

in c

rash

with

car

% pedestrian-car KSI% …-car KSI% population = % KSI

male

female

75+

25-59

60-74

0-11

12-24

Figure 2.46. Part (as victim) in total number of victims in crash with a car (%) against part in total population (%).

On the interrupted line in Figure 2.46 the part in the number of KSI victims is equal to the part in total population. For a subgroup above this line, the part in the number of KSI victims is greater than the part in total population. So, the members of such a subgroup are relatively often KSI victim in a crash with a car. Subgroups below the line are relatively rarely KSI victim in a crash with a car. In Figure 2.46 we see that males and age groups 0-11 and 75+ are relatively often pedestrian-car KSI victim. Females and persons between 25 and 59 years old are relatively seldom pedestrian-car KSI victim. In comparison with other transport modes the pedestrians of age 0-11 and 75+ are relatively often in a severe crash with a car, whereas the pedestrians of age 12-24 and 25-59 are less often in a KSI crash with a car. This means that on top of general road safety policy the very young and very old pedestrians need additional attention. In Figure 2.47 for male and female car drivers and car drivers of six age categories (see Section 2.4.2) the average part as opponent in the total number of pedestrian-car KSI victims over 1985-2004 is plotted against the average relative part in the total population with driving licence. For


comparison, the same is done for the subgroups' part as opponent in the total number of KSI victims with car as opponent.

0

20

40

60

80

0 20 40 60 80

% population with driving licence

% c

ar o

ppon

ent o

f KSI

vic

tim% opponent in pedestrian-car KSI% opponent in …-car KSI% population = % KSI

male

female

60+

25-29 40-49

18-2430-39

50-59

Figure 2.47. Part (as car driver) in total number of victims in crash with a car (%) against part in total population with driving licence (%).

On the interrupted line in Figure 2.47 the part in the number of KSI victims is equal to the part in the total population with driving licence. In the figure we see that males and the age groups 18-24 and 25-29 are relatively often opponent in a pedestrian-car KSI crash. Females are relatively seldom the car driver in a pedestrian-car KSI crash. Comparison with other transport modes shows that the car driver subgroups are not more or less often involved in KSI accidents with pedestrians than in general KSI accidents.

2.5. Overview

Section 2.5.1 describes the main conclusions of this chapter. In Section 2.5.2 the results of this chapter are used to give a period by period description of the development of the number of pedestrian-car KSI casualties and of pedestrian-car KSI casualty risk. Furthermore in this section we formulated hypotheses about the influence factors which probably underlie these developments.

2.5.1. Main conclusions

On the basis of the results of the descriptive analysis, overview tables were made that for each year and each collision type or subdivision indicate significant level changes by – or + signs (See Appendix 2). For each collision type and subdivision, the overview tables provide the contribution to the total number of casualties (KSI) is given as well as the overall trend over 1976-2004. The tables facilitate insight in where (which collision type and which subdivision of pedestrian-car) and when (which year) significant changes may have occurred. With this insight combined with the knowledge about the possible effects of explanatory variables (start, end, rough estimate of effect)


which is described in the next chapter, possible hypotheses about the influence of specific developments or measures on specific subdivisions of crash risk can be formulated. For example, the fact that in winter 1979 a significant decline occurred and was followed by a significant increase in winter 1980 hints at a possible temporary seasonal effect (see also Section 2.5.2). Taking a look at the results in the overview tables we conclude the following. Generally, the subgroups in the subdivisions have similar overall trend but there may be differences of 1% or more. Furthermore, there are differences with respect to trend breaks in one or more years. The 1979 break is general, i.e. valid for the largest subgroups as defined by a subdivision. The average annual decline of the number of pedestrian-car casualties over 1976-2004 is 6%. For the subgroups this annual decline lies between 5% and 7%, with one exception: the 0-11 aged children show an average annual decline of 8%. Based on differences with respect to the overall trend, the years of significant change (trend breaks), the contribution of subgroups to the total number of pedestrian-car KSI casualties (see Section 2.2.3), and differences in risk development the following subdivisions are considered relevant: − urban / rural; − location: intersection / road section; − season: winter / spring / summer / autumn; − day of week: Mon-Thu / Fri-Sun; − hour: 7:00-19:00 hr / 19:00-7:00 hr; − gender of victim: male / female; − gender of car driver: male / female; − age of victim: 0-11 / 12-24 / 25-59 / 60-74 / 75+; − age of car driver: 18-24 / 25-29 / 30-39 / 40-49 / 50-59 / 60+; − weather and road condition: dry – dry / dry – wet / rain – wet; − province: Groningen, Friesland, etc. For these particular subdivisions, models of crash risk should first be separately constructed, and the results of the separate models should be compared to decide whether models at disaggregated levels are necessary.

2.5.2. Hypotheses

This section summarizes the most important developments in victim numbers, exposure and risk. Periods with different trend and years with trend break are distinguished and described. If possible, for each period or year the most probable explanation(s) for the trend or trend break in that period or year are stated in the form of one or more hypotheses. The coding of the hypotheses is explained in Section 1.4.2. Conclusions with respect to victims numbers in subgroups which are defined by a subdivision which does not need subdivision of our exposure measure, i.e. population number, can easily be translated to conclusions regarding risk. For example, it makes no sense to subdivide population into urban and rural population. So to compute urban and rural risk we use total population. Because population has a gradual increasing development over time,


dividing victim numbers by population numbers so as to obtain risk affects the overall slope only, not the trend breaks. 1976-1981 Except for the decline in 1979, in 1976-1981 pedestrian-car KSI risk was stable (see Figure 2.41). Male risk decreased over 1979-1981, but female risk slightly increased (see Figure 2.42). 1979 and 1985 Both in winter 1979 and winter 1985 there was a significant drop in the number of pedestrian-car KSI victims (see Figure 2.24). Especially the 1979 winter drop is observable in almost all subdivisions considered. Furthermore, both drops extend to other crash types as moped-car (Blois, Goldenbeld and Bijleveld, 2007), bicycle-car, and car-car (Janssen and Bijleveld, in prep.). Both winter drops were followed by a somewhat less strong increase in the winter of the following year. On the basis of these notions we formulated the following hypothesis: H79&85.1.A: The winter drop of the number of pedestrian-car KSI victims in

1979 and 1985 was caused by a temporarily seasonal effect, probably winter weather circumstances.

1982-2003 1982-2003 is a period of steady decline of pedestrian-car KSI risk (see Figure 2.41). In 1982, 1984, 1985, 1989, 1991, and 1997 decline is more than 10%. In 1986, 1988, and 1996 there is slight increase or almost no decrease. From 1982 to 1983 the number of pedestrian-car KSIs on urban road sections decreased by 200 KSIs (-20%), whereas in the same year the number of KSIs on urban intersections increased by 176 (+52%). These changes were, at least for a great part, caused by the introduction of a new crash category for road section crashes under the influence of intersections in the database in 1983. From then these crashes were categorized as intersection crashes instead of road section crashes. H82-83.1.A: The considerable decrease of pedestrian-car KSIs on urban

road sections and the considerable increase on urban intersections both in 1982-1983 were caused by a change in the definition of road section and intersection crashes.

Since 2001 the number of pedestrian-car KSI victims on 30 km/h roads starts to increase strongly (see Figure 2.18). This corresponds to our knowledge that since the start of the Sustainable Safety program in 1997 the transformation of 50 km/h roads into 30 km/h roads has been accelerated. In fact, this is important knowledge which can be used to better estimate the growth of the 30 km/h zones, about which the available information is restricted. The number of 0-11 aged pedestrian-car KSI victims drops significantly from 1999 to 2000 and from 2000 to 2001. We think these drops can be related to the proceeding transformation of 50 km/h roads into 30 km/h roads and formulate the following hypothesis. Chapter 3 tells more about the development of 30 km/h zones.


H99-01.1.A: The transformation of more and more 50 km/h roads into 30

km/h roads as part of the Sustainable Safe program, which started in 1997, resulted in the strong decrease of pedestrian-car KSI victims of 0-11 years old in 2000 and 2001.

2004 From 2003 to 2004 there is a 19% drop of pedestrian-car KSI risk (see Figure 2.41). The 2004 drop of pedestrian-car KSI victims is found for all crash types considered, except for pedestrian-bicycle, which represents a relatively small number of KSIs (see Figure 2.13 and Appendix 2). 1976-2004, general Decline of male risk is stronger than of female risk (see Figure 2.42 and Figure 2.30). Also the involvement of male car drivers in pedestrian-car KSI accidents decreases faster than the involvement of female car drivers in such accidents (see Figure 2.31). We suggest that these differences can be related to changes in the pattern of life of men and women which also changed their transport behaviour. The greater participation of women in the labour market is an example of such a life pattern change. In Chapter 3 we will describe this kind of developments in more detail. Here, we already state a preliminary hypothesis: H76-04.1.A: Because of changes in pattern of life, e.g. the greater

participation of women in the labour market, pedestrian-car KSI casualty risk decreased more slowly for females than for males.

Injury severity decreased from half seventies to end nineties, on both intersections and road sections (see Figure 2.16, Figure 2.20, and Figure 2.22). Since end nineties injury severity has steadily increased again. Inspection of the development of injury severity per age group tells us that the decline of the ratio killed : seriously injured from 1976 to 1997 is strongest for the 0-39 aged persons. The decline of the ratio seriously injured : slightly injured from 1976 to 1997 is strongest for the 0-24 aged and 40-49 aged people. At this instance we do not have sufficiently reliable information about these developments which could help us to get a clue about the underlying influence factors and state a hypothesis. Possible explanations can be looked after in the area of technical improvement of cars (e.g. ABS), better checks on maximum speed in urban areas, improvement of health care, etc. The increase of injury severity after 1998 might be explained by the use of more and more heavy cars (SUVs), increase of efficiency in health care, diminishing registration of less severe injury accidents, etc. Chapter 3 will describe these kind of developments in more detail. In the beginning of the eighties pedestrian-car KSI risk differed largely between age groups (see Figure 2.43). Especially, risk for 0-11 aged children, for persons above age 75, and to a less extent for 60-74 aged persons was very high. To illustrate the then differences: risk for 0-11 years old children was about eight times as high as risk for 30-39 aged persons. Until the start of the 21st century risk for the high risk age groups declined by a factor 4 or 5, whereas it declined by factor 2 for the low risk groups. As such risk differences among the age groups became much smaller. At this


instance we lack the information to be able to state what was at the heart of this levelling out of risk over the age groups. Chapter 3 tries to get more grip on this problem. Young car drivers are, compared to their part in total population, relatively often involved in pedestrian-car KSI crashes. This is also valid for other crash types with car as opponent. H76-04.2.A: Young car drivers are compared to their part in road traffic

relatively often involved in pedestrian-car crashes and other crashes with car as opponent.

Comparison of pedestrian-car KSI risk among provinces shows that the young province of Flevoland has relatively low risk, less than half the risk of the other provinces. We attribute this to the widespread application of new city and road design principles. For example, in the cities in Flevoland they consequently enforced division of slow traffic flow and motorized traffic flow and division of residential areas with (enforced) low maximum speed and access roads with higher maximum speed. H76-04.3.A: The consequent application of new, safe city and road design

principles in Flevoland has resulted in considerable lower pedestrian-car KSI risk than in the other provinces.


3. Developments and interventions in 1970-2003

3.1. Outline

A literature study was conducted to identify interventions and developments that may likely or evidently have influenced the development of pedestrian-car KSI crashes over time. This part of the report describes the results of the literature scan and contains the following sections: explanation of types of influence factors (Section 3.2), results of literature scan (Section 3.3), and overview of findings (Section 3.4).

3.2. Types of influence factors

The factors that determine how crashes and crash risk evolve over time are called the influence factors. Influence factors can be either background variables (or: autonomous developments), e.g. the increase of stress in society, or policy measures (or: interventions), e.g. the set-up of road safety education projects. Figure 3.1 presents the model of influence factors.

EXPLANATORY VARIABLESExamples:•Use of alcohol and drugs•Technicalstate of cars• ‘SustainablySafe’ content

INFLUENCE FACTORS •Background variables •Policy measures

RISK

CRASHES and/orCASUALTIES

INDICATOR VARIABLESfor exposure. Examples:•Person kilometers•Vehicle fleet

EXPOSURE

Figure 3.1. Variables that affect development of accidents over time.

Influence factors can be continuous developments, e.g. the building of roundabouts or demographic developments, or changes at one point in time, e.g. the introduction of free public transport for students in 1991. Policy measures at one point in time are generally called interventions. Influence factors are quantified by way of the explanatory variables. For example, driving speed can be an explanatory variable for the stress in society and the number of days with frost can be an explanatory variable for the severity of winter. Explanatory variables can be induced from the influence factors and vice versa. One way of finding the explanations of developments in crashes and crash risk is to start searching for developments in society (the background variables) or policy measures taken during trends observed or around the year of a trend break. For this purpose, literature and expert knowledge can be consulted. Next, the accessory explanatory variables must be found. Another way of analysing the problem is to start from information about the development of explanatory variables and then try and find the underlying influence factors.


3.3. Results of literature scan

This results section lists the main literature sources (Section 3.3.1) and describes risks of pedestrians in the seventies (Section 3.3.2), mobility and lifestyle developments that may have affected pedestrian-car risk (Section 3.3.3), main safety interventions (Section 3.3.4), specific road safety measures (Section 3.3.5) and various other developments, such as weather, economy, social-cultural changes (Section 3.3.6).

3.3.1. Main literature sources

The research literature about interventions or developments that affected pedestrian safety in general and pedestrian-car crashes in particular is mainly covered and summarised in the following sources: − Hummel, T. (1998). Dutch pedestrian research review. SWOV-report R-9; − SWOV Factsheet (2005). Road safety of young children in the

Netherlands (In Dutch); − Centre for Transport Research AVV (2003). Vulnerable road users.

Centre for Transport Research AVV (In Dutch); − Factsheets Vulnerable Road Users 01 (Pedestrians), 02 (Pedestrians in

need of support means), 09 (Elderly) and 22 (Children 4-8) from the Centre for Transport Research AVV.

The following publications provided more general evidence on safety benefits of infrastructural measures: − AVV (2004). Veilig op weg : Monitoring Startprogramma Duurzaam Veilig.

Eindverslag. Transport Research Centre (AVV), Ministry of Transport, Public Works and Water Management, Rotterdam, The Netherlands;

− PROMISING (2001) Measures for pedestrian safety and mobility problems : final report of Workpackage 1 of the European research project PROMISING (Promotion of Measures for Vulnerable Road Users), Deliverable D1. National Technical University of Athens NTUA, Athens;

− Reekmans, S., Nuyts, E and R. Cuyvers (2004). Effectiviteit van infrastructurele verkeersveiligheidsmaatregelen. RA-2004-39. Steunpunt Verkeersveiligheid, Diepenbeek;

− Wegman, F.C.M., Dijkstra, A., Schermers, G. en P. van Vliet (2005). Sustainable Safety in the Netherlands: the Vision, the Implementation and the Safety Effects. 3rd International Symposium on Motorway Geometry Design.

Several publications provided us some insight into developments of pedestrian safety in the seventies and early eighties: − Blokpoel, A. and A. van Boven (1983). De verkeersonveiligheid in

Nederland 1981/1982 (Road safety in the Neteherlands 1981/1982). Report 83-42. SWOV, Leidschendam;

− Blokpoel, A. (1984). Effecten van schooltijden en afstanden op de verkeersonveiligheid van leerlingen van het basisonderwijs. R-84-30. SWOV, Leidschendam;

− Janssen, S.T.M.C. (1984). Demonstratieproject herindeling en herinrichting van stedelijke gebeiden (in de gemeenten Eindhoven en Rijswijk). R-84-28-I. SWOV, Leidschendam;


− Minnen, van J. (1978). Trendanalyse verkeersonveiligheid. Trend analysis road safety 1. Report –78-25a. SWOV, Leidschendam;

− Veilig Verkeer Nederland (1982). Wat bezielt Veilig Verkeer Nederland (What moves the Dutch Safe Traffic Organization?) Hilversum, Veilig Verkeer Nederland;

− Wegman, F.C.M (1987). Stimuleringsplan Äctie –25%". R-87-29. SWOV, Leidschendam.

For more general information on social trends that can affect mobility and risk we read the following publications: − Breedveld, K., Broek, van den A. (2001). Trends in de tijd. Een schets

van recente ontwikkelingen in tijdsbesteding en tijdsordening. Sociaal Cultureel Planbureau, Den Haag;

− Harms, L. (2003), Mobiel in de tijd. Op weg naar een auto-afhankelijke maatschappij, 1975-2000. Report 2003-14. Sociaal Cultureel Planbureau SCP, Den Haag;

− Langen, A. and M. Hulsen (2001). Schooltijden in het basisonderwijs: feiten en fictie. ITS, Stichting Katholieke Universiteit, Nijmegen;

− Schoon, C.C. (2005). De invloed van sociale en culturele factoren op mobiliteit en verkeersveiligheid. Report R-2005-7. Institute for Road Safety Research SWOV, Leidschendam;

− Schreuders, M. & C.C. Schoon (2005). Omgevingsverkenning Ruimtelijke Inrichting en Verkeersveiligheid. Report. Institute for Road Safety Research SWOV, Leidschendam.

The following publications informed us about changes in social position and lifestyle of the elderly: − De Klerk, M.M.Y. (2001). Rapportage ouderen 2001. Veranderingen in de

leefsituatie. Sociaal Cultureel Planbureau, Den Haag; − De Klerk, M.M.Y en J.M. Timmermans (1999). Rapportage ouderen 1999

(Report on the Elderly). Sociaal en Cultureel Planbureau, Den Haag. For a complete overview of the consulted literature the reader is referred to the References at the end of this report.

3.3.2. Risks of different travel modes in the seventies

There were a number of continuous developing policy measures such as traditional black spot approach, the construction of more and better signalised intersections, construction of new crosswalks, construction of roads according to the homezone principle. It can be assumed that in the seventies these combined infrastructural measures developed gradually and only affected risk level in a very modest way. For example, by 1980 there were only about 1800 special homezone streets (Kraay and Bakker, 1984) which is less than 1% of the total streets in the Netherlands. The BREV subsidy led to some experiments in 5% of the Dutch municipalities, that is in 29 municipalities (Kraay and Bakker, 1984). Kraay and Bakker (1984) also paid attention to how traffic measures were related to policy aims in the municipalities that worked with BREV subsidy. In general the studied municipalities formulated targets dealing with


accessibility of inner city and excluding traffic that has no destination in the city area. Also, municipalities took traffic measures in response to needs and complaints of citizens. The researchers note that municipalities in general did not formulate clear road safety targets in regard to safety of pedestrians and bicyclists (Kraay and Bakker, 1984; p. 24). Besides infrastructural measures, other developments that could have affected pedestrian-car crashes were the school traffic education, attention paid by schools to the safety of school routes, the volunteer organization of crossing guards ('lollipop men') and increased public awareness stimulated by newspaper and television coverage and special safety interest groups. It can be assumed that political and public awareness of road safety as a social problem drastically increased after the bad road safety year 1972 which claimed over 3000 road fatalities in the Netherlands. Although some measures such as obligatory seat belts and moped helmets did not affect pedestrian-car safety directly they contributed to the public awareness of road safety as a social problem. Although a historian may well unravel a treasury of objective data on these subjects, our brief literature scan only reveals some glimpses of these developments. Table 3.1 presents the fatality risk of different travel modes in 1978. In 1978 walking was about twice more risky than bicycling and the fatality risk of elderly pedestrians was about four times higher than that of other age groups.

Risk (fatalities per 108 km)

Age Car Bicycle Pedestrian

12-14 0,33 3,38 6,76

15-17 1,80 2,58 6,91

18-24 2,58 2,11 4,34

25-35 0,92 1,01 2,32

36-44 0,86 2,21 5,23

45-64 1,04 4,91 5,89

65 and older 3,16 29,48 27,07

Total 1,31 4,38 8,39

Total males 1,49 5,53 11,94

Total females 0,99 3,11 5,79

Table 3.1. Risk of driving, bicycling and walking in 1978 (Source: Veilig Verkeer Nederland (1982).


Table 3.2 presents the mortality rate for walking and bicycling 1965, 1972 and 1980.

Mortality (fatalities per 100.000 inhabitants)

Age Walking Bicycle

1965 1972 1980 1965 1972 1980

0-14 6,2 5,8 2,2 3,0 4,5 3,3

15-24 1,7 1,2 0,8 2,4 1,7 2,3

25-34 1,7 1,1 0,8 1,1 1,2 0,9

35-44 1,3 1,3 0,8 0,9 1,2 1,1

45-64 3,7 3,5 1,5 3,6 3,7 2,2

65+ 16,0 15,9 8,0 13,0 15,7 10,1

Total 4,7 4,4 2,1 3,5 4,2 3,0

Table 3.2. Fatality risk per 100.000 inhabitants for walking and bicycling in 1965, 1972 and 1980 (Source: Veilig Verkeer Nederland (1982).

As we can see, in Table 3.2 the fatality risk for walking has decreased more strongly between 1972 and 1980 than between 1965 and 1972. The strongest decrease in fatality risk has occurred for young pedestrians aged 0-14 years old. The 45-64 and 65+ groups of pedestrians also show large decreases in fatality risk between 1972 and 1980. Another insight into development of crash involvement of pedestrians is given by Oei (1984). Figure 3.2 presents the involvement of pedestrians in injury crashes for different age groups. The 65+ aged pedestrians were about twice as much involved in injury crashes as the other age groups. Also the 15-24 aged were relatively often involved in injury crashes. Pedestrians of 0-14 years old are not included in the figure.

40

60

80

100

120

140

160

180

200

220

240

1977 1978 1979 1980 1981 1982 1983

Pede

stria

n in

volv

ed w

ith in

jury

cra

shes

pe

r 100

.000

.000

trav

elle

r km

s

15-24 years old pedestrians 25-44 years old pedestrians45-64 years old pedestrians 65 + years old pedestriansAll pedestrians 15+

Figure 3.2. Involvement of pedestrians in injury crashes per 100,000,000 traveller kilometres in 1979-1982, split out for different age groups (Source: Oei, 1984)


3.3.3. Mobility and lifestyle developments

This section describes the developments in mobility of pedestrians in general (Section 3.3.3.1), for children (Section 3.3.3.2), for women (Section 3.3.3.3), and for the elderly (Section 3.3.3.4).

3.3.3.1. General mobility of pedestrians

The mobility of pedestrians can be subdivided into to door-to-door walks and walks as part of a larger multi-modal travel chain. The factsheet 'Vulnerable Road Users' of the Transport Research Centre AVV describes the following developments in pedestrian mobility between 1980 and 2000: − 10% less door-to-door walks; − decrease of mean walking distance per person from 1,06 km to 0,94

(12% decrease); − decrease of walking time from 76 hours per year to 59 hours per year; − increase of total walks as result of an increase of trips with car and public

transport; − increase of total walking distance with 14% as result of increase of

population.

3.3.3.2. Mobility of children

In 1992, a survey was carried out to measure more precisely how children travelled to primary school (Houwen et al. (2003). The lack of relevant data was the direct incentive to carry out this research, sponsored by the National Bike Council (in Dutch: 'het Fietsberaad'). Nearly two-thousand filled-in surveys (response rate 25%) provided information about travel behaviour of 3136 children. Main results were: − Half of the children goes on bike to school, one third on foot and one sixth

is brought by car. − Age of the child, age difference between parent and child, and

urbanization of living and school area were major factors in determining whether the child went to school alone or under supervision. The younger the child, the larger the age difference between parent and child and the more urbanized the living and school area, the higher the chance that the child will be supervised going to school.

− The most frequently mentioned reasons for supervising the child on the way to the school were that the child is too young to go on its own, that the route to school is not safe and that it is cosy to go together.

− The most important reasons for parents to always bring their child to school with car were that the school is too far away, that the route to the school is not safe, and that school lies on the route to work.

− How children travel under supervision (by car, bike, or on foot) depended on the distance to school, the urbanization of living and school area, the travel modus of the mother, and the availability of a car.

In the period 1994-2002, the number of pedestrian kilometres of children decreased with 20 percent and the number of bike kilometres decreased with 10 percent (SWOV Factsheet 'Road safety of children in the Netherlands') . In the same period, the number of car kilometres increased with 10%. This could indicate that from 1994 onwards parents more frequently bring and take children to schools instead of letting them walk or bike alone.


The factsheet 'Vulnerable road users 09' about young children (4-8 years) of the Transport Research Centre AVV reports several basic facts about mobility and safety of young children. Children aged 4-8 comprise a group of slightly less than 1,000,000 (about 6% of the total Dutch population). Of this group about 95% attends a regular primary school and 5% a special education school. The number of young children that is transported to school or supervised during going to school has steadily increased over time and is even now still increasing. Of children going to foot to school about 85% does this under supervision. The age at which children are allowed to travel individually to school (by foot or by bike) has increased. Young children comprised about 8% of the total number of traffic fatalities in the fifties and in recent years about 1%. Clearly, autonomous mobility of young children has steadily decreased from the sixties to the beginning of the 21st Century. It is expected that the number of double-income households is very important for the extent to which pre-school and primary school children are allowed to go outside and play or move in unprotected space. Since the school year 1983/1984 each (primary) school is legally required to offer stay facility for children outside formal school time. In 1981 about 84% of the primary schools in larger cities in West-Netherlands provided these stay facilities and only 44% of the rural area schools (Blokpoel, 1984). Blokpoel (1983) estimated that 120 seriously injured traffic casualties aged 4-12 years could be prevented if all primary school children stayed at their school during the afternoon break. Blokpoel also mentions that with the start of 1985/1986 all primary schools are required to start and end school time for all children at the same time. This could possibly lead to later start times and later end times (and lesser playing time) with a possible maximum prevention of about 70 casualties.

3.3.3.3. The changing situation of women

Table 3.3 provides an insight into the fast growing participation of women in the labour market between 1971 and 1997.

Males Females Total

1971 84,5 29,4 57,4

1980 76,1 31,8 53,8

1990 74,6 43,6 59,3

1997 77,4 51,8 64,7

Table 3.3. Gross participation in labour market (in persons), based on population aged 15-64, 1971-1997. Source: Social Cultural Planbureau 1998, p. 222.

Table 3.4 shows that starting in the eighties, the participation of Dutch women in the labour market has considerably increased. The table shows that even in the nineties the participation in the labour market of women between 25 and 54 years old strongly increased.


Age Females Males

1990 1999 1990 1999

15-24 47 46 47 49

25-34 59 74 92 94

35-44 49 64 93 94

45-54 36 53 85 90

55-64 12 18 43 46

Total 44 54 75 79

Table 3.4. Gross participation in the labour market of Dutch males and females 1990 and 1999. Source: Central Bureau Statistics, Survey Labour Force (Enquête beroepsbevolking); Labour force count Arbeidskrachtentelling (figures presented in Keuzenkamp and Oudhof, 2000).

3.3.3.4. The changing situation of the elderly

The 'Report on the Elderly 1998' (De Klerk and Timmersmans, 1999) informs us about some basic changes in the social situation and lifestyle of the elderly (55+) that may have had consequences for their mobility. From this report we note the following changes: − Between 1980 and 1994, the life expectancy for males increased with 2,1

years and for females with 1,1 years. − There has been a sharp fall in the participation in work by older men from

1970 to 1996. In 1973, three-quarters of men aged 60 were in paid employment; in 1996, less than a quarter.

− In the early nineties, the percentage of men aged 50-64 participating in the labour market started to increase again (slightly).

− The labour market participation rate of older women almost doubled between 1971 and 1997, to almost 27%.

− Especially, the 75+ aged people are prone to frequent changes in the degree to which they face health limitations.

From the "Report on the Elderly 2001" (De Klerk, 2001) we learn the following: − The percentage of people who are exercising has increased between

1983 and 1999, especially among the elderly. They are particularly active in terms of swimming, bicycling, tennis and walking.

− Between 1997 and 2000 the number of elderly people who work has increased. In the 50-64 age groups 64% of the men and 31% of the women were working in 2000. The increase for both men and women was approximately the same.

− In the nineties social participation of the elderly has continued to grow in a number of areas.

3.3.4. Main safety interventions

In the seventies there were no large scale road safety interventions specifically aimed at pedestrian safety. SWOV reports that review the safety interventions of the seventies (e.g. Blokpoel and van Boven, 1983) mostly


mention specific measures such as seat belt law (1975), moped helmet law (1975), the introduction of a legal alcohol limit (1974), and the required installation of a red reflector on the backside of bicycles (1979). The main interventions or developments in the seventies that may have influenced pedestrian-car safety were: 1. alcohol law; 2. some variant of blackspot approach; 3. homezone construction; 4. the work of crossing guards; 5. subsidy arrangement BREV (in Dutch: 'Beschikking rijksbijdragen

experimenten in verblijfsruimten'); 6. specific improvements of intersections; 7. specific local city initiatives. In practice, most of these interventions would only have had a minor effect on pedestrian-car crashes and an effect that should be less than the increase in risk by the annual growth in motorized traffic. Van Minnen (1978) reports that over the period 1964-1976 the number of pedestrian fatalities is decreasing since 1970 with the strongest decreases occurring between 1972 and 1974. After that the number of pedestrian fatalities seems stable for some years: 424 in 1974, 396 in 1975 and 403 in 1976. Van Minnen notes that since 60-65% of pedestrian fatalities is the result of a pedestrian-car crash, the increasing numbers of cars would lead to the expectation of an increase in pedestrian fatalities. He concludes that there is some (unknown) development that influences pedestrian safety in a positive way and thereby compensates for the growth of car traffic (Van Minnen 1978, p. 28). Basically the following major developments played a role in the eighties and continued in the nineties: 1. replacement of intersections by roundabouts (Section 3.3.5.7); 2. construction of 30 km/h zones (Section 3.3.5.6); 3. new and effective alcohol policies (Section 3.3.5.3); 4. starting trend of decreased walking, construction of playgrounds for

young children, and increased supervision of children (Section 3.3.3.2); 5. the work of traffic crossing guards; 6. new policies and developments in urban planning (construction of new

safer neighbourhoods with increased separation of residential and traffic functions) (Section 3.3.6.4);

7. further work on black spot approach (this section); 8. promotion plan –25% (this section). In 1990-1992, the Dutch Ministry of Transport tried to stimulate municipalities to conduct traffic safety policies more actively1992 by means of the Action -25%. Municipalities that could show increased effort to improve road safety could get higher subsidies. Mulder et al. (1994) investigated whether this Incentive Scheme Campaign demonstrably influenced road safety. They performed a road hazard analysis for the Dutch municipalities, based on the number of road accident victims. For this purpose, the municipalities were classified into the following three groups: (1) active; (2) less active; and (3) least active. These groups refer to the municipal policy-related activity in the road safety field in response to the influence of the Incentive Scheme Campaign -25%. The analysis compared the 1984-1986 period with the 1990-1992 period. The results of the study did


not offer a clear correlation between the degree of municipal activity and the development in road hazard. With regard to the evaluation of the effectiveness of the black spot approach, Vis (2000) states that the collected Dutch data were too limited and that the few evaluations have not sufficiently corrected for potential confounding factors such as changes in mobility, general trends, regression to the mean, and migration of crashes. In 1996, the number of Dutch pedestrians killed was approximately 50% less than the corresponding figure for 1986. This decrease was higher than that of the total number of traffic fatalities (which fell in the same period by 23%). This extra improvement in pedestrian safety was attributed to better protection of certain groups (e.g. children transported more by car and playing more in protected areas than on streets) and to reduction in the amount of walking (PROMISING, 20010, p. 19).

3.3.5. Specific road safety measures

This section describes specific road safety measures which can be expected to be relevant for the development of pedestrian-car casualties and pedestrian-cas casualty risk. The following measures will pass in review: crossing guards (Section 3.3.5.1), improvement of intersections (Section 3.3.5.2), enforcement of the prohibition of drink-driving (Section 3.3.5.3), intensified police enforcement (Section 3.3.5.4), the Sustainable Safe program 1997-2001 (Section 3.3.5.5), expansion of 30 km/h zones (Section 3.3.5.6), roundabouts (Section 3.3.5.7), and the introduction of 60 km/h zones (Section 3.3.5.8).

3.3.5.1. Crossing guards

Since 1947 crossing guards, also know as 'lollipop men' and 'lollipop ladies', help children to cross roads on their route to primary schools. The ever growing number of crossing guards necessitated good rules and facilities for their work. In March 1980 the Foundation Crossing Guards (in Dutch: 'Stichting Verkeersbrigadiers') was set up. In 2002, there were more than 36,000 adult crossing guards and over 17,000 youth crossing guards guarding child safety at school routes of over 1,500 primary schools. Already in the sixties and seventies several thousands of crossing guards were active. Regrettably there are no year reports of activities of crossing guards in the seventies or eighties. According to one estimate about 4,000 school crossing guards were present in the late nineties, mainly near primary schools, covering almost 30% of all primary schools (PROMISING, 2001).

3.3.5.2. Improvement of intersections

Hummel (1998) reviews the research done on the effectiveness of the improvement of crosswalks and intersections. Although Hummel reviewed the evaluation research done on crosswalks and intersections, he does not provide national estimates of the size of these measures in one particular year, or the extent to which these measures have evolved over the years. Other publications also do not provide these estimates. The most important


research reviewed by Hummel is the somewhat larger scale research by Dijkstra and Bos (1997). We will consider some of the main findings of this research. The Dijkstra Bos 1997 report presents accident data on 173 sites in several Dutch cities, before and after small scale measures were introduced. The measures concern several types of pedestrian street crossing facilities and 30 km/h area implementations. Emphasis is given to pedestrian safety effects. In the analysis distinction was made between location measures (43 sites) and area measures (130 sites). Possible location measures were: − narrowing; − narrowing & small bicycle paths; − narrowing & pedestrian waiting strips; − median island; − median island & lanes bending outwards; − median island & axis realignment; − median island & double axis realignment − median island & bus stop; − junction size reduction; − junction median island; − roundabout. Possible area measures were: − 30 km/h signs only; − road humps only; − road humps & narrowings; − road humps & axis realignments; − road humps & other measures; − road humps & narrowings & axis realignments; − road humps & narrowings & other measures; − road humps & axis realignments & other measures; − road humps & narrowings & axis realignments & other measures; − road humps & street closures & narrowings or axis realignments; − narrowings or other measures (without humps); − axis realignments & narrowings or other measures (without humps). With regard to the number of all injury accidents it was observed that apparently about 50% of the location measures has contributed positively to traffic safety, whereas the other 50% had a negative safety effect. Only the junction measures (junction size reduction, junction median island, roundabout) seem consistently to generate less accidents. The larger effects, however, were mostly based upon but few data and therefore were not very reliable. The overall result of the location measures was slightly positive for traffic safety. With respect to pedestrian safety the situation is worse. Except in case of a roundabout, the numbers of both pedestrian involved accidents and pedestrian victims have increased after the measures, albeit effect estimates were rather uncertain because of generally small databases. The overall result of the measures was negative for pedestrian safety.


Accident data showed that all area measures types were coupled with diminished numbers of all injury accidents. In one case, however, no effects could be determined. No comparison could be made because there were zero accidents in the before period, due to the short study period. It is striking that even the simple use of 30 km/h signs only, seemed to have a considerable positive effect on general safety. The total number of accidents decreased after the introduction of the 30 km/h signs, but the number of pedestrian accidents increased! In conclusion, half of the measures had a positive effect on pedestrian safety. In the other half of the cases, pedestrian safety became worse. Nevertheless, the overall safety effect of the area measures was positive for pedestrians, because the positive effects (decrease of accidents) proved to be larger than the negative effects (increase of accidents).

3.3.5.3. Enforcement of the prohibition of drink-driving

Based on the yearly SWOV roadside surveys of drinking-and-driving it was possible to estimate the effects of several legal, enforcement and publicity measures in the period 1980-2000 (Mathijssen, 1991, 1998a, 2001; AVV, 2001). In 1984-1986 several important measures were taken to decrease drinking-and-driving. In 1984 a start was made by replacing the old test tubes with new breath testing devices. This led to a smaller chance of false negative readings and smaller costs per test. In 1985 the enforcement operations shifted from selective to Random Breath Testing. This led to an increase in the general preventative effect of police testing operations. From 1986 onwards the Dutch Traffic Safety Association (VVN) and the Ministry of Public Health joined forces to produce yearly alcohol publicity campaigns, especially aimed at the young. These measures of 1984-1986 led to a considerable increase in the chance of being caught, and drinking and driving in weekend nights decreased further from 12% in 1983 to 8% in 1987. The decrease was most significant among young drivers, indicating that the publicity campaigns were successful.

0

2

4

6

8

10

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14

16

1973

1975

1977

1979

1981

1983

1985

1987

1989

1991

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Perc

enta

ge

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000N

umbe

r

percentage motorists with BAG > 0,5 g/lnumber of alcohol-related accidents

Figure 3.3. Development of percentage of drivers with BAG above the legal (first Y-axis) limit and of alcohol-related accidents (second Y-axis)


In the second half of the eighties another set of measures was taken. In 1987 evidential breath analysis was legally permitted so as to promote the efficiency of police surveillance. The following year, the Dutch drivers got acquainted with the ‘cash on the nail’ policy for minor offences (transaction immediately after the evidential breath analysis). This was done to remove workloads from the courts and thus to to improve the efficiency of sentencing. In 1988 special public transport for visitors of discos and bars etc., the so-called disco buses, were introduced. An extension of the ‘cash on the nail’ policy of the Ministry of Justice occurred in 1991. Now, also for serious offences, a completed summons for the court case was presented to the offender, immediately after the evidential breath test, usually accompanied by a transaction proposal. The introduction of breath analysis and ‘cash on the nail’ led to a further increase of random police testing and, in its turn, to an increase in the chance of getting caught. The percentage of drinking drivers during weekend nights decreased further from six in 1990 to below four (3.9%) in 1991. The availability of alternative transport has probably also had a positive effect, but its extent is unknown. From the early seventies to 2000, the level of drinking-and-driving in the weekend nights decreased from 15 to 4.5 percent. This development in percentage of drivers with a BAC > 0.5 g/l is shown in Figure 3.3. Figure 3.3 shows that a large drop in alcohol-related crashes occurred in the mid-eighties.

1 Pedestrian-car KSI victims no alcohol

2 Pedestrian-car KSI victims alcohol

3 Apply % change no alcohol victims to alcohol victims

4 Estimated change through other influences

5 Estimated added change alcohol policy

1981-1983 1.484 158 158

1984-1990 1.085 (-27%) 113 (-28%) 116 -42 -3

1991-2000 657 (-39%) 60 (-47%) 68 -45 -8

2001-2003 468 (-29%) 37 (-38%) 43 -17 -6

Table 3.5. Estimated reduction in number of pedestrian-car KSI victims as direct result of alcohol policy.

Table 3.5 presents the calculation of the number of pedestrian-car KSI victims that may have been prevented by the new alcohol policies in the eighties and nineties. As shown in the table, the calculation comprises five steps: 1. For four different periods, the choice of which was based on the above

description of alcohol policy through the years, the number of pedestrian-car KSI victims from crashes in which alcohol did not play a part was collected and the percentual change from period to period was computed.

2. For the same periods, we collected the number of pedestrian-car KSI victims from crashes in which alcohol use was involved and computed the percentual change from period to period.


3. From period to period, the percentual change of the number of pedestrian-car KSI victims from crashes in which alcohol did not play a part was applied to the number of pedestrian-car KSI victims from crashes in which alcohol use was involved.

4. On the basis of the results of step 2 and step 3, we computed the estimated change of the number of pedestrian-car KSI victims from crashes in which alcohol use was involved resulting from other influences than alcohol policy.

5. On the basis of the results of step 2 and step 4, we computed the estimated change of the number of pedestrian-car KSI victims from crashes in which alcohol use was involved resulting from alcohol policy.

The calculation indicates that the new alcohol policies may have contributed to decrease in pedestrian-car KSI victims in 1984-2003.

3.3.5.4. Intensified police enforcement

The Bureau of Traffic Enforcement (BVOM), which is part of the Dutch Public Prosecutors Office, has launched a four-year programme for intensified police traffic enforcement in eight of the 25 Dutch police regions. By the end of 2001, all police regions participated in the programme. The enforcement programme has a region-wise approach and focuses on 5 spearheads: speeding on secondary roads, drink-driving, red light running, seat belt usage, and helmet use of moped riders. In every participating region, a project team of 25 policemen who will be deployed to do enforcement of targeted traffic behaviours only, is added to the general constabulary. For all spearhead behaviours, targets are set in terms of effort indicators (hours spent and number of fines issued) as well as in terms of effect indicators (reduction in percentage of violators).

3.3.5.5. The Sustainable Safe program 1997-2001

The Association of Local Authorities, the Association of Water Boards, the Inter-Provincial Consultation, and the Ministry of Transport, representing all tiers of government in the Netherlands, together signed an agreement on the so-called 'Start-up programme for Sustainable Safety' in December 1997 (Ministry of Transport, 1997). The agreement contained 24 measures and actions, which were implemented between 1998-2002. The Start-up programme contained three major measures that could have affected the occurrence of pedestrian-car crashes: − Extension of urban 30 km/h zones (Section 3.3.5.7); − Extension of roundabouts (Section 3.3.5.8); − Extension of rural 60 km/h zones (Section 3.3.5.9).

3.3.5.6. Expansion of 30 km/h zones

Prior to the launching of the Start-up programme, 30 km/h zones and homezones had been implemented in a large number of towns and villages throughout the Netherlands. In total some 8,500 kilometres of the approximately 55,000 kilometres of urban residential streets had been designed as homezone or 30 km/h zones (Schermers and van Vliet, 2001). The Start-up programme anticipated that 30 km/h zones would be expanded by an additional 12,000 kilometres by the end of 2002.


The urban access roads purely provide access to properties alongside these roads. That is to say: through-traffic taking a short cut should be prevented. Because these roads are open to all traffic and permit all movements, speeds should be low in order to be safe. All access roads are designated as 30 km/h roads. Following the launch of the Start-up programme, local authorities submitted applications for the national subsidies and it became apparent that the budgeted funds would be inadequate to cover the demand. Furthermore, many local authorities realised that an amount of less than €10,000 per kilometre (50% of which is for the account of the road authority) would be totally inadequate for re-engineering certain road types now designated for 30 km/h use. Certain roads had generous geometric standards applicable to higher order roads but these now had to be downgraded and their layout adapted to conform to standards set for 30 km/h zones. To achieve this within the design principles set in the standards would require substantial investments. In the short term it was realised that funds for this could not be made available and therefore the concept of 'low-cost' implementation was introduced. Depending on the budget available to the road authority, it may implement traffic calming measures on the basis of the existing guidelines for 30 km/h zones (optimal spacing of measures) or implement on the basis of guidelines for a 'low-cost' approach. The 'low-cost' approach essentially allows for the phased introduction and realisation of roads in newly designated 30 km/h zones. In this way, the area can be gradually transformed to the desired end-state. In the short term, the posted 30 km/h speed limit is partially supported by traffic calming devices, whilst the transformation of the area is made clearly evident to the user. Over time, the density of measures can be increased. At the beginning of 2003 it was estimated (AVV, 2004) that some 30,000 kilometres of 30 km/h roads had been implemented, almost 10,000 kilometres above the agreement made in the Start-up programme. Without any doubt, this was a very successful result. In order to learn the effects of this expansion of 30 km/h zones an evaluation study was carried out (DHV, 2004). The zones were selected on the basis of size, period of completion, style of the suburb, access to the area, willingness of the local authority to assist with measurements (traffic volumes, speeds, and law enforcement), and road safety. The areas were selected to be nationally representative and geographically distributed over the entire country. A number of the selected areas had already implemented traffic calming measures prior to designation the area as a 30 km/h zone. Some of the older suburbs (from the fifties or sixties) with grid layouts had an average of 15 speed humps, plateaus, or exit constructions, whereas two-thirds of the suburbs of the seventies and eighties which have a ring structure had an average of 21 measures per suburb. The common residential streets show a rate of 0,324 fatalities and in-patients per million vehicle kilometre. For the length of 30 km/h zones, which


was installed during the Start-up Programme, this results in an expected number of fatalities and in-patients of 948 in 2002. However, only 294 victims were registered. So the estimated effect of the zone 30 over 1997-2002 amounts to 948 - 294 = 654 casualties. Based on the ratio between the number of pedestrian-car KSI casualties inside urban area in 1997-2002 and the number of KSI casualties of all vehicle types inside urban area in the same period, the estimated number of prevented KSI victims (-654) was converted in an estimated number of prevented pedestrian-car KSI victims in the period 1997-2002 (-48). The steps in this re-calculation are given in Table 3.6. Estimation saved pedestrian-car KSI victims by 30 km/h zones..

Calculation saved pedestrian-car KSI victims by 30 km/h zones

1 All vehicle types KSI victims

2 Pedestrian-car KSI victims

Estimated reduction 1997-2002 -654 -48

Correction factor 0,074

Table 3.6. Estimation saved pedestrian-car KSI victims by 30 km/h zones.

We used the above-mentioned two-year only data on the total length of 30 km/h roads and those on the length of 50 km/h roads in combination with the ratios between slightly injured, seriously injured and killed victims on 30 km/h roads and on 50 km/h roads to make an eyeball estimate of the fraction of 30 km/h roads over 1976-2004. The result is shown in Figure 3.4, in which the bold interrupted line represents the estimate. According to this estimate, the fraction of 30 km/h roads starts to increase from 1985 and from 2000 on this increase is accelerated.

0

0,02

0,04

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1975 1980 1985 1990 1995 2000 2005

ratio

vic

tims

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: 50

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/h

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frac

tion

30 k

m/h

in u

rban

are

a

Killed Seriously injured Slightly injuredFraction 30 km/h in urban area Estimated fraction

Figure 3.4. Estimation of fraction 30 km/h roads


3.3.5.7. Roundabouts

The process of changing four-arm and three-arm junctions into single-lane roundabouts started in the eighties, long before the Start-up Programme. The evaluation of the safety effects revealed that such conversions yield a reduction of 73% in the number of casualties (corrected for the crash and casualty trend on junctions in urban areas). For cyclists and mopedists the number of casualties decreased by 62% (van Minnen, 1990). A remaining issue at roundabouts concerns priority for cyclists on separate cycle paths. Currently there is no mandatory requirement for this. This lack of uniformity is currently a topic of discussion amongst local, provincial, and national road authorities. The crash rate at roundabouts with the rule 'priority to cyclists' is higher than at roundabouts with the rule 'priority to drivers' (0,11 versus 0,02 crashes with in-patients per roundabout per year) (Dijkstra, 2004). Wegman et al. (2005) provide an estimate of the number of fatalities and in-patients prevented by the construction of roundabouts in the period 1997-2002. During the Start-up Programme about 1,000 roundabouts were built. The mean number of fatalities and in-patients is 0,44 per year on a common junction and 0,08 per year on a roundabout. The difference between these accident rates was estimated to be the effect of the roundabout: 0,36 per roundabout per year. The preventive effect of 1,000 roundabouts in 1997-2002 was 360 fatalities and in-patients. As in Section 3.3.5.6 we determined the ratio between pedestrian-car KSI casualties and all KSI casualties for a re-calculation. The victim numbers were restricted to intersections of roads with maximum speed of 30, 50, 60, or 80 km/h and to the period 1997-2002. With this ratio we estimated the prevented number of pedestrian-car KSI casualties as a results of roundabouts in 1997-2002 to be eleven. The steps in the re-calculation are given in Table 3.7.

Calculation saved pedestrian-car KSI victims by roundabouts

1 All vehicle types KSI victims

2 Pedestrian-car KSI victims

Estimated reduction 1997-2002 -360 -11

Correction factor 0,031

Table 3.7. Estimation saved pedestrian-car KSI victims by roundabouts.

3.3.5.8. Introduction of 60 km/h zones

In the Start-up programme it was agreed upon to implement at least 3,000 kilometres of rural roads in 60 km/h zones over the period 1998 – 2002. These are minor rural roads with an access function only. Roads designated as potential 60 km/h roads will (at least in the short term) operate as 80 km/h roads. Because of the higher speed differential between


road users, road authorities must pay specific attention to intersections with cycle-moped paths to ensure the safety of the more vulnerable road user. In general, speed limits are posted as a logical result of the road scene as perceived by the road user. For 60 km/h roads however, new conditions were introduced in regulations and standards and these are : 1. The road primarily provides access to (private and public) property. 2. To prevent high volumes and proportions of through-traffic, roads and

their environment are adapted (calmed) accordingly. 3. In the light of speed reduction and raising awareness extra attention is

paid to (potential) unsafe locations. 4. Roads with transition zones from one speed limit to another are marked

to indicate the higher or lower limit. 5. If the transition to a higher speed limit occurs within 20 metres of an

intersection, that intersection will be priority controlled or an exit construction will be introduced.

A new design element of these 60 km/h zones was the introduction of a motor vehicle lane in the middle of the carriageway. The marking consists of a broken line on both sides of the motor vehicle lane. The rest of the width, consisting of two (edge) strips - sometimes covered in red asphalt - is reserved for cyclists. The aim is to give motorized traffic and cyclists their 'own' space, although not physically separated. The results of the Start-up programme (AVV, 2004) indicated that at the beginning of 2003 some 12,500 kilometres of 60km/h road had been realized. The total length exceeds the original target of 3,000 kilometres by 9,500 kilometres. The majority of these roads were re-designed and re-engineered on the basis of the so-called 'low-cost' approach. Again, this was a very successful intervention. The Association of Water Boards initiated a crash evaluation (before-and-after study) of twenty 60 km/h zones comprising some 800 km of rural access roads (Beenker et al., 2004). These roads were changed by introducing specific measures: raised junctions, road sections with bicycle lanes and speed humps. The crash trend in these twenty zones was compared to the crash trend in control areas (with a total of 2,300 km of rural access roads). Prior to the infrastructure changes (also referred to as the before period), the roads in the control and roads in the test areas had similar geometric design characteristics. The injury crash rate (number of injury crashes per km) in 60 km/h zones decreased by 18% compared to the control areas. The total number of casualties (both fatalities and injured) was 25% less after the 60 km/h zones were installed. The absolute number of crashes with injury on junctions within the 60 km/h zones decreased by 50% compared to the control areas. Most of the effect can be contributed to the (raised) junctions; the effect of the measures on road sections is small (and not statistically significant). Examining the casualty rate (number of casualties per 1000 km road length) on 60 km/h roads, then the number of fatalities decreased from 8 in 1998 to 2,6 fatalities per 1000 km road length in 2003. Simultaneously the number of in-patients decreased from 40,8 in 1998 to 27,6 per 1000 km in 2003.


3.3.6. Other developments

This section pays attention to other developments that may have affected pedestrian-car risk, such as car-related developments (Section 3.3.6.1), weather (Section 3.3.6.2), large-scale change in travel patterns (Section 3.3.6.3), economic activity (Section 3.3.6.4), developments in urban and area planning (Section 3.3.6.5), and social-cultural developments (Section 3.3.6.6).

3.3.6.1. Car-related developments

In the Netherlands, about 30% of passenger cars drive with daytime running lights (DTR). According to a report by Elvik et al. (2003), it is likely that DTR has a favourable effect on accidents involving pedestrians. However, effect estimates differ between studies, no specific studies for the Dutch situation have been done, and there are no precise data on the development of cars with DTR over 1980-2006. It can be assumed that new developments in the area of passive car safety may have gradually improved the safety of cars for pedestrians. One of these developments may be anti-lock brakes (ABS). These are designed to improve the manoeuvrability of a vehicle when braking. They are believed to be beneficial in preventing accidents through improved braking and thus avoiding an accident. The safety impact of ABS is not quite clear. The site of the Monash University Accident Research Centre describes some studies about the safety effects of ABS. “Kullgren, Lie and Tingvall (1994) performed a real-life study examining accidents that to some extent involved the braking system. Cars with and without ABS, but otherwise identical, that had been involved in accidents, were compared on a number of crash characteristics. It was found that overall, cars with ABS were significantly less likely to be involved in a rear impact collision. On dry roads, there was no difference between cars with and without ABS in terms of accident incidence. However, on icy surfaces, cars with ABS were less likely to hit another car, but were more likely to be hit than a car without ABS. No statistically significant difference was found for injury risk between the two populations. In addition, no statistically significant difference was found for the severity of crashes caused by cars with and without ABS. Thus, overall, the effect of ABS on improving road safety was found to be significant only on icy roads and primarily in relation to rear impact collisions. Other studies have shown similar results, also relating to wet surfaces. Evans and Gerrish (1995) also found that overall, ABS reduces the risk of having a two-vehicle crash. When driving on dry roads there is little difference in crash risk between a car with ABS and one without. When driving on wet roads, however, a car with ABS is less likely to crash into a vehicle in front of it, but more likely to be hit by a car travelling behind, than a car without ABS. Evans and Gerrish (1995) suggest that drivers with ABS take larger risks when driving. These risks include shorter headways and higher speeds.” (http://www.monash.edu.au/muarc/about/carpolicy.html) Since 1997 Euro NCAP provides motoring consumers with a realistic and independent assessment of the safety performance of some of the most popular cars sold in Europe. It can be expected that Euro NCAP will


encourage significant safety improvements to new car design. It can be assumed that the increase in cars with improved passive safety potential for pedestrian-car crashes has been very gradual since the mid-nineties. For the moment data are lacking of the extent to which safer car fronts or other passive safety devices have increased in the Netherlands in the period 1996-2006. All in all, several car-related developments (DTR, ABS, safer car fronts) may have contributed to a decrease in pedestrian-car risk. The research evidence on both DTR and ABS is fully conclusive on this, mentioning different estimates (for DTR) and possible negative side-effects (for ABS). Presumably, these developments have been very gradual and data on the annual increase of these car-related developments in the Netherlands are lacking. Given the current knowledge about these developments, we see no possibility of including one or more of these developments in a time series model of pedestrian-car risk.

3.3.6.2. Weather

The number of road traffic victims in a particular year is greater or less than was to be expected on the basis of normal statistical fluctuations. Such deviations are sometimes partly attributed to the influence of weather. In the Netherlands, Bos (2001) examined whether, and to what extent, extreme weather conditions were accompanied by important deviations in the numbers of victims, exposure, and (thus derived) victim rate. The data analysed were from two-month periods between 1995 and 1998. The study clearly shows that there is a measurable relation between, on the one hand the weather and, on the other hand the number of victims, the victim rate, and the exposure. Bos found that precipitation and temperature were the most important factors, when examining the relationship of weather and road safety in both summer and winter. A great amount of precipitation, as either rain or snow, generally accompanied a higher victim rate for all modes of transport, and a smaller exposure, especially for cyclists. During a mild winter and a warm summer, more kilometres were cycled. Also, in a mild winter with little precipitation, more kilometres were travelled by car. A warm summer is accompanied by a higher victim rate for car occupants. According to the data, the influence of sunshine and wind is slight. During the four years studied, the greatest weather influences occurred in 1995 and 1998. In 1995 there were more road deaths as a result of a) the milder weather of and greater precipitation in the January-February period, b) the hot summer in July-August, and c) the severe winter with a lot of snow and fog in November-December. In 1998, there were less road deaths as a result of the relatively wet and cool summer weather in July-August. After correcting for weather influences, the researcher estimated that the number of road deaths in 1995 should be lowered by 65, and raised by 44 for 1998. The maximum influence of the weather is thus considerably less than 100 road deaths per year, at least in the years studied.


0

5

10

15

20

25

1970 1975 1980 1985 1990 1995 2000 2005

aver

age

daily

tem

pera

ture

(deg

rees

Cel

sius

)

Jan-MarApr-JunJul-SepOct-Dec

Figure 3.5. Average daily maximum temperature per quarter, 1971-2004.

According to Bos, a cold dry winter leads to less traffic casualties. Figure 3.5 shows the average daily temperature for different seasons between 1970 and 2004. We can see in this figure that the winters of 1979, 1985, 1986, 1987 and 1996 were extremely cold.

1979 1985 1986 1987 1996 Average 1971-2004

Average maximum temperature (°C)

2.9 3.7 4.4 3.7 4.3 7.1

Average minimum temperature (°C)

-2.7 -3.3 -2.3 -2.7 -1.7 0.7

No. of days with day frost

25 22 18 15 17 6

No. of days with night frost

61 61 53 61 64 35

No. of hours with precipitation

236 127 167 191 103 186

Amount of precipitation (mm) 212 86 158 145 81 184

Characterization winter

Very cold and wet

Cold and dry

Cold and some dryer than normal

Cold and some dryer than normal

Cold and dry

Table 3.8. Weather data for the winters (January-March) of 1979, 1985, 1986, 1987, 1996 and the average values over 1971-2004.

The severity of the winters of 1979, 1985-1987, and 1996 is further illustrated by the data in Table 3.8. The table shows the average maximum temperature, the average minimum temperature, the number of days with day frost, the number of days with night frost, the number of hours with precipitation, and the amount of precipitation for the winter months of the five above-mentioned years. The table also gives the average values over 1971-


2004. By combining the information on the different variables the years are characterised as follows: 1979 was very cold and wet, 1985 and 1996 were cold and dry, 1986 and 1987 were cold and some dryer than normal.

3.3.6.3. Large change in travel patterns

Changes in risk can occur as a result of changing exposure of specific risk groups which may be viewed as an autonomous development. As we shall see further on, the term autonomous is somewhat arbitrary since specific measures that are not road safety measures may have contributed to this development. There is evidence that in the early nineties a large shift in mobility occurred amongst young persons which also may have affected the occurrence of pedestrian-car crashes. First, we will consider the changes of the modal split between 1990 and 1991 for two age groups. Second, we will more specifically look at changes in car ownership, car use, and use of public transport over 1985-1997 for people between 18 and 24 years old. Third, we will shortly discuss the possible consequences of these changes for pedestrian-car KSI casualty risk.

Ages 18-24

1990 1991 change (abs.) change (%)

Pedestrian 321 390 69 21

Bicycle 1723 1732 8 0

Moped 373 348 -25 -7

Motorcycle 498 475 -22 -4

Car 13059 11317 -1742 -13

Bus 1537 2230 693 45

Tram/metro 401 508 106 26

Train 2499 4311 1813 73

Other 201 275 74 37

Unknown 0 2 2

Total 20613 21589 976 5

Table 3.9. Kilometres travelled by people of age 18-24 by travel mode1 in 1990 and 1991 and the absolute and percentual change. Source: National Travel Survey (CBS).

Modal split 1990-1991 for 18-24 and 30-39 aged In 1991 young people from 18 to 24 years old travelled 7% less moped kilometres and 13% less car kilometres than in 1990. At the same time their bus, tram, and metro kilometres grew with 45%, 26%, en 73% respectively. See Table 3.9, which shows the young people's kilometres by travel mode for 1990 and 1991 and the absolute and percentual change. Also the pedestrian kilometres increased (21%), which can be explained by the fact

1 Note that the sample margin of the mobility figures by age group and by travel mode are the larger the smaller the sample is. For example, the margin is 40% for moped kilometres by 30-34 aged and 65% for moped kilometres by 35-39 aged (SWOV, 2003).


that an increase of the use of public transport induces an increase of the number of short-distance pedestrian trips, namely to and from train stations and tram, metro, and bus stops. We compared the above mobility changes for 18-24 aged to the changes for an age group with (almost) no students, the 30-39 years old people. Table 3.10 shows that the figures for this age group are clearly different: moped kilometres of this group increase strongly (115%), car kilometres increase slightly (3%), bus kilometres and their tram or metro kilometres increase (by 8% and 29% respectively) and their train kilometres decrease (17%).

Ages 30-39

1990 1991 change (abs.) Change (%)

Pedestrian 545 585 40 7

Bicycle 1978 1994 16 1

Moped 58 124 66 115

Motorcycle 292 233 -59 -20

Car 25639 26289 650 3

Bus 554 597 43 8

Tram/metro 160 207 47 29

Train 2082 1738 -344 -17

Other 296 266 -29 -10

Unknown 2 8 6 331

Total 31605 32041 436 1

Table 3.10. Kilometres travelled by people of age 30-39 by travel mode1 in 1990 and 1991 and the absolute and percentual change. Source: National Travel Survey (CBS).

So, when we compare the mobility changes over 1990-1991 for the 18-24 aged with those for the 30-39 aged, we see a remarkable sharp increase of public transport kilometres for the 18-24 aged which goes hand in hand with a relatively strong decrease of car kilometres and strong increase of pedestrian kilometres. This is a clear indication of a strong mobility effect in 1991. Car ownership, car use, and use of public transport 1985-1997 for 18-24 aged During the 1990-1992 period, a sharp reduction occurred in the number of kilometres driven by the 18-24 years old car drivers, both students and non-students alike (Twisk, 2000). Figure 3.6 presents the mileage of non-students versus mileage of students for public transport use and car driving. The figure shows a large increase in public transport use by students following the introduction of the Free Public Transport Pass (FPTP) for students in January 1991. In January 1991, the FPTP was introduced, allowing students with an entitlement to a student grant to use public transport in exchange for a cut in their monthly grant. About 41% of the population of the 18-24 year olds were students. As a result of the FPTP students used public transport more often. At the same time car mileage decreased for students, but an even larger decrease was visible for the non-


students, starting a few years earlier. Hence, whereas the introduction of the FPTP may have had some impact on the car use of students, it certainly was not the main explanation.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

year

mile

age

student - car driver student - PBnon-student - car driver non student PB

Figure 3.6. Mileage of non-students and students for public transport use ('PB') and car driving.

An explanation for the reduced car mobility of non-student young male drivers may be found in the economic situation. The economic situation can affect car ownership and driver licence possession, and therefore indirectly, car mobility. The late eighties and early nineties were characterised by a stagnation in economic growth, international instability (the Gulf War) and high oil prices. It appeared that in the period between 1985 and 1997, the absolute number of young men with a driving licence declined with 22 percent points; the number of young women with a driving licence decreased with 11 percent points. A similar pattern was observed in car ownership. In 1985, approximately 40 per cent of the young men owned a car; in 1997 this had almost halved to 24 per cent. For young women the decrease was smaller: from approximately 19% in 1985 to circa 15% in 1997. Table 3.11 provides an overview of the developments of various factors affecting the accident involvement of, in particular, young male drivers. Table 3.11 shows the relative (indexed) increase or decrease in percentage of the average of the years 1995-1997, with the average of the years 1985-1987 set at 100 percent for young men and women separately. It can be concluded that a combination of factors may explain the reduction in accident involvement of young car drivers in the period between 1985 and 1997. The number of 18-24 year old people in the Dutch population decreased for men and women by the same amount. The accident risk decreased as well, for young women more so than for young men.


Index 1995-1997 (1985-1987 = 100)

Male Female

Absolute number of 18-24 year olds in population 82 83

Absolute number of licence holders 78 89

Car ownership 53 74

Car mileage of the total group 18-24 62 92

Car mileage per licence holder 79 104

Car mileage per car owner 96 110

Absolute number of drivers involved in serious injury crashes

55 64

Accident risk 89 70

Table 3.11. Summary of indexed developments 1987-1997 (Source: Twisk, 2000).

It would therefore be logical to expect that the number of young female car drivers involved in accidents had declined more than that of young male car drivers. However, statistics of accident involvement show the contrary (see Table 3.11). This situation can be explained by a substantial reduction in exposure of young men in particular. The exposure of young men declined mainly because fewer of them hold a driving licence and own a car. The exposure of young men declined mainly because fewer of them hold a driving licence and own a car. The decrease is also visible for young women, but to a much smaller extent. In addition, female driver licence holders and car owners increased their car mobility, whereas this was not the case for the male licence holders and car owners. Consequences of the mobility changes for pedestrian-car KSI casualty risk A strong decline of car mobility of 18-24 years old people from 1990 to 1991 can most probably be related to a decline of pedestrian-car KSI casualty risk over the same period. This expectation is based on Figure 2.36, which shows that the 18-24 aged is the age group which is most involved as a car driver in a pedestrian-car KSI casualty.

3.3.6.4. Economic activity

For a large part mobility is generated by economic imperatives. A large part of the national population spends about one to two hours driving from home to work and back. Transport of commercial goods is part and parcel of economic activity. Theoretically it can be assumed that the state of the economy has strong impact on road safety. One important indicator of the state economy is unemployment. We may assume that a high unemployment rate can have the following consequences for traffic safety: − in general less drivers on the road; − less drivers on the road during peak hours when traffic density is high

and possible conflicts are numerous; − less drivers on the road that drive being sleepy (early morning) or tired

after a hard day work.


Based on one or more of these assumptions it can be expected that road safety increases when unemployment rises. In the Netherlands no research has been done into this relationship. Several Australian studies have shown that it is possible to construct models which link downturns in economic activity (especially unemployment) with reductions in road fatalities (Hakim et al. 1991; Haque 1991; Pettit 1992). These models also took into account changes in road safety measures and other factors where relevant. Thoresen et al. (1992) developed a model which explained about 50% of the variations in monthly Victorian road fatalities during the period 1985 to 1990. The model estimated that in 1990 a reduction of 53 fatalities could be associated with the increase in unemployment. This represents a reduction of 6,8% of the 1989 road toll or about one quarter of the total drop in 1990. For 1991 the model indicated that perhaps one third of the reduction from 1989 could be associated with unemployment. Newstead et al. (1998) estimated the contributions to road safety of the following interventions and developments: intensified enforcement on drink-driving and speeding, economic activity, alcohol sales, improvements to the road system through treatment of accident black spots. The percentage change in road trauma levels, as measured by serious casualty crash numbers, due to each factor was estimated for each year over the period 1990-1996. The researchers computed models linking variations in serious casualty crashes to various factors using monthly crash data from the years 1983 to 1996. Subsequently, the contributions of random breath testing (RBT), speed camera tickets issued, levels of road safety television publicity, unemployment rates and alcohol sales to the reduction in the number of serious casualty crashes were estimated for the period 1990-1996. A method of separately estimating the effect of accident black spot treatments and separating this from the trend was described and applied. The major contributors and the apparent percentage reduction in serious casualty crashes due to each measure were estimated as: − speed camera operations: 10-11% each year; − 'speeding' and 'concentration' television advertising: 5-7% each year; − drink-driving program: 9-10% each year; − reduced alcohol sales: 3% in 1990; 6% in 1991; 7% in 1992; 9% in 1993;

8% in 1994; 9% in 1995; 10% in 1996; − reduced economic activity measured by unemployment rates: 2% in

1990; 12% in 1991; 15% in 1992; 16% in 1993; 14% in 1994; 1 0% in 1995; 10% in 1996;

− Accident Black Spot treatments: 1.6% in 1990; 2.5% in 1991; 3.2% in 1992; 5.3% in 1993; 6.2% in 1994; 6.2% in 1995; 5.6% in 1996.

Figure 3.7 presents the percent change in volume growth of the Dutch gross national product. It can be seen that periods of economic recession began in 1974, 1990, and 2000. Periods of strong economic recovery were 1976, 1983, 1994, and 2004. On the basis of the above research results, the following general hypothesis can be formulated for the total period 1976-2004: H76-04.4.B: Periods of (strongly lowered) economic activity (1981, 1990

and 2000) are associated with relatively low pedestrian-car KSI casualty risk.


-2

-1

0

1

2

3

4

5

6

7

1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005

Categoriëen

%ch

ange

ann

ual d

omes

tic e

cono

mic

pro

duct

Annual change bruto domestic product (BBP) in percentage

Figure 3.7. The annual percent change in volume growth of the Dutch gross national product (BBP). Source: CBS

3.3.6.5. Old and new developments in urban and area planning

Schreuders and Schoon (2005) have conducted a study into the consequences of area and urban planning developments for traffic and road safety in the Netherlands. In general, over the years, the Netherlands has known a development of increasing urbanization. In the highly populated large city agglomerations the dependence on the car has been lessened due to large supply of other transport facilities, notably different forms of public transport. It is to be expected that some dispersed form of urbanization will also occur in rural areas. This will likely lead to more wide distance trips and increased dependence on the car. On both the regional as the local level, there is tendency towards increase of scale, e.g. living areas being built at the edges of cities, large outlet stores and mega cinemas being built near industrial zones. Based on both ongoing and future developments Schreuders and Schoon identify three major trend scenario's (see Table 3.12): 1. Continuation of the previous area and urban planning policies; 2. Present area planning according to the 4th Memorandum Extra

Concerning Area Planning (in Dutch: '4e Nota Extra over de Ruimtelijke Ordening');

3. Intended area planning according to the Memorandum Space (in Dutch: 'Nota Ruimte').

Table 3.12 presents the expected traffic and road safety developments of these trend scenarios.


Area planning factors Indication of effects on traffic, transport and traffic safety 1

Number of trips

Car use Use of slow travel modes

Number of short trips

Use of public transport

Chance of conflict stituations between slow and motorized traffic

Crash risk (with safe infra-structuur)

Scenario 1. Continuation of the previous area and urban planning policies

Increasing urbanization + - ++ ++ ++ - -

Increasing mix of functions: combination of living, working and activities

++ -- ++ ++ ++ - -

Increase of scale ++ ++ -- -- -- ++ +

Further condensing of highly urbanized areas

-- -- ++ ++ ++ -- -

Scenario 2. Present policy: focus in area planning according to the 4th Memorandum Extra Concerning Area Planning (in Dutch: '4e Nota Extra over de Ruimtelijke Ordening'

Building conform the vision van 'compact city'

+ - + + ++ -- -

Stadsuitbreiding met aansluiting openbaar vervoer

+ - + + ++ -- -

Building "VINEX-uitleglocaties"

++ ++ - -- 0 + 0

Building "VINEX-inbreidingslocaties"

- - ++ ++ 0 - -

Scenario 3. Focus in area planning according to Memorandum Space (in Dutch: 'Nota Ruimte')

Growth focused on concentration at cities (clustered urbanization)

-- -- ++ ++ ++ -- -

Large scale house-buidling in rural areas

++ ++ -- -- -- ++ +

1 Note: + : increase 0 : neutral - : decrease

Table 3.12. Area use and planning trends according to trend scenario 1 and estimation of effects on traffic, transport and road safety (Source: Schreuders and Schoon.,2005).

3.3.6.6. Social-cultural developments

Schoon (2005) made an inventory of possible social and cultural development that can affect mobility and road safety in the future. The report identifies a number of developments that have already begun in the eighties or nineties and will further grow in impact in the near future. Some of the most likely occurring developments are the following: − The car density per house will grow and the dependence on car mobility

will grow as a result of increase of scale, combination of tasks, and participation of women taking part in the labour market.

− It has been predicted that in 2010 car driving will be the favourite means of transport for shopping rather than bicycling or walking. In the services industry (shops, companies) increases of scale occur that will be associated with larger distances between servicing areas and living areas. This will lead to a further decrease in both walking and cycling.


− There has been a growing trend of bringing children to schools and fetching them with car. This increases unsafety near school grounds for those children who walk to school.

− The increase in car density per house will result in parking problems. Badly parked cars on street corners will reduce sight and this may increase risk for children in residential areas, especially in older neighbourhoods.

− Children grow up more and more in a car-dominated culture. As a result of this opportunity to learn to move around in traffic independently and to estimate traffic risks becomes less.

− The number of special vehicles and walking aids for the elderly will increase.

− Specific categories of pedestrians, such as elderly or handicapped persons making use of a one- or two seat car with moped engine and moped registration (in Dutch known as 'brommobiel'), will grow in number in the future, and if they are untrained or not well-informed about priority rules they may engage in unsafe traffic behaviour, such as riding the 'brommobiel' with a speed nearing 15 km/hr, or not giving priority to other road users.

The growth and effects of these developments on road safety are however not estimated or quantified in the report.

3.4. Overview

Section 3.4.1 describes the main conclusions of this chapter. In Section 3.4.2 the results of this chapter are used to evaluate the hypotheses from Chapter 2 and to formulate new hypotheses.


The literature scan shows that pedestrian safety has not been researched very much in the Netherlands. The literature provides no direct estimates of effects of measures in the seventies or eighties on reduction of pedestrian-car crashes. The evaluation of the Sustainable Safe program also does not provide a direct estimate of the importance of this program for pedestrian safety. In the literature, we could only find scarce references to voluntary, self-organized road safety activities by informal road safety initiatives such as crossing guards, or local or national road safety action groups (e.g. "Stop de kindermoord"). Although between 20,000 and 30,000 crossing guards have been active in supervising child safety on important crossings along school routes, there were no year reports of the work and activities of the school crossing guards in the seventies or eighties. Some early studies into risk of pedestrians (Veilig Verkeer Nederland, 1982; Oei, 1984) show that risk for different age groups already declined in the early seventies. Since there was no large-scale program of measures, or for that matter any national safety measures at all, to improve pedestrian safety in the seventies, this decrease of risk has to do with collective awareness and learning and self-support mechanisms (e.g. voluntary crossing guards supervising school routes). The descriptive analyses suggest that pedestrian-car risk has declined, for all age groups and for males and females, in the period 1970-2003. The


literature scan provides several explanatory factors for this decline in the period 1980-2003. It is difficult to rate these factors in terms of importance for reducing pedestrian-car crashes. The order in which we list these factors is arbitrary. In general, presumably only effective in some scope since beginning or mid-eighties, infrastructure has become more safe due to continued efforts of black spot approach, and the gradual acceptance of new design principles for roads, road crossing and road structure in city areas. However, research cited in Hummel (1998) suggests that especially measures at specific locations did not improve safety for pedestrians at all. Only the overall effect of area measures was positive for pedestrians. Also, presumably becoming effective in eighties, the (then new) city planning developed city areas that had a clearer hierarchy of roads, necessitated less mingling of cars and pedestrians, and thus in general were more safe for pedestrians than the old neighbourhoods built in the fifties and sixties. Also, in the eighties, drinking and driving decreased, a large number of crossing guards continued to supervise safety on school routes, schools, and municipalities themselves, became increasingly aware of the safety of school routes (sometimes encouraged by road safety publicity and action groups) and undertook some protective action. For example, increasingly in the eighties parents started to bring children to schools with their cars, or walked with them to schools. The safety of (young) children presumably has increased because parents, schools and municipalities all have become increasingly aware of the risks of modern-day traffic, and have undertaken some protective action on the basis of this concern. The growth of this awareness has been stimulated by national and local publicity campaigns and actions by road safety organizations, one of which was primarily focused on children's safety, and of course, everyone could see for himself that the amount of traffic on the roads was increasing. While the young children were protected more and more by their parents, schools and traffic guards, the elderly pedestrians became in general more vital and active over the years, and in the late nineties started to use walking aids more frequently. Together with the general factor of safer infrastructure, this may account for their general decrease in risk

3.4.2. Hypotheses

The literature scan has not presented evidence or insight that contradict the hypotheses formulated in the previous chapter concerning the development of the total number of casualties and overall risk and the development of various subgroups. However, it has provided additional support of some of Chapter 2’s hypotheses (see Section 3.4.2.1). Furthermore, two new hypotheses could be formulated on the basis of the results of the literature scan (see Section 3.4.2.2).

3.4.2.1. Review of old hypotheses

Hypothesis H79&85.1.A states that the winter drop of pedestrian-car KSI casualties in 1979 and 1985 was caused by a temporarily seasonal effect, probably winter weather circumstances. In Section 3.3.6.2 we saw that the winters of 1979, 1985-1987, and 1996 were severe. Furthermore, in literature (Bos, 2001) we found that cold winters are related to less road casualties, which supports our hypothesis. The cold winter years 1986,


1987, and 1996 are not mentioned in the hypothesis. When we go back to Figure 2.24 we can see that the number of pedestrian-car KSI casualties was relatively low in the winters of 1986 and 1987 as well but not in 1996. On the basis of these results we extend the old hypothesis, thereby creating a new one, in the following way: H79-87.1.B: The number of pedestrian-car KSI casualties in 1979, 1985,

1986, and 1987 was relatively low because of the severe winter weather in those years.

Hypothesis H99-01.1.A relates the strong decrease of pedestrian-car KSI victims of 0-11 years old in 2000 and 2001 to the transformation of more and more 50 km/h roads into 30 km/h roads as part of the Sustainable Safe program, which started in 1997. Our literature-based estimation of the fraction of 30 km/h roads in urban area (see Figure 3.4) indeed shows an accelerating growth since 2000. Furthermore, we estimated a relevant, reducing effect of the replacement of 50 km/h roads by 30 km/h roads (see Table 3.6. Estimation saved pedestrian-car KSI victims by 30 km/h zones.) on the number of pedestrian-car KSI victims, of which many are 0-11 years old (about 40% according to Figure 2.32). We conclude that the findings from the literature scan with respect to the 30 km/h zones are a support of the hypothesis. Hypothesis H76-04.1.A states that because of changes in pattern of life, e.g. the greater participation of women in the labour market, pedestrian-car KSI risk decreased more slowly for females than for males. Table 3.3 and Table 3.4 in Section 3.3.3.3 shows that the participation of Dutch women in the labour market has indeed increased. About the effect of this development on female risk nothing was found in literature. However, we can reason out that because of the increased labour participation women started to walk more often. As our exposure measure is the number of female inhabitants, that increased walking leads to an increase or to less decrease of female pedestrian-car KSI risk. It can be expected that also the fact that women started to walk more during the busy hours with a lot of car traffic played a part. According to Hypothesis H76-04.3.A, the consequent application of new, safe city and road design principles in Flevoland has resulted in considerable lower pedestrian-car KSI risk than in the other provinces. From literature we found that Sustainable Safe measures as the construction of 30 km/h zones and roundabouts saved casualties (see Section 3.3.5.5), which supports this hypothesis. No information was found on Hypotheses H82-83.1.A and H76-04.2.A.


3.4.2.2. New hypotheses

The literature scan of this report and the earlier report of Blois, Goldenbeld and Bijleveld (2007) identifies two years (or periods) in which a combination of development or measures may have affected general road safety in a major way: − The year 1990 saw both the introduction of the Free Public Transport

Pass for students and the beginning of economic recession. Presumably, both developments would have had a reducing impact on road safety in general.

− The period 2000-2002 saw again economic recession and two measures (Sustainable Safe and intensified police enforcement) that started in 1997-1998 and that may have reached a peak value (full effective scope) somewhere in this period (Sustainable Safe perhaps in 2000-2001; Intensified Enforcement in 2001-2002).

We may hypothesise that pedestrian-car safety has increased more rapidly than normal in 1990-1991 and in 2000-2001, or: H90-91.1.B: Pedestrian-car KSI casualty risk has decreased more rapidly

than normal in 1990-1991 because of the introduction of the free public transport pass for students and the beginning of economic recession.

H00-02.1.B: Pedestrian-car KSI casualty risk has decreased more rapidly

than normal in 2000-2002 because of the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession.

In Section 3.3.6.4, we formulated the following hypothesis about the effect of the level of economic activity: H76-04.4.B: Periods of (strongly lowered) economic activity (1981, 1990

and 2000) are associated with trend breaks in pedestrian-car KSI risk.

Increased economic activity makes that people walk and drive more during the busy hours. As such, we expect that a good economy can be related to more pedestrian-car KSI crashes. As our exposure measure is the population size, this effect can be found in risk, not in exposure. In our approach risk is the probability that an inhabitant becomes a pedestrian-car KSI victim. This risk increases when at constant population size people are going to walk more and therefore cross roads more often.


4. Trends in crash risk 1978-2003

4.1. Outline

This part of the report presents the results of structural time series models of the development of KSI casualties, exposure, and risk of pedestrian-car KSI crashes. In Section 4.2, we will describe the modelling approach and give the mathematical equations and assumptions of the model. Section 4.3 presents the main outcomes of the four models. Section 4.4 contains a review of the hypotheses from the previous chapters and an overview of the main results of this chapter.

4.2. Method

In this section, we will elucidate on the method used. First, Section 4.2.1 presents the general formulation of the state space models used. Then, Section 4.2.2 describes the procedure of model estimation. Finally, in Section 4.2.3 we describe how we used seasonal (quarterly) data. As much as possible we try to explain our method in a way that will be clear to a readership that may be less well versed in structural time series and matrix algebra. This may mean that we sometimes use examples, metaphors, or terminology that, from a technical point of view, does not quite cover the mathematical or statistical intricacies of the subject. For readers that are interested in the exact mathematical background of the models in this report, we refer to other (SWOV-) publications (Bijleveld, 1999; Bijleveld and Commandeur, 2004; Commandeur and Koopman, 2007; Durbin and Koopman, 2001; Gould, Bijleveld and Commandeur, 2004; Harvey, 1989).

4.2.1. Model description

In fact, a model is a collection of assumptions about how reality was, is, and will be. A model is always a simplified representation of (a part of) reality. For the analysis of crash risk, the following theoretical model is defined. The observed number of pedestrian-car KSI casualties is assumed to be the product of two unobserved (latent) variables: pedestrian-car KSI casualty risk and pedestrian-car exposure. Furthermore, we admit the existence of a measurement error in the observation of the number of KSI casualties. In formula, the number of KSI casualties is modelled as in equation (4.1). Pedestrian-car KSI casualties = Pedestrian-car KSI casualty risk × Pedestrian-Car exposure × error1 (4.1)


Pedestrian-car KSI casualty risk is decomposed into a trend and a seasonal component: Ped.-car KSI casualty risk = Trend (Ped.-car KSI casualty risk) × Seasonal (Ped.-car KSI casualty risk)

(4.2) As stated earlier in this report (Sections 1.3.4 and 2.2.4) exposure is an unobserved, latent variable which is expressed in some quantifiable variable. In our model we used the population size as approximation of pedestrian-car exposure: Population size = Pedestrian-car exposure × error2 (4.3) Pedestrian-car exposure was not decomposed into trend and seasonal. We assumed pedestrian-car exposure as measured by population size was not or hardly influenced by season. See also Section 4.2.3, which is about the use of seasonal data. The theoretical model was translated into state space formulation, containing so-called measurement equations and state equations. The measurement equations relate to all manifest, observed variables in the model. The state equations relate to all latent, unobserved model variables. The measurement equations resemble the equations of the log-transformed theoretical model above: Pedestrian-car KSI casualties t = level (Pedestrian-car KSI casualty risk) t + seasonal (Ped.-car KSI casualty risk)t

+ level (Pedestrian-car exposure) t + error1 t (4.4) Population size t = level (Pedestrian-car exposure) t + error2 t (4.5) Equations (4.4) and (4.5) are valid for time step t = 1 to N, where N is the number of time steps. There are six state equations: for both unobserved variables, i.e. pedestrian-car KSI casualty risk and pedestrian-car exposure, we formulated one equation for the development of the level in time, one for the development of the slope in time, and one for the development of the seasonal: level t+1 = level t + slope t + error3 t (4.6) slope t+1 = slope t + error4 t (4.7) seasonal t+3 = - seasonal t+2 - seasonal t+1 - seasonal t + error5 t (4.8) Equations (4.6) and (4.7) are valid for time step t = 1 to N-1 and equation (4.8) for t = 1 to N-3. The initial values level 1 , seasonal 1 , seasonal 2 , and seasonal 3 are treated as fixed and unknown coefficients. The level represents the 'height' of the state, whereas the slope and the error respectively represent the systematic and random difference in height


between two consecutive time steps. The level in a time step is assumed to be equal to the level in the previous time step increased by the slope and the seasonal of the previous time step and a random error. The slope in a time step is, besides some random error, equal to the slope in the previous time step. The seasonal describes the pattern that risk in the specific seasons is sort of systematically lower or higher than the annual average risk. As such, the product of four consecutive values of the seasonal in equation (4.2) must be equal to one, whereas the sum of four consecutive values of the seasonal in equation (4.4) is equal to zero. Of course, another condition to the seasonal is that its value for a season in a specific year is equal to its value for the same season in the previous or the next year, except for some random disturbance, which allows the seasonal to change over time. To meet these conditions we chose to determine the seasonal as in equation (4.8), but other equations are possible as well. Equations (4.6) to (4.8) show that the state in a time step as described by level, slope and seasonal is a direct function of the states in the previous time steps. The level and slope components together are called the ‘trend’ or the ‘trend component’. The seasonal component can also just be called ‘seasonal’. The error is also called ‘residual’ or ‘irregular component’. So, that leaves us the following components: − trend (level + slope); − seasonal; − error (or residual or irregular). Explanatory variables, e.g. trend breaks, can be added to the measurement equations or to the state equations. If a trend break is added to the measurement equation it represents a systematic change in the measurement of the observed variable. If it is included in the state equations, it represents a systematic change of level, slope, or seasonal of the latent, unobserved variable.

4.2.2. Model estimation

To prevent ending at sub-optimal parameter estimations, the log-likelihood optimisation of each model was started from a number of uniformly distributed starting points, i.e. combinations of initial parameter values (see Section 4.2.1). The log-likelihood value is indicative of the overall fit of the model; in general, the higher the log-likelihood value, the better the fit of the model. The log-likelihood value was used to test the significance of trend breaks. For each trend break variable we computed how much the log-likelihood value would decrease as a consequence of removing the variable. The more the log-likelihood value had decreased, the more important is the contribution of the variable to the model. Various residual tests were carried out on both model residuals and state auxiliary residuals to check whether the model satisfied the following


assumptions on the residuals sufficiently well: non-autocorrelation, homoscedasticy, normal distribution. Last but not least the outcomes of the model were compared to knowledge and expectations of experts to see whether the model actually made sense. The outcomes of the time series models can be classified under the following headings: 1. general model performance parameters, such as the log-likelihood

function and various tests of residuals; 2. predicted values based on all observations; 3. description of the corrected trends of exposure and risk. In this chapter (in Section 4.3) we will present and discuss the trend and seasonal component of risk. Appendices 5-12 present the modelled level, slope, and seasonal of risk and the level and slope of exposure (equations 4.6-4.8). Appendices 13-20 contain the predicted values of the number of pedestrian-car KSI victims and of population size (equations 4.1-4.5). The y-axes of the various graphs in the present chapter show changes in risk expressed in pedestrian-car KSI casualties per 1,000,000 inhabitants. The aim of this report is to understand how pedestrian-car risk has evolved, and to locate breaks in the trend and seasonal of risk in time. In order to identify significant trend breaks and seasonal breaks in risk, we have developed an exploratory grid-search algorithm. Basically, in the first run of the grid-search algorithm, the most significant break is identified and included in the model. This identification is carried out by inserting for each time step a variable which has value 1 in that time step and 0 in the other time steps and by assessing the time step where inserting such a 0/1-variable improves the likelihood of the model most. In the next runs of the algorithm the breaks from the previous runs are included and the algorithm is repeated until the most significant break is not statistically significant. At the end of the algorithm, the significance of all breaks is assessed again, by measuring for each break the change in likelihood when removing it from the model. When this change is below a certain minimum level, the break is not significant. It is possible that a break appears to be insignificant due to the insertion of another break later in the algorithm. The more breaks are included in the model, the more likely it becomes that the model is actually over-fitting the data and applies breaks to mere noise in the data. In general, we presume that the breaks with a 'Chi tail' of lower than 1% are significant. This method has three major drawbacks. The first drawback is that the model is only adjusted by inserting breaks. Consequently, effects which have been gradual in real are modelled by (discrete) breaks. The modelled risk, therefore, will rather tend to a step-like than to a smooth function of time. We must keep this in mind when interpreting the model results. The second disadvantage of this method is that random fluctuations in the data may cause misleading results. For example, if in a certain time step a specific measure led to lower risk and if in the previous time step there was lower risk due to random fluctuations in the data, then this algorithm may locate a trend break in the latter time step, i.e. one time step too early.


The third drawback is that the grid-search algorithm occasionally inserts a trend break based on a random fluctuation in the data. Then, another trend break has to be inserted to correct for this overestimation. Trend breaks like this can be recognised as a peak or a ‘crevice’ in the figure of the time series and have to be interpreted with care. More value can be attached to a break after which trend continues at a higher or lower level.

4.2.3. Seasonal data

In this study, pedestrian-car KSI casualty risk is estimated as the ratio between the number of pedestrian-car KSI victims and the population size. The choice of population size as exposure measure was thoroughly underpinned in Section 2.3.4. The effect of season is mainly visible in the victim numbers, not in the population size, which gradually grows during a year. This indifference to season can be considered a drawback of the use of population size as exposure measure for pedestrian-car casualty risk, because we may expect more seasonal influence on exposure and other influence than just gradual growth of exposure. In the analyses, we have used quarterly data; the first quarter is equal to the months January, February and March and is assumed to be equal to the season 'winter'. For convenience sake, we will use the term ‘seasonal component’ in this report to indicate differences in pedestrian-car risk between the quarters in a year. In doing this, it should be noted that the annual trimesters do not correspond precisely with the yearly seasons (seasons start nine days before quarters). Population size is given for 1 January of each year. The 1 January values were assigned to the first quarters of the corresponding years. The state space model itself interpolated the missing values.

4.3. Results

In this section, the main results of the fitted models are presented. First, Section 4.3.1 presents the basic results for the general pedestrian-car KSI casualty risk model. This model provides an impression of how pedestrian-car KSI casualty risk has evolved over time when pedestrian-car exposure and possible trend and seasonal breaks in risk are taken into account. Section 4.3.2 presents results regarding the development of the trend component of pedestrian-car KSI casualty risk for different subgroups, which were defined by characteristics of the casualties. These subdivisions were chosen on the basis of outcomes from the descriptive analyses in Chapter 2. Section 4.3.3 presents the results of subdivision models concerning the development of the seasonal component of pedestrian-car KSI casualty risk. From the previous section, we would like to repeat that the model results show the trend and seasonal of pedestrian-car KSI casualty risk, estimated on the basis of the observed number of pedestrian-car KSI casualties and the population size. Risk is depicted as number of casualties per 1,000,000 inhabitants. Furthermore, the model results present the time steps in which trend breaks probably occurred.


4.3.1. The overall model of pedestrian-car risk

The first step in the model analysis was to fit a model without explanatory variables for total pedestrian-car KSI casualty risk. This model provides an impression of how pedestrian-car risk evolves over time when pedestrian-car exposure and possible breaks in the trend and seasonal component of risk are taken into account. Equations (4.4) to (4.8) provide the information on the construction of this model. As can be seen in these equations, the observed casualties were related to the unobserved risk and the unobserved exposure. The unobserved exposure is estimated by the observed population size.

0

100

200

300

400

500

600

700

800

1975 1980 1985 1990 1995 2000 2005

Pede

stria

n - C

ar K

SI C

asua

lties

95% Conf Smoothed predictions 95% Conf Observations

Figure 4.1. Smoothed model predictions: pedestrian-car KSI casualties.

Figure 4.1 presents the smoothed predictions of the number of pedestrian-car KSI casualties in this basic Latent Risk Model. The figure contains the original observed data per quarter and the smoothed model predictions, through which a curve is fitted. The smoothed model predictions are the model estimates of the observed data, excluding the random error. The figure shows that there obviously is a (seasonal) pattern of peaks and pits. Figure 4.2, Figure 4.3 and Figure 4.4 present the separate development of respectively the trend component of risk, the seasonal component of risk, and the trend component of population size. Year-to-year multiplying of the data values in these three figures leads to Figure 4.1. This can be derived from Formulas (4.1) and (4.2) or from Formula (4.4) when this formula is transformed from an additive to a multiplicative function by inversing the log-transformation. As can be seen in Figure 4.1, the predicted pedestrian-car KSI casualties steadily decrease over time. The model fits fairly well with the exception of large underestimation of observed casualties in 1978 and 1987. Furthermore, we can check that more than 5% of the observations is outside the 95% confidence bound, which is caused by the fact that the measurement error of the observations is not included in this confidence bound. The model predicts the most probable value of the number of pedestrian-car casualties, not of the observations.


Figure 4.2 presents the development of the trend component of pedestrian-car KSI casualty risk. As can be seen in Figure 4.2, there are three disruptions in the steady decline of pedestrian-car risk; in 1985 (downwards), 1986 (upwards) and 1990 (downwards). Two of these trend breaks appear to be statistically significant, the trend break in 1985 is not. The development of the trend component for specific subdivisions is subject of Section 4.3.2.

0

10

20

30

40

1975 1980 1985 1990 1995 2000 2005

Pede

stria

n - C

ar K

SI C

asua

lties

per

1.0

00.0

00 p

opul

atio

n

95% confidence Level Risk 95% confidence

Figure 4.2. Smoothed model state: trend of pedestrian-car KSI casualty risk.

The development of the seasonal component of pedestrian-car KSI casualty risk is depicted in Figure 4.3. Figure 4.3 illustrates that for general pedestrian-car KSI risk, the seasonal component has been stable from 1980 to 1998. In 1979, 1998 and in 2002 there were significant breaks in the seasonal component. The development of the seasonal component for specific subdivisions is subject of Section 4.3.3.

0

0,5

1

1,5

2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent

Seasonal Average = 1

Figure 4.3. Smoothed model state: seasonal of pedestrian-car KSI risk.


Figure 4.4 presents the development of the trend component of population size, which is used as a proxy for pedestrian-car exposure. As can be expected, population size grows smoothly, without significant trend breaks. There are slight drops in the growth though, around 1982, 1995 and 2002.

13

14

15

16

17

1975 1980 1985 1990 1995 2000 2005

Popu

latio

n si

ze (x

1.0

00.0

00)

Level Population

Figure 4.4. Smoothed model state: trend of population size.

4.3.2. Development of the trend component of risk for specific subdivisions

Based on the outcomes of the descriptive analyses in Chapter 2, we constructed risk models for seven specific subdivisions of pedestrian-car risk. In each of these models, the measurement equations specify that predicted pedestrian-car KSI casualties are a function of risk level, risk seasonal, and level of population size. These subdivision were: (1) male / female, (2) five age groups, (3) inside or outside urban areas, (4) working day versus weekend, (5) road section versus intersection, (6) day and night and, (7) dryness of weather and road surface. For the first two subdivisions it was possible to define population size, for subdivisions (3) to (7) this was not possible. Despite the fact that this means that we are actually comparing pedestrian-car KSI casualties for these subdivisions, as there is no difference in population size between subdivisions (3) to (7), we present the results in KSI casualties per population size, i.e. we present the risk. Appendices 5-12 contain the smoothed states results of the subdivision models, Appendices 13-20 contain the smoothed predictions. Table 4.1 presents an overview of the risk decrease for each subdivision group. On average, pedestrian-car risk has considerably decreased from 1978 to 2003. In 2003 this risk is only one-fifth of the risk in 1978. The average risk decrease has been quite similar for most groups with the exception of a slightly smaller risk decrease for the age group 12-24 years, the age group 25-59 years, and for casualties at intersections. The slightly smaller risk decrease at intersections is caused by a change in encoding the location of crashes in 1983. As stated in Section 2.3.1, a part of the crashes which was encoded at road sections until 1983, was encoded as intersection crashes after 1983. This change cannot clearly be discovered in the average


risk decrease of crashes at road sections, probably because there are significantly more crashes at road sections than at intersections.

Risk decrease

Exponential growth coefficient

Total % over 1978-2003

Average % per year

Overall -1,62 80% 6,0%

Male -1,75 83% 6,5%

Female -1,51 78% 5,6%

Age: 0-11 years -1,91 85% 7,1%

Age: 12-24 years -1,02 64% 3,8%

Age: 25-59 years -1,35 74% 5,1%

Age: 60-74 years -1,57 79% 5,9%

Age: 75+ years -1,62 80% 6,0%

Inside urban area -1,62 80% 6,0%

Outside urban area -1,52 78% 5,7%

Working day -1,57 79% 5,9%

Weekend -1,79 83% 6,7%

Road section -1,68 81% 6,3%

Intersection -1,35 74% 5,1%

Day -1,60 80% 6,0%

Night -1,75 83% 6,5%

Dry-dry -1,59 80% 5,9%

Dry-wet -1,72 82% 6,4%

Wet-wet -1,47 77% 5,5%

Table 4.1. Decrease in pedestrian-car KSI casualty risk over 1978-2003: exponential growth coefficient, decrease over total period (%), and average yearly decrease (%).

Table 4.1 corresponds to the conclusions of the descriptive analysis in Section 2.5.1. The average yearly decrease of the total is 6% and the subgroups are between 5 and 7%. Only the age groups 0-11 and 12-24 differ with a respective higher and lower average annual decrease. Table 4.2 presents the results concerning the identification of possible trend breaks in risk for the various subdivisions. It shows that trend breaks for the various subdivisions frequently occur around 1986, 1990 and 2001.


Significant trend breaks in risk

1978-1979 1980-1989 1990-1999 2000-2004

Overall +1986 1990

Male +1979 +1986, 1987

Female +1986 1990 +2002

Age: 0-11 years 1982 1990 2000, +2001

Age: 12-24 years +1992

Age: 25-59 years 1984 1990, 1993, 1995

Age: 60-74 years +1986 1990

Age: 75+ years

Inside urban area +1986 1990

Outside urban area

Working day +1985, +1987, 1988

1990, 1998

Weekend +1986 1996, +1997

Road section +1986

Intersection +1983 1990, 1996, +1995, 1998

Day 1990, 1999

Night 1993

Dry-dry 1985, +1985 2001, +2001

Dry-wet +1994, +1995, 1995, +1997, 1998

Wet-wet 1995

Table 4.2. Significant trend breaks in pedestrian-car KSI risk for various subdivisions (+ means an upward trend break).

4.3.2.1. Trend of risk by sex

Figure 4.5 presents the development of pedestrian-car casualty risk for male and female pedestrians, in KSI casualties per 1,000,000 population. As can be seen in Figure 4.5 the downward trend of risk was reversed for both males and females in 1986. After this upward trend break, the level of risk decreases again. For females there is a significant downward trend break in 1990 and a remarkable upward trend break in 2002. In general, pedestrian-car casualty risk for males is significantly higher than for females. According to Blois, Goldenbeld and Bijleveld (2007) this holds for moped-car accidents as well. A drawback of the grid-search algorithm is that it occasionally inserts a trend break based on a seemingly random fluctuation in the data. Then, another trend break has to be inserted to correct for this overestimation (see also Section 4.2.2). This is illustrated by the peak of male risk around 1986. It is quite unlikely that there actually has been such a high temporary peak in risk for males. Therefore peaks like these have to interpreted with care.


0

10

20

30

40

50

1975 1980 1985 1990 1995 2000 2005

KSI

cas

ualti

es p

er 1

.000

.000

pop

ulat

ion

Male Female

Figure 4.5. Trend of pedestrian-car KSI casualty risk by sex.

Figure 4.5 shows that male risk tended to move towards female risk. However, the relative difference between KSI casualty risk of males and females has been fairly constant until 2002. Figure 4.6 shows the share of male risk and female risk in total pedestrian-car KSI risk over 1978-2003. In 2002 an upward trend break in female risk led to a significant increase in the share of female risk.

30%

40%

50%

60%

70%

1975 1980 1985 1990 1995 2000 2005

Shar

e in

tota

l ris

k by

sex

(per

yea

r)

Male Female Ratio = 1

Figure 4.6. Share in total risk by sex per year.

4.3.2.2. Trend of risk by age

Figure 4.7 presents the pedestrian-car risk curves for different age groups. Young (0-11 years) and elderly (75+ years) pedestrians have a significantly higher risk per population than the other age groups. Probably this is mainly caused by the lack of experience in traffic, the reduced visibility of small


children, the impossibility to drive most other vehicle types (0-11 years), and the deteriorating physical abilities and fragility of elderly people (75+ years).

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1975 1980 1985 1990 1995 2000 2005

KSI

cas

ualti

es p

er 1

.000

.000

pop

ulat

ion

Level Age 0-11 Level Age 12-24 Level Age 25-59 Level Age 60-74 Level Age 75+

Figure 4.7. Trend of pedestrian-car KSI casualty risk by age group.

Differences in risk between the various age groups have been reduced since 1978. Whereas the risk decrease of the elderly (75+) seems to have been a steady decline with only one trend break around 1996, the risk decrease of young, 0-11 aged pedestrians shows a number of significant trend breaks; in 1982, 1984, 1990 and 2000. Three age groups, 0-11, 25-59 and 60-74 years, show a trend break in 1990.

0%

10%

20%

30%

40%

50%

1975 1980 1985 1990 1995 2000 2005

Shar

e in

tota

l ris

k by

age

gro

up (p

er y

ear)

Age 0-11 Age 12-24 Age 25-59 Age 60-74 Age 75+

Figure 4.8. Share in total risk by age group per year

The share of age group 0-11 years in pedestrian-car risk has decreased since 1978. In 2001 the drop was most significant. On the other hand, risk of age groups between 25-74 years has increased. The share in total risk of age group 12-24 has increased most (Figure 4.8).


As noted earlier, it is not possible to specify population size for the following subdivisions. Therefore risk is calculated by dividing the pedestrian-car KSI casualties per subgroup by the total population. Not all results of all subdivisions are presented in this section. Only if a subgroup within a subdivision has a risk trend development that considerably differs from the overall pedestrian-car risk development, or from the other subgroups within this subdivision then we present the development of risk for the subgroups within this subdivision.

4.3.2.3. Trend of risk by road section / intersection

Figure 4.9 presents the development of risk on road sections and intersections. The pedestrian-car KSI risk at road sections is higher, which means that more KSI casualties are caused at road sections. The earlier mentioned change in encoding of the location of crashes in 1983 is clearly visible. The figure also shows two of the drawbacks of the grid-search algorithm; it does not necessarily find the exact time step of trend breaks and it can overestimate the magnitude of a trend break through which an extra opposite trend break has to be inserted. The first is shown by the trend break in risk of road sections in 1982, while it is probable that this is caused by an event in 1983. The latter is shown by that same peak, which exists of an upward and a downward jump, while it is more probable that there was a less sharp increase followed by a less steep drop, so with a smaller upward peak. Furthermore there is a an increase in risk at road sections in 1986, while the risk at intersections does not change significantly in that period. Further, in this subdivision the drop in 1990 is visible, whereas at intersections this drop is larger.

0

10

20

30

1975 1980 1985 1990 1995 2000 2005

KSI

cas

ualti

es p

er 1

.000

.000

pop

ulat

ion

Level Road Section Level Intersection

Figure 4.9. Trend of pedestrian-car KSI casualty risk by road section – intersection.

4.3.2.4. Trend of risk by day / night

The development of the share of KSI casualties by day in total risk per year is depicted in Figure 4.10. The remainder of the casualties occur at night. We have chosen to show the share in risk of day instead of showing the risk


development of both subgroups, because the magnitude of risk during day is much higher than during night. By showing the share in total risk, the visibility of the development of the subgroups is better. The drop in 1990 in the share in total risk of casualties during the day is caused by a drop in the risk of casualties during the day. The peak in 2002 is caused by a drop in risk of casualties during the night.

60%

70%

80%

90%

1975 1980 1985 1990 1995 2000 2005

Shar

e in

tota

l ris

k by

day

/ ni

ght (

per y

ear)

Share risk during the day

Figure 4.10. Share in total risk by day per year (remaining percentage is share of risk during night).

4.3.2.5. Trend of risk by weather and road condition

Figure 4.11 shows the risk curves under various conditions of weather and road surface; dry-dry, dry-wet and rain-wet. The large drop of risk in 1985 in dry-dry conditions is presumably the effect of the strong winter in 1985. Due to the fact that the exposure measure (population size) is not directly linked with travel performance, Figure 4.11 does not necessarily mean that driving under 'dry-dry' conditions is more dangerous. It is also possible that the occurrence of 'dry-dry' conditions was more frequent than 'dry-wet' and 'rain-wet' conditions in 1985. When a travel performance measure would have been selected as proxy for pedestrian-car exposure, the latter explanation would have led to a difference in exposure, instead of a difference in risk.


0

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30

1975 1980 1985 1990 1995 2000 2005

KSI

cas

ualti

es p

er 1

.000

.000

pop

ulat

ion

Level Dry-Dry Level Dry-Wet Level Rain-Wet

Figure 4.11. Trend of pedestrian-car KSI casualty risk by weather and road condition

4.3.2.6. Trend of risk for other subdivisions and conclusion

As the major part of pedestrian-car crashes occurs in side urban areas, the development of KSI casualty risk inside urban areas is nearly equal to the general pedestrian-car risk development. Risk during working days is higher than during weekends. The development of casualty risk during weekends and working days is quite similar to the general risk development as well. In conclusion: − All subdivision groups show a clear downward trend in pedestrian-car

risk. − For many subdivisions the year 1986 shows an upward trend break and

1990 shows a downward trend break in pedestrian-car KSI casualty risk. − The share of age group 0-11 years in pedestrian-car risk has decreased,

with the most significant drop in 2001. − The share of risk of age groups between 25-74 years have increased.

The share of age group 12-24 has increased most. In the next section we will take a closer look at the development of the seasonal component of risk.

4.3.3. Development of the seasonal component of risk for specific subdivisions

Beside breaks in the trend component of risk, the grid-search algorithm also identified breaks in the seasonal component. The seasonal component is the quarterly fluctuation of the KSI casualty risk around the yearly average. This fluctuation shows a quite similar pattern every year. A break in the seasonal means that the yearly pattern of differences in risk among the four seasons has changed from one year to another. The seasonal pattern in risk can be caused by a change in mobility behaviour per season of (part of) the population on the one hand. On the other hand, differing climatic circumstances, like weather, visibility, road condition, humidity etc. can be the cause of seasonal patterns in pedestrian-car KSI casualty risk.


This section presents results concerning changes in seasonal components in the overall model and the various subdivision models. Some subdivisions are heavily correlated to the seasonal effect, for example the variable day / night. The share of risk at night in the total risk is relatively high during the winter and the autumn. However, this is not necessarily caused by the fact that the risk is higher during nights in the winter and autumn. A straightforward explanation can be the fact that during the winter, the nights are longer, through which the chance that a crash occurs during the night increases. This is a drawback of population size as proxy variable for exposure. For this reason we have not presented the seasonal results for day/night and for weather and road conditions.

4.3.3.1. Seasonal of total pedestrian-car KSI casualty risk

The seasonal component of the overall risk model is depicted in Figure 4.12. It is also called the 'seasonal' or the 'seasonal coefficient', as it is the factor with which the yearly average per quarter should be multiplied, to get the predicted KSI casualties (see Section 4.3.1). The average of this coefficient should be 1 for every year, which actually holds for every four consecutive seasons (formula 4.8). There are two kinds of changes in the seasonal. The seasonal effect can become larger, which is recognised by larger fluctuations between the seasons (larger amplitude). The second kind of change is a change in the distribution of casualties over the seasons in a year, which is recognised by a differing pattern. A combination of these two changes is possible as well.

0

0,5

1

1,5

2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent


Figure 4.12. Smoothed model state: seasonal of pedestrian-car KSI casualty risk.

In Figure 4.12 however, it is not possible to distinguish the seasons. Therefore the share of each season in the yearly pedestrian-car KSI casualties is illustrated in Figure 4.13. In this figure trend of risk, seasonal of risk, and trend of population size are included. As there are no significant trend breaks in population size, a change of the share of a season in Figure 4.13 can refer to a change in trend or seasonal of risk.


Three significant seasonal breaks can be distinguished in Figure 4.12: 1979, 1998 and 2002 . The seasonal component has been stable from 1980 to 1998. Both of the above-described types of changes can be recognised in the figure: in 1979 the amplitude change refers to a larger seasonal effect, the different pattern in 1998 refers to a change in the distribution of casualties over the seasons.

0%

5%

10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

e of

qua

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in y

early

KSI

Cas

ualti

es

1st Quarter 2nd Quarter 3rd Quarter 4th Quarter

Figure 4.13. Share seasons in yearly pedestrian – car KSI casualties.

Figure 4.13 provides a better perception of the separate seasons. Over the total period the share of the first quarter (winter) changed most, from being low in 1978 to being the highest in 2003. Compared to 1978, the differences between seasons in 1979 have become smaller (share of autumn decreased significantly). Between 1980 and 1997, the seasonal component remained rather stable. In the period 1998-2001 the seasonal component structurally changed, with differences between seasons 1, 2 and 4 becoming insignificant, and with the summer as the main deviation from the overall trend. In the years 2002 and 2003, the differences between autumn and summer remained the same as in the previous years, but the difference between winter and spring increased. Table 4.3 presents the results concerning the identification of possible breaks in the seasonal component for the subdivisions. The overall pedestrian-car risk and nearly all subdivisions show a significant break in the seasonal component in 1979 or 1980. Moreover there are breaks around 1985, 1998 and 2002.


Significant break in seasonal of risk

1978-1979 1980-1989 1990-1999 2000-2004

Overall 1979 1998 2002

Male 1979 1985, 1986 2002

Female 1979 1980 2001

Age: 0-11 years 1980, 1983 1995 2002, 2003

Age: 12-24 years 1980 1993, 1998 2002

Age: 25-59 years 1979 1984 2001

Age: 60-74 years 1980 2002, 2003

Age: 75+ years 1979 1997 2000

Inside urban area 1980, 1985, 1986

1998 2002

Outside urban area 1979 1998

Working day 1980, 1981, 1988

1998 2002

Weekend 1979 1985

Road section 1980, 1985, 1987

1991, 1998, 1999

Intersection 1979 1980, 1981, 1984

2002, 2003

Table 4.3. Significant breaks in the seasonal of pedestrian-car KSI casualty risk for various subdivisions.

4.3.3.2. Seasonal of risk by sex

0

0,5

1

1,5

2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent


Figure 4.14. Seasonal of pedestrian-car casualty risk – Male.

Figure 4.14 till Figure 4.17 present the seasonal component and the share in predicted annual casualties of males and females. The changes in seasonal component for male pedestrians resemble those of the overall model. Like the overall seasonal, the changes occur first after 1979, then after 1996 and finally in 2002. In contrast to the overall change pattern, for males, there is a


disruption in seasonal component in 1985-1986. The increase in share of casualties in winter in the years 1978-1984 is reversed by a strong drop in relative share in 1985.

0

0,5

1

1,5

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1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent


Figure 4.15. Seasonal of pedestrian-car casualty risk – Female.

The seasonal pattern of female resembles the pattern of males. There are two major differences. First of all the seasonal effect of females is larger. This means that the difference between the number of KSI casualties per season varies more for females than for males. Second of all the shape of the pattern differs, which is made more clear in Figure 4.16 and Figure 4.17. Winter, spring and autumn have a comparable share, summer is significantly lower in male pedestrian-car casualties. Conversely, female KSI casualties have a higher peak in autumn and a smaller difference between spring and summer. Both for men and women there are considerable changes in the seasonal pattern around 1986 and 2002. In these two figures it is also clear that the seasonal effect is larger for females, especially in 2002 and 2003.

0%

5%

10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

e of

qua

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in y

early

KSI

Cas

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Figure 4.16. Share seasons in yearly KSI casualties – Male.


0%

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10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

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Cas

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Figure 4.17. Share seasons in yearly KSI casualties – Female.

Figure 4.18 shows the difference in share of males in total casualties per season, based on the smoothed model predictions of the KSI casualties. The share of males varies structurally per season. In the second quarter, the spring, the share of males increases every year and the share of females increases every year in autumn.

40%

50%

60%

70%

1975 1980 1985 1990 1995 2000 2005

Shar

e m

ale

casu

altie

s pe

r qua

rter

(sm

ooth

ed

pred

ictio

ns)

Share male (smoothed prediction)

Figure 4.18. Share of male in KSI casualties per season (smoothed prediction).

4.3.3.3. Seasonal of risk by age

The development of seasonal component for the age groups is presented in Figure 4.19 to Figure 4.23. It appears that the seasonal effect is largest for the age groups 60-74 and 75+ years. Except for the age group 0-11, all age


groups have a break in the seasonal around 1979. This break is highest for age group 75+.

0

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2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

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Figure 4.19. Seasonal of pedestrian-car casualty risk – Age 0-11 years

The seasonal component is increasing for age group 0-11. It is visible that there is one season that has a yearly peak, the other three seasons do not differ much from each other. In 2001 there has been a significant rise in one of the quarters. For age groups 0-11 and 12-24, Figure 4.24 and Figure 4.25 show more detailed information on the seasons.

0

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2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent



The seasonal is smallest for the age group 12-24. Between 1980 and 1998 the seasonal stayed fairly constant. Beside the break in 1979, there is a striking break in 1998. Unlike age group 0-11, there is one season that causes a yearly low in the figure.


0

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1,5

2

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

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ent



Age group 24-59 has the most constant development of the seasonal. Except for the breaks in 1979 and 2001 and the slight changes in shape in 1986 and 1999, there are no significant changes.

0

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1

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2

2,5

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

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The age group with the highest dependency on seasons is age group 60-74. Apparently the number of pedestrian-car accidents of this age group depend heavily on the time of the year, or factors related to the time of the year. The seasonal is increasing as well. Two seasons are every year low, spring and summer. Age group 75+ shows a more constant development, also with spring and summer as yearly lows. The 1979 peak is highest for this age group.


0

0,5

1

1,5

2

2,5

3

1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent


Figure 4.23. Seasonal of pedestrian-car casualty risk – Age 75+ years

In contrast to the general seasonal pattern and those for males and females, the seasonal pattern for children shows the spring to be the most risky season (see Figure 4.24). For children, both the winter and spring have become relatively more risky than the other seasons. Surprisingly, the risk in the winter tends to go up for young children in 1980-1983 and then down again. Another striking thing is the fact that autumn has the smallest risk of all seasons for age group 0-11, while autumn is the most risky seasons for the total and most other subdivisions.

0%

5%

10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

e of

qua

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early

KSI

Cas

ualti

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Figure 4.24. Share seasons in yearly KSI casualties – Age 0-11

The age group 12-24 years shows distinct seasonal patterns in 1980 (large increase share winter in risk) and in the periods 1981-1991 (small differences between spring and summer and between autumn and winter, with highest risk in winter), 1994-1997 (relative small differences, large increase in share spring, decrease in winter), 1998-2001 (large decrease in summer, increase in winter) and 2002-2003 (spring and summer close


together) (depicted in Figure 4.25. For the age groups 25-59, 60-74 and 75+ years the dispersion of casualties over the year is quite similar to the yearly dispersion of the total casualties, with the remark that the seasonal effect increases with increasing age, as stated above.

0%

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15%

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25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

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Cas

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Figure 4.25. Share seasons in yearly KSI casualties – Age 12-24

4.3.3.4. Seasonal of risk by inside / outside urban area

The seasonal inside urban areas is larger than outside urban areas, especially between 1980 - 1992. Summer is every year a low in the seasonal inside urban areas.

0

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1975 1980 1985 1990 1995 2000 2005

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Figure 4.26. Seasonal of pedestrian-car casualty risk – Inside urban area.


0

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1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

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ent


Figure 4.27. Seasonal of pedestrian-car casualty risk – Outside urban area.

Between 1980 and 1998 there was no notable difference between the seasons regarding pedestrian-car KSI casualty risk outside urban areas. In 1978 and 1979 there was an extreme difference. In 1998 the seasonal effects outside urban areas have permanently increased. As can be seen in Figure 4.29 the share of the winter has increased.

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5%

10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

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Cas

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Figure 4.28. Share seasons in yearly KSI casualties – Inside urban areas.


0%

5%

10%

15%

20%

25%

30%

35%

40%

1975 1980 1985 1990 1995 2000 2005

Shar

e of

qua

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KSI

Cas

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Figure 4.29. Share seasons in yearly KSI casualties – Outside urban areas.

4.3.3.5. Seasonal of risk by road section / intersection

There is a striking difference in the magnitude of the seasonal effect on road sections and intersections. In Figure 4.30 and Figure 4.31 we can note that the seasonal effect is twice as large at intersections.

0

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1975 1980 1985 1990 1995 2000 2005

Seas

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Figure 4.30. Seasonal of pedestrian-car casualty risk – Road section.


0

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1975 1980 1985 1990 1995 2000 2005

Seas

onal

coe

ffici

ent


Figure 4.31. Seasonal of pedestrian-car casualty risk – Intersection.

At road sections the seasons have roughly an equal share, except for the summer, which is significantly lower. At intersections, the seasonal shares differ significantly (Figure 4.32 and Figure 4.33).

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25%

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40%

1975 1980 1985 1990 1995 2000 2005

Shar

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Cas

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Figure 4.32. Share seasons in yearly KSI casualties – Road section.

Figure 4.34 confirms the fact that there are different seasonal effects between road sections and intersections. The share of casualties on road sections varies over the seasons according to a yearly pattern. This pattern changes in time. Breaks in the trend of risk at road sections or intersections can be recognized by a change in the level of the graph, whereas breaks in the seasonal patterns can be recognized by a change in the shape of the pattern. The trend breaks and seasonal breaks in risk are summarised in Table 4.4. For a more detailed description of the trend breaks in risk, see Section 4.3.2.3. The most significant change occurred in 1983.


0%

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10%

15%

20%

25%

30%

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40%

1975 1980 1985 1990 1995 2000 2005

Shar

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KSI

Cas

ualti

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Figure 4.33. Share seasons in yearly KSI casualties – Intersection.

50%

60%

70%

80%

90%

1975 1980 1985 1990 1995 2000 2005

Shar

e ca

sual

ties

on ro

ad s

ectio

ns p

er q

uart

er

(sm

ooth

ed p

redi

ctio

ns)

Share road section (smoothed prediction)

Figure 4.34. Share of KSI casualties at road sections per season.

Trend breaks

1978-1979 1980-1989 1990-1999 2000-2003

Road section +1986

Intersection +1983 1990, 1996, +1995, 1998

Seasonal breaks

Road section 1980, 1985, 1987

1991, 1998, 1999

Intersection 1979 1980, 1981, 1984

2002, 2003

Table 4.4. Trend breaks and seasonal breaks in risk at road sections and intersections.


4.3.3.6. Seasonal of risk by working day / weekend

Except for a small peak in 1985, the development of the seasonal in weekends is similar to the overall seasonal development. Therefore only the development of seasonal pedestrian-car KSI risk during working days is depicted (Figure 4.35). The seasonal development of risk during working days differs at two points from overall seasonal development. First, there is no significant change in 1979. Second, the seasonal is larger in the period 1980 – 1988. In both subdivisions summer is lowest every year.

0

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1975 1980 1985 1990 1995 2000 2005

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Figure 4.35. Seasonal of pedestrian-car casualty risk – Working day.

4.4. Overview

This section first reviews the hypotheses formulated in the previous chapters in Section 4.4.1. Then an overview of the main conclusions of this chapter is given in Section 4.4.2.

4.4.1. Review of hypotheses

There is a considerable decrease of pedestrian-car KSIs on urban intersections and a considerable increase on urban road sections in 1982-1983. According to hypothesis H82-83.1.A this change was caused by the modification of the definition of intersection and road section crashes, this is supported by Figure 4.9. Hypothesis H99-01.1.A relates the strong decrease of pedestrian-car KSI victims of 0-11 years old in 2000 and 2001 to the accelerated transformation of 50 km/h roads into 30 km/h roads after 2000, which is illustrated by Figure 3.5. The model results of this chapter show a significant downward trend break in risk in 2000 for 0-11 aged pedestrians, which supports this hypothesis. H76-04.1.A supposes that changes in pattern of life of women caused that pedestrian-car KSI risk decreased more slowly for females than for males. The model for male and female KSI casualties supports the fact that


pedestrian-car KSI risk decreased more slowly for females than for males, but only for the period after 2001. In the period before 2001 female risk decreased at the same rate as male risk. Because of this notion the possible explanation of the slower female risk decrease which we found in Chapter 3, i.e. the growing female participation in the labour market, looses most of its value, as the fast increase of female participation already started in the eighties. Hypothesis H79-87.1.B states that the 1979, 1985, 1986 and 1987 winter drops in the pedestrian-car KSI risk were caused by the harsh winters in those years. The results of almost all subdivisions in this chapter support the hypothesis that there are significant changes in the seasonal pattern in 1979 and around 1985, which supports this hypothesis. Hypothesis H90-91.1.B suggests that the trend break in the pedestrian-car KSI risk from 1990 to 1991 is caused by the introduction of free public transport for students in November 1990 and the start of economic recession (see Section 3.3.6.3). Through both developments a significant part of the young, (potential) new drivers started to travel by public transport instead of by car. This is illustrated by Figure 4.36, which shows a large drop in person kilometres of 18-24 year old car drivers. See also Figure 3.6, Table 3.9, Table 3.10, and Table 3.11. The decrease of young car drivers' kilometres might have caused a decrease in pedestrian-car risk, as young car drivers are the main contributors to pedestrian-car KSI casualties (see Figure 2.35).

0,0

2,0

4,0

6,0

8,0

10,0

1985 1990 1995 2000 2005

Bill

ion

pers

on k

ilom

etre

s

Car driver (age 18-24)

Figure 4.36. Person kilometres of 18-24 year old car drivers. Source: National Travel Survey.

The trend break in 1990 is visible in three age groups; 0-11, 25-59 and 60-74 years. The age group which contains the largest part of the students (12-24 years) does not show a trend break in 1990. It is probable that for the age group 12-24 the decrease in risk caused by the decrease in mobility of a group of relatively dangerous car drivers is compensated by the fact that this group shifts from the car to public transport. This shift induces more trips as pedestrian in travelling to and from the public transport services, as is illustrated by Table 3.9. This is supported by the fact that the share of the age group 12-24 years has increased most since 1990, compared to the


other age groups. The 1990 drop in KSI casualty risk is visible in the subdivision ‘working day’ and ‘day’, but not in the subdivisions ‘weekend’ and ‘night’. This might indicate that the combined effect of the introduction of free public transport for students and the start of economic recession is strongest during working days and during daytime. All together, this bring us to the following additional hypothesis: H90-91.2.C: The introduction of free public transport for students in

November 1990 and the start of economic recession caused a shift of car trips to public transport for the age group 12-24. Due to this, the number of pedestrian trips for age group 12-24 increased, which compensated the risk decrease as described in hypothesis H90-91.1.B.

Hypothesis H00-02.1.B assumes that the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession caused the significant decrease in risk in 2000-2002. This is not supported by the model results as only the age group 0-11 years shows a significant decrease. Hypothesis H76-04.4.B states that periods of strongly lowered economic activity (1981, 1990, 2000) are associated with lower pedestrian-car risk. This hypothesis is not supported by the results of this chapter, as only in 1990 a significant trend break is distinguished. In 1981 or 2000 there are no significant trend breaks. The model results of this chapter provide no information on the hypotheses H76-04.2.A and H76-04.3.A.


Pedestrian-car KSI casualty risk has decreased considerably from 1978 to 2003. In 2003 this risk is only one-fifth of the risk in 1979. This decrease holds for all subdivision groups studied. The share of risk of age group 0-11 in total risk decreased most in the period 1978-2003. Until 2001 male risk decreases as fast as female risk. In 2002 an upward trend break was found in female risk and consequently the share of female risk in total risk increased significantly. Over the total period 1978-2003, the risk in the winter period has changed most, from being low in 1978 to being the highest in 2003. In the period 1998-2001 the seasonal component has structurally changed: differences between winter, spring and autumn became irrelevant and consequently mainly summer risk deviated from average annual risk. In 2002 and 2003 the difference between autumn and summer remained equal to the previous four years, but the difference between winter and spring increased. The grid-search algorithm found breaks in the seasonal component in 1979/1980 and around 1985, presumably caused by the harsh winters in those years. Furthermore, there were seasonal breaks around 1998 and 2002.


There were significant changes in the seasonal of pedestrian-car KSI risk in 1979, 1998, and 2002. We suspect that these changes were caused by changes in travel pattern over the seasons. For example, the changes in the staggering of holidays might have played a part. Weather might have been a factor as well. However, as we did not find substantive information in literature about seasonal patterns of walking and the influence of weather, we think these suspicions are too weak to be the basis of one or more hypotheses on the causes of the observed changes in the seasonal. The seasonal of pedestrian-car KSI risk differs between: male and female, age groups, inside and outside urban area, intersections and road sections and slightly between working day and weekend. We did not find information in literature which would lead us to a consistent idea about the causes of these differences. Regarding the observed difference between road sections and intersections, it is possible that in the autumn and winter more pedestrians cross the streets at intersections, due to which the risk at intersections increases. Another possible explanation can be found in weather circumstances and related to that the visibility of pedestrians. Maybe the reduced visibility has a greater effect at intersections, as the driving task is more complex at intersections. However, because we think our arguments are too weak and not supported by any information in literature, we did not state hypotheses on this subject.


5. Further explorations

5.1. Outline

In Chapter 4 of this report the pedestrian-car crash risk was modelled. We have seen that for several subdivisions risk has encountered one or more trend breaks, e.g. around 2000 for 0-11 aged children. The state space models of pedestrian-car risk, however, do not explain the developments in risk. They describe trends in risk and exposure in a statistically pure way and identify the significant trend breaks. The descriptive analysis in Chapter 2 showed that subdivision of the year in season or month is an important factor for the analysis of pedestrian-car KSI casualties (Sections 2.4.1.14, 2.4.2 and 2.5.1). For example, the 1979 fall of casualty numbers mainly occurred in winter and spring. Hypothesis H79&85.1.B supposes strong influence of harsh winter weather on the victim numbers. In Chapter 3 more hypotheses were formulated about the possible effect of weather and economy, both seasonal variables. In this chapter, we will further explore the possibility of constructing an explanatory model of pedestrian-car risk with a time step of one month and a more exploratory analysis technique. The special research question for this exploration was: 1. Can sensible models be constructed that identify the possible

contribution of economic and weather variables to pedestrian-car casualty risk, over and above the contribution from interventions and trends in exposure?

In Section 1.3.3 we clarified our decision to base our risk models on registered victim numbers instead of under registration-adjusted numbers. As explained in that section, this might lead to incorrect conclusions about the effect of explanatory variables. In a model on monthly basis this consideration is even more valid because registration level varies over the seasons: in summer and spring registration level is lower than in winter and autumn. Therefore, the second research question was: 2. What could be the possible effect of using registered crash or victim

numbers instead of under registration-adjusted numbers? In the descriptive analysis (see Section 2.3.2.3) we concluded that the 0-11 aged are the major age group under the pedestrian-car KSI victims. We wondered whether a special analysis of this group could lead to new or other insights with respect to risk development over and above the insights from the general analysis. As such, we formulated the third research question as follows: 3. Can a sensible model be constructed for 0-11 aged pedestrian-car KSI

victims that provides additional insight in the underlying factors of risk development for this age group?

Because the analysis is exploratory, we used a fast, simple and straightforward technique: linear regression analysis. For a comparison of state space modelling and linear regression analysis and a description of the


drawbacks and opportunities of applying linear regression analysis to time series the reader is referred to Section 1.4.3.1. Section 5.2 describes the method used and Section 5.3 the results. Section 5.4 contains an overview of results and concludes with a discussion.

5.2. Method

Two linear regression models were constructed: 1. Pedestrian-car KSI risk 1976-2004 by month, computed as

registered victim numbers divided by population number; 2. Pedestrian-car KSI risk 1976-2004 by month, computed as under

registration-adjusted victim numbers divided by population number; 3. Pedestrian-car KSI risk 0-11 aged 1976-2004 by month, computed

as registered victim numbers divided by population number. To answer our first research question, which is about the possible influence of seasonal variables as economy and weather (see Section 3.3.6.2), it was decided to model with a time step of one month. To get an idea of the possible effect of not adjusting for registration level (second research question) we also worked out a risk by month model on the basis of the adjusted number of victims. We used the registration level as determined for the total number of road victims for this purpose and did not take into account the possible error resulting from using this general measure, nor the potentially high level of uncertainty in the numbers itself. Just as the other analyses in this chapter, this specific analysis should be considered as explorative. To answer the third research question about the 0-11 aged victims and to be able to compare results to the general model, we needed to construct a risk by month model for 0-11 aged pedestrian-car KSI victims. Section 5.2.1 shortly describes the general form of the linear regression model applied. Section 5.2.2 deals with the explanatory variables considered and their quantification. Section 5.2.3 contains a description of the stepwise approach to the construction of the explanatory model.

5.2.1. Linear regression model

The model form chosen is multiplicative:

mmnnnn ZZZn eeeXXXCY ⋅⋅⋅ +++ ⋅⋅⋅⋅⋅⋅⋅⋅= ββββββ ...... 221121

21 (5.1) in which Y is the pedestrian-car KSI risk, C is a constant, X1…Xn are the explanatory variables with positive, non zero values, Z1…Zn are the explanatory variables with negative or zero values (e.g. temperature and the index of consumers' confidence), and the β's are the regression coefficients. For easy interpretation we prefer the Xβ representation above the eβX form. Therefore, we chose to model as much variables as possible, i.e. all variables with positive, non zero values, in the former representation form.


To make it a additive model, which is needed to apply linear regression analysis, we take the natural log of equation 5.1:

mmnnnn ZZXXCY ++ ++++++= ββββ ...log...logloglog 1111 (5.2)

5.2.2. Variables

On the basis of the results of the literature scan (see Appendix 3 and Section 3.3), the following explanatory variables were considered when constructing the linear regression models. For each variable, the list below gives the date from which the variable is active and (if needed) the end date. Between brackets the abbreviations used are given. From Appendix 3, the following variables were selected: 1. Fraction of urban roads (50 km/h or below) where maximum speed is 30

km/hr, from before 1976 (Fr30km/h). After 1997 the building or improving of infrastructure was accelerated by the Sustainable Safe program;

2. Recognition of home zones, 15 September 1977 (HomeZones); 3. Parking lights forbidden in urban area, dimmed headlights in bad

weather circumstances during the day, 16 April 1977 (CarLight); 4. Introduction electronic breath testing devices for the selection of alcohol

suspects on the street, 1984 (BreathDev); 5. Gradual transition from selective to a-select police checks, 1985 (A-

selectChecks); 6. MOT test for all cars from ten years old, 15 July 1985

(MOTtestCars10yr); 7. Start publicity campaigns VVN en WVC, 1986, and duration of driving

licence till 70 years, 1 July 1986 (CombPolicy1986); 8. MOT test for all cars from three years old, 1 January 1987

(MOTtestCars3yr); 9. Introduction breath analysis for evidentiary purposes, October 1987

(BreathAnalysis); 10. Introduction special transport for visitors discos and cafés, 1988

(DiscoTransport); 11. Introduction of free public transport pass for students 17 years and older

who have a university grant, 1 October 1990 – 1 November 1995 (PT91);

12. Further extension tit-for-tat policy, 1991 (TitForTat); 13. More severe penalties for drink driving, 1992 (SevPenAlcohol); 14. National street play day, each year in May/June from 1994

(StreetPlayDay); 15. Limitation of the pass for free public transport for students, students

have to choose between a PT pass for weekend days or for working days only, 1 November 1995 (PT95);

16. Introduction of regional traffic enforcement teams and BAC limit lowered to 0,2 promille for beginning drivers, 1999 (CombPolicy1999);

17. Campaign to stimulate use of hands free phone, 26 August 1999 – July 2000, April 2002 – December 2002 (HandsFree);

18. Information campaign 'The Schools have started again', each year in August - October from 2000 (SchoolStart);

19. Adaptive Cruise Control (ACC) and Antilock Brake System (ABS) in new cars, 2001 (ACC&ABS);


20. National campaign ‘Lollipop Men’, 11 September 2001 only (CampaignLPM);

21. Alcohol campaigns combined with structural, intensive police enforcement, each year 15 December – 15 March, 1 June – 15 September from December 2001 (AlcoholCampaigns);

22. Prohibition of using hand held mobile phone during driving car or moped, 30 March 2002 (ProhibHandHeld);

As we do not know the exact month of the start of publicity campaigns by VVN en WVC in 1986, we assigned this start to the middle of the year, month July. Because the extension of the duration of the driving licence till 70 years old occurred in the same month, making distinction between both variables in the model made no sense. For this reason both variables were combined into one: CombPolicy1986. For the same reason, the introduction of regional traffic enforcement teams and the lowering of the BAC limit to 0,2 permille for beginning drivers, both in 1999, were combined into CombPolicy1999. Also the following economic variables were included in the analysis: 23. CBS index of consumers' confidence in the economy (ConsConf); 24. Unemployment percentage (Unempl); To take into account calendar effects, also the following variables were considered: 25. Number of days per month (Ndays); 26. Fractions of days which are working days (FrWorkDay); The effect of visibility was taken into account by including the number of hours with daylight during the usual travel hours as an explanatory variable in the analysis: 27. Number of hours between 7:00 hr and 0:00 hr with daylight (Daylight). To incorporate effects of light and weather (see Section 3.3.6.2), the following variables were included in the analysis: 28. Maximum temperature (Tmax); 29. Minimum temperature (Tmin); 30. Minimum temperature if it is below zero degrees (Tfrost); 31. Fraction of time with rain (FrRain); 32. Fraction of days which are warm and dry, i.e. with maximum

temperature > 24 °C and rain duration < 0.5 hr (FrWarmDry); 33. Fraction of days which are hot and dry, i.e. with maximum temperature >

30 °C and rain duration < 0.5 hr (FrHotDry); 34. Fraction of days which are cold and wet, i.e. with maximum temperature

< 3.5 °C and rain duration > 4 hr (FrColdWet); 35. Fraction of days which are cool and wet, i.e. with 3.5 °C < maximum

temperature < 10 °C and rain duration > 4 hr (FrCoolWet); 36. Fraction of days with frost (FrFrost); 37. Extreme winter of 1979 with a lot of snow and black ice (ExtrWinter). The 2004 casualty numbers are surprisingly low. Until now no satisfactory explanation has been found for this drop (Stipdonk, 2005). To test whether some influence factor has affected the registered casualty numbers, the following variable is introduced:


38. Unknown influence factor, January 2004 (Unknown2004). Finally, to include other, fixed seasonal effects we included each month as a variable (39. Jan, 40. Feb, …, 50. Dec) as well as a variable to describe general trend (51. TimeStep). The way of quantifying above explanatory variables is explained in Table 5.1.

No. Quantification method / Source

2-22, 37, 38-49

These are the so-called '0/1' variables which are 1 in the months in which the variable is active, 0.5 in the months in which it is active from the 15th, 0 otherwise.

1 The fraction 30 km/h roads is a best ‘on the eye’ estimate on the basis of urban KSIs on 50 km/h roads, urban KSIs on 30 km/h roads, and the estimated fraction 30 km/h roads for 1998 and 2003 from literature (see Section 3.3.5.6). See Figure 3.4

23, 24 CBS, Statline. The annual values are transposed to monthly values by applying the monthly pattern of recent data (2001-2004) to the older data.

25, 26 Derived from the calendar.

27 Travel was assumed to happen between 7:00 hr and 0:00 hr. Daylight hours during these travel hours were determined on the basis of the times of sun set and sun rise on the 15th of each month.

28-36 KNMI, daily values.

50 This is the number of the time step, i.e. 1 for January 1976, 2 for February 1976, ….., 348 for December 2004.

Table 5.1. Quantification of the explanatory variables.

5.2.3. Stepwise approach to model construction

To find the linear regression model which best explains the number of pedestrian-car KSI casualties by the above described explanatory variables, a stepwise approach was followed. This approach starts from the model with all explanatory variables and is called Backward Elimination (see text framework below). Variables are removed from the model depending on the significance (probability) of the F-value. A variable is removed if the significance level of its F-value is greater than the removal value (0.05). With this approach, step by step the model gets more and more predictive value and shrinks to a model in which the most important variables are included.


As stated in Section 1.4.3.1, statistical test as the F-test for model significance and the t-test for the significance of the regression coefficients are not reliable if the model residuals are not independent, not homoscedastic, or not normally distributed, which often is the case when applying linear regression to time series data. Therefore, the results of these tests were interpreted with care.

5.3. Results

5.3.1. Model 1: registered number of victims 1976-2004 by month

After applying the approach described above the final Model 1 is as shown in Table 5.2. Appendix 21 shows the observations, the model predictions, and the standardized residuals. The appendix also includes the outcomes of standard statistical model tests, like the F-test and Student-t test, and diagnostic tests of the residuals. Explanatory variable No.1 Category Effect2

RiskKSI = 85 Constant

times e-0.054 * PT91 11 Policy measure -5.3% 3

times e-0.054 * SchoolStart 18 Policy measure -5.3% 3

times Daylight-0.75 27 Daylight -6.9% 4

times e0.050 * Tfrost 30 Weather -4.9% 5

times e-0.26 * FrWarmDry 32 Weather -6.3% 6

times e-0.30 * ExtrWinter 37 Weather -26% 3

times e-0.17 * Unknown2004 38 Unknown -16% 3

times e0.14 * Mar 41 Month +15% 3

times e0.15 * Apr 42 Month +16% 3

times e0.29 * May 43 Month +34% 3

times e0.23 * Jun 44 Month +26% 3

times e-0.0054 * TimeStep 51 Overall trend -0.54%7

1 See Section 5.2.2. 2 Effect on pedestrian-car KSI risk per month. 3 Effect of start of policy measure, unknown factor, extreme winter weather, or month influence. 4 Effect of 10% increase of variable. 5 Effect of decrease from -1 oC to -2oC. 6 Effect of increase of fraction from 0.25 to 0.5 (FrWarmDry). 7 Effect of increase by one.

Table 5.2. Model 1.

"Backward elimination is a variable selection procedure in which all variables are entered into the equation and then sequentially removed. The variable with the smallest partial correlation with the dependent variable is considered first for removal. If it meets the criterion for elimination, it is removed. After the first variable is removed, the variable remaining in the equation with the smallest partial correlation is considered next. The procedure stops when there are no variables in the equation that satisfy the removal criteria." Source: SPSS 12.0.1 for Windows.


For example, in the appendix we can read that 94% of the model predictions is within the 95% reliability interval of the observations, which is a good result. From the normal probability plot, we can see that the errors are not completely well normally distributed; the tails of the distribution are too big. The prediction errors are not fully homoscedastic: the error variance over the first half of the observations is clearly smaller than over the rest of the observations. Furthermore, the errors are not autocorrelated: the correlation coefficient is smaller than 0.1 for most of the lags and is smaller than 0.2 for all lags. Table 5.2 must be read as follows: RiskKSI is estimated by the product of the constant 85, e-0.054 * PT91

, Daylight -0.75, …, and e-0.0054 * TimeStep. The most right

column of the table shows the estimated effect of a specific change of each variable on the number of pedestrian-car KSI victims. For example, the introduction of free public transport for students in 1991 resulted into a 5% decrease of pedestrian-car KSI risk. Although the model assumptions are not always satisfied (see the considerations on the prediction errors above), the model can still be used to demonstrate the opportunity to estimate the effects of measures and developments, as can be seen in Table 5.3.

Measure / development Period Effect in Effect on number of victims

Public transport pass 1991 Oct 90-1991 1991 -49 -5.6

Average weather circumstances 1995-2004

2003 2003 +8 +1.8

Average weather circumstances 1995-2004

2004 2004 -5 -1.3

Unknown factor 2004 2004 -69 -19

Table 5.3. Estimated effects of measures on the number of pedestrian-car KSI casualties.

For example, the introduction of the free public transport pass for students with a university grant in 1991 would have resulted in a 6% decrease of the number of pedestrian-car KSI victims in 1991. If there had been average weather circumstances in 2003 and 2004, then the number of pedestrian-car KSI victims would have been 2% higher in 2003 and 1% lower in 2004. This result means that the difference between the 2003 and 2004 pedestrian-car KSI victim numbers would increase, implying that the specific weather in 2003 and 2004 cannot even partly explain the strong decline of victim numbers from 2003 to 2004. Finally, according to this model the unknown factor in 2004 has led to a 19% decrease of the number of registered pedestrian-car KSI victims in 2004.


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Figure 5.1. Pedestrian-car KSI casualty risk observed and predicted by Model 1.

Figure 5.1 shows the annual pedestrian-car KSI risk as observed and as predicted by Model 1. Figure 5.2 gives the Model 1 prediction errors per year. Most remarkable are the overestimation of pedestrian-car KSI victims in 1976 and the underestimation in 1986. The mean absolute error percentage is 2.6%.

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Figure 5.2. Model 1 prediction errors (%) per year

5.3.2. Model 2: adjusted number of victims 1976-2004 by month

The approach described in Section 5.2.3 was also applied to risk based on the adjusted number of pedestrian-car KSI victims (Model 2). For each month, this adjusted number was determined by dividing the registered number by the registration level of that month.


Monthly registration levels with respect to the total number of victims, i.e. all travel modes, were taken from the SWOV database. This database provides monthly registration levels for the number of killed from 1996 to 2004, and for the number of seriously injured from 1990 to 2003. We completed these figures for the whole period 1976-2004 in the following way: − For the number of killed in 1976-1995 we used the monthly averages

over 1996 to 2000. − For the number of seriously injured in 2004 we used the unofficial annual

value of 51% and the average ratios between the monthly values and the annual value over 1990-2003.

− For the number of seriously injured in 1984-1989 we used (yet) unofficial monthly values.

− For the number of seriously injured in 1976-1983 we used the monthly averages over 1984-1988.

The registration level for KSI victims was computed by taking the weighted mean of the registration level for killed victims and the registration level for seriously injured victims. The monthly number of killed and the number of seriously injured victims were the weights in this computation. The final Model 2 is as shown in Table 5.4. Appendix 21 shows the observations, the model predictions, and the standardized residuals.

Explanatory variable No.1 Category Effect2


times e-0.12 * SchoolStart 18 Policy measure -11% 3

times FrWorkDay0.098 26 Calendar effect +0.94% 4

times e0.046 * Tfrost 30 Weather -4.5% 5

times e-0.31 * ExtrWinter 37 Weather -27% 3

times e0.31 * Jan 39 Month +36% 3

times e0.15 * Feb 40 Month +16% 3




times e0.082 * Jun 44 Month +8.6% 3

times e0.094 * Aug 46 Month +9.9% 3

times e0.16 * Oct 48 Month +17% 3

times e0.23 * Nov 49 Month +26% 3

times e0.38 * Dec 50 Month +46% 3


1 See Section 5.2.2. 2 Effect on pedestrian-car KSI risk per month. 3 Effect of start of policy measure, unknown factor, extreme winter weather, or month influence. 4 Effect of 10% increase of variable. 5 Effect of decrease from -1 oC to -2oC. 6 Effect of increase by one.

Table 5.4. Model 2.


When we compare Model 2 to Model 1 with the help of Tables 5.4 and 5.2, we can see that several new variables have been introduced whereas other have been removed. The variables Tfrost, ExtrWinter, and TimeStep are in both models and their coefficients in Model 2 are almost equal to those in Model 1. Variable SchoolStart is in both models as well, but its value in Model 2 is more than twice its value in Model 1. The variable PT91 is included in Model 1 but not in Model 2. Model 1’s variables Daylight, FrWarmDry, and FrCoolWet are not included in Model 2. Their influence has been replaced by the introduction of the variable FrWorkDay, new month variables or the change of coefficients of month variables. Remarkable is that the variable Unknown2004 is not included in Model 2, whereas it was in Model 1. This suggests that the sharp fall in the registered number of pedestrian-car victims was caused by a fall in the registration level. Model 2’s general trend is somewhat less sharply decreasing than in Model 2: 0.50% instead of 0.54% per month. This has to do with the slightly decreasing registration level.

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pede

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Figure 5.3. Pedestrian-car KSI casualty risk observed and predicted by Model 2.

Figure 5.3 shows the annual pedestrian-car KSI casualty risk as observed and as predicted by Model 2. Figure 5.4 gives the Model 2 prediction errors per year. The mean absolute error percentage is 3.2%. Errors are clearly larger than in Model 1, which is not remarkable because extra uncertainty is introduced by the correction for registration level.


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Figure 5.4. Model 2 prediction errors (%) per year.

5.3.3. Model 3: registered number of 0-11 aged victims 1976-2004 by month

The third model we analysed is a risk by month model for the 0-11 aged pedestrian-car KSI victims. Table 5.5 shows the final Model 3 after applying the approach described in Section 5.2.3. Appendix 21 presents the observations, the model predictions, and the standardized residuals. The appendix also includes the outcomes of standard statistical model tests, like the F-test and Student-t test, and diagnostic tests of the residuals. When we compare Model 3 to Model 1 with the help of Tables 5.5 and 5.2, we can see that several new variables have been introduced whereas other have been removed. The variables Unknown2004 and TimeStep are in both models and their coefficients in Model 3 are almost equal to those in Model 1. Variables Mar, Apr, May, and Jun are in both models as well, but their value in Model 3 is different from their value in Model 1. The month variable Feb is in Model 3 but not in Model 1. Model 1 includes the policy measures PT91 and SchoolStart, Model 3 contains Fr30km/h and CampaignLPM. In Model 1, the effect of changing day temperature and/or daylight length is represented by the variables Daylight and FrWarmDry, in Model 3 by Tmax and FrWarmDry. In Model 1, winter weather is represented by the variables Tfrost and ExtrWinter, in Model 3 by FrCoolWet and FrFrost. Noteworthy is that in Model 1 more severe frost leads to less KSIs, whereas in Model 3 more frost days lead to more KSIs under 0-11 aged.


Explanatory variable No.1 Category Effect2


times Fr30km/h -0.18 1 Policy measure -1.7% 4

times e-0.58 * CampaignLPM 20 Policy measure -44% 3

times e-0.029 * Tmax 28 Weather -5.6% 5

times e-0.69 * FrWarmDry 32 Weather -16% 6

times e-1.6 * FrColdWet 34 Weather -21% 6

times e0.33 * FrFrost 36 Weather +8.6% 6

times e-0.19 * Unknown2004 38 Unknown -17% 3

times e0.15 * Feb 40 Month +16% 3




times e0.27 * Jun 44 Month +31% 3


1 See Section 5.2.2. 2 Effect on pedestrian-car KSI risk per month. 3 Effect of start of policy measure, unknown factor, or month influence. 4 Effect of 10% increase of variable. 5 Effect of increase from 20 oC to 22oC (Tmax). 6 Effect of increase of fraction from 0.25 to 0.5 (FrWarmDry, FrFrost) or from 0.15 to 0.3 (FrColdWet). 7 Effect of increase by one.

Table 5.5. Model 3.

Surprisingly, the variable Schoolstart is not included in Model 3, where this measure should of all people mainly affect 0-11 aged children. This result casts doubt upon the significance of this variable in Model 1 and might be an example of the restrictions of the application of linear regression analysis to time series. Because the model errors do generally not satisfy the assumptions (see Section 1.4.3.1), the significance test is not completely reliable. However, there may be other causes of this peculiar result. The way of implementing this variable in the model might play a part here. Furthermore, it is possible that some other influence factor, which is not or not fully entered in the analysis, decreased by about 5% the total number of pedestrian-car KSIs each year from 2000 in August to October. Figure 5.5 shows the annual pedestrian-car KSI risk as observed and as predicted by Model 3. Figure 5.6 gives the Model 3 prediction errors per year. Most remarkable is the underestimation of pedestrian-car KSI victims in 1999. The mean absolute error percentage is 6.3%, which is clearly larger than in Model 1 (2.6%).


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Figure 5.5. Pedestrian-car KSI casualty risk for 0-11 aged children observed and predicted by Model 3.

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Figure 5.6. Model 3 prediction errors (%) per year

5.4. Overview

This section first reviews the hypotheses formulated in the previous chapters (Section 5.4.1). Then an overview of the main conclusions of this chapter is given and new hypotheses are formulated (Section 5.4.2).

5.4.1. Review of hypotheses

According to the results of the linear regression model for 0-11 aged pedestrian-car KSI victims (Model 3), the transition of 50 km/h roads into 30 km/h roads, which accelerated after 2000, decreased the number of 0-11 aged KSIs. This result is a clear support of Hypothesis H99-01.1.A.


Hypothesis H79-87.1.B, which states that the number of pedestrian-car KSI casualties in 1979, 1985, 1986, and 1987 was relatively low because of the severe winter weather in those years, is supported by the results of Models 1 and 2. The variable ExtrWinter, which represents the extreme 1979 winter, is included in both models and this variable is negatively correlated to risk. In January and February 1985, February 1986, and all first quarter months in 1987 frost temperature (variable Tfrost) is relatively low and this variable is in both models positively correlated to risk, i.e. low frost temperature is associated with low risk. The results of the linear regression analysis on the basis of the registered number of victims (Model 1) suggests that the introduction of the free public transport pass for students in November 1990 resulted into a decrease of pedestrian-car risk. This partly supports hypothesis H90-91.1.B, which assumes the influence of the beginning economic recession as well. Influence of economic variables is not included in the models. Hypothesis H00-02.1.B states that pedestrian-car KSI risk has decreased more rapidly than normal in 2000-2001 because of the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession. The influence of a part of the Sustainable Safe program, i.e. the transition of 50 km/h roads into 30 km/h roads, is acknowledged by the model for 0-11 aged (Model 3). No economic or enforcement variables were included in the models, although they were considered in the analysis (see Section 5.2.2). So, this hypothesis is partly supported by the model results. See also hypothesis H99-01.1.A. Hypothesis H76-04.4.B supposes that periods of (strongly lowered) economic activity (1981, 1990 and 2000) are associated with lower pedestrian-car risk. The linear regression analyses did not find additional prove for this hypothesis. The linear regression models of this chapter provide no information on the following hypotheses: H82-83.1.A, H76-04.1.A, H76-04.2.A, H76-04.3.A, and H90-91.2.C.

5.4.2. Main conclusions and new hypotheses

5.4.2.1. Research question 1: sensible model with economic and weather variables

In all its simplicity, the linear regression model 1 gives an interesting insight in how an explanatory model for road unsafety can look like. Furthermore, it demonstrates the possibilities to estimate the effects of measures by way of a model, as is illustrated by Table 5.3. We must underline the restrictions to the application of linear regression modelling to road safety time series. Because in time series the regression model residuals do normally not fully comply with important conditions as independence, homoscedasticy, and normality, the standard errors of the estimated coefficients are not fully reliable. Therefore, the test of significance of the model coefficients is neither completely reliable. Furthermore, there are a lot of uncertainties in road safety data which are not dealt with in the linear regression model. Therefore the analyses in this chapter are


explorative and their results indications of how interrelations in road safety may be. Furthermore, we do not pretend to have a full overview of all possible measures or developments that have influenced the occurrence of pedestrian-car crashes. This might lead to wrong interpretations of results in the sense that effects are allotted to the wrong measure or to one measure where it should have been allotted to two measures, etc. So, we must be careful with the interpretation of the results of these explanatory models. Ideally, we find similar results from different sources as descriptive analysis, literature scan, and modelling on the basis of which strong conjecture of the real interrelations arises. The linear regression results clearly support the statement that weather has significant influence on pedestrian-car risk. In the models weather is represented by four variables: maximum temperature, frost temperature, extreme winter conditions, and the fraction warm and dry days. We must underline the indicative character of this result. The outcomes of Model 2 suggest that the warm weather influence in the models is related to the larger number of holidays in summer. We think influence of weather has been proven, but what exact weather variables, both in theory and practice, most significantly affect pedestrian-car risk is a subject for further study, which we wholeheartedly recommend. On the basis of the above results we suggest the following hypothesis: H76-04.5.D: Extreme winter weather, severe frost, and the combination of

warmth and drought are associated with low pedestrian-car KSI casualty risk.

The regression results do not suggest influence of the considered economic variables, i.e. consumer confidence and unemployment, on pedestrian-car KSI risk. This, however, does not exclude the possible influence of economy on pedestrian-car risk. It could be that other economic variables have affected pedestrian-car risk or that specific groups of pedestrians or car drivers have been influenced by the economic developments. The linear regression analysis also indicates that since 2000 in August to October pedestrian-car risk is lower than in the same months in the years before. This could point to a positive effect of the campaign "Schools have started again". However, linear regression analysis based on the registered number of 0-11 aged victims does not support this conclusion. So, regarding this aspect further investigation is needed.

5.4.2.2. Research question 2: effect of using registered numbers instead of under registration-adjusted numbers

The regression model based on adjusted victim numbers (Model 2) is clearly different from the model based on registered victim numbers (Model 1). The differences regard the variables included in the model and the variable coefficients. Furthermore, adjusting for under registration introduces additional uncertainty to the model results.


The model on the basis of under registration-adjusted KSI numbers (Model 2) provided insight in the possible causes of the relatively very strong fall of the number of registered KSI victims from 2003 to 2004. In literature no real cause was found for the strong decline of the killed victims (Stipdonk, 2005). According to the model on the basis of registered KSI numbers (Model 1), some unknown change at the end of December 2003 or at the beginning of January 2004 relevantly influenced the number of registered victims. In Model 2, however, we found no relevant influence of this unknown January 2004 change. According to this second analysis the decline of registration level caused part of the 2004 fall of registered victims. Because registration level decreased by 10%, from 57% in 2003 to about 51% in 2004, this explains about half of the 20% fall of registered victims. We also noticed that in Model 2 the 2003 victim numbers are clearly underestimated. According to this model, most of the other half of the explanation of the 2004 fall of registered victims lies in the unexplainably high 2003 victim numbers. On the basis of these conclusions we formulated the following hypothesis: H03-04.1.D: The fall of registered pedestrian-car KSI victims from 2003 to

2004 is partly due to the drop of the registration level and partly due to one or more unknown factors which made pedestrian-car KSI victim numbers in 2003 relatively large.

This means that to explain the large difference in victim numbers between 2003 and 2004, we should rather focus on the high numbers in 2003 than on the low values in 2004. From the results of Model 1 and Model 2 we conclude that using registered numbers instead of under registration-adjusted numbers has influence on the estimated effect of explanatory variables. Still we do not think it is wise to use the adjusted numbers, because uncertainty in the registration level is high and furthermore registration level is not known or still more unreliable for conflict types and further subdivisions. On the basis of this result we formulated the following hypothesis: H76-04.6.D: Using registered victim numbers instead of 'registration-

adjusted' victim numbers in the analysis of crash risk has influence on the resulting description of the development of crash risk and the estimated effects of influence factors.

5.4.2.3. Research question 3: sensible model for subgroup (0-11 aged)

The linear regression model for 0-11 aged pedestrian-car KSI victims (Model 3) shows that specific factors have influenced risk development for this age group. One of those a specific factors is the transition of 50 km/h roads into 30 km/h roads, which accelerated after 2000. Furthermore, the model suggests that the national campaign “Lollipop Men” in September 2001 has decreased the number of KSIs in the month of the campaign. Indeed, the number of KSIs in that month is relatively small for a September month. However, we cannot exclude the possibility that other factors which are not included in the model may have caused this low value as well. For example, it might have been so that as a consequence of the 9-


11 terrorist attacks in New York children have been kept inside because of the threatening atmosphere in the country. A general problem with the analysis of subgroups, which is even stronger present in a model on month basis, is that the number of KSIs per time unit is small. Consequently, the observation error becomes relatively large, so that apparent changes can be just the effect of randomness. To cope with this problem of small numbers we should take care not to make subgroups too small and to take into account the observation error, as is done in state space analysis.


6. Discussion and recommendations

In the present report an attempt was made to understand and model the time series of exposure, crashes and risk for a particular crash type, i.e. pedestrian-car KSI crashes. First, to explore the background of modelled casualty risk, we performed descriptive analysis of pedestrian-car KSI casualty data to identify developments and factors which could influence the time series. Second, a literature and database study was performed to identify relevant road safety interventions that may have influenced pedestrian-car crashes. Third, state space models were used to model time series of pedestrian-car KSI casualties, exposure and KSI casualty risk. Fourth, we performed explorative linear regression analyses to get insight in (1) the possible influence of weather and economy on pedestrian-car KSI casualty risk, (2) the possible effect of using registered instead of underregistration-corrected casualty numbers, and (3) the specific factors affecting risk for 0-11 aged children. Since the approach and method used are new, the current study can be seen as feasibility study on the basis of which recommendations can be formulated for future work in this area. This chapter closes the report by providing an overview of hypotheses (in Section 6.1) and a discussion of main findings (in Section 6.2) and by discussing possibilities for further study in this field (in Section 6.3).

6.1. Overview of hypotheses

In this overview only the latest versions of the hypotheses are considered. The hypotheses are discussed in order of the first year of the period which they refer to. H76-04.1.A: Because of changes in pattern of life, e.g. the greater

participation of women in the labour market, pedestrian-car KSI casualty risk decreased more slowly for females than for males.

The descriptive analysis and the state space analysis showed less decline in female pedestrian-car KSI risk than in male risk. In the literature scan the growing participation of women in the labour market since the eighties was confirmed by national statistics. However, the model analysis showed that female risk decreased as fast as male risk until 2001 and increased in 2002 after which it decreased again. This result contradicts the hypothesis and makes 2002 a main year of interest with respect to describing and explaining the differences between female and male pedestrian-car KSI casualty risk. H76-04.2.A: Young car drivers are compared to their part in road traffic

relatively often involved in pedestrian-car crashes and other crashes with car as opponent.

The statement in this hypothesis is supported by data, by literature, and by experts. It is a logical statement, because young car drivers are relatively


inexperienced drivers and they more often than others show reckless behaviour on the road. H76-04.3.A: The consequent application of new, safe city and road design

principles in Flevoland has resulted in considerable lower pedestrian-car KSI casualty risk than in the other provinces.

This hypothesis was based on the comparison of pedestrian-car KSI casualty risk among provinces, which was made in the descriptive analysis. The literature scan provided arguments in favour of this hypothesis in the shape of estimates of risk-decreasing effects of specific safe road design principles as the construction of 30 km/h zones and roundabouts. H76-04.4.B: Periods of (strongly lowered) economic activity (1981, 1990

and 2000) are associated with lower pedestrian-car KSI casualty risk.

Comparison of the KSI data and economic data and results of Australian studies and the moped-car study (Blois, Goldenbeld and Bijleveld, 2007) suggest this hypothesis. For a more substantial motivation, we wait for the results of a thorough study of the influence of economic development on mobility and risk (Wijnen, in prep.). H76-04.5.D: Extreme winter weather, severe frost, and the combination of

warmth and drought are associated with low pedestrian-car KSI casualty risk.

H79-87.1.A: The number of pedestrian-car KSI casualties in 1979, 1985,

1986, and 1987 was relatively low because of the severe winter weather in those years.

The relatively low pedestrian-car KSI casualty risk in the winters of 1979, 1985, 1986, and 1987, which is confirmed by the descriptive analysis and the state space analysis, in combination with the fact that those winters were very cold, are a clear indication of the plausibility of this hypotheses. Further support is provided by the results of the linear regression analysis with weather variables. Concluding: as different analyses support this hypothesis, we can state that these hypotheses are very probably true. H76-04.6.D: Using registered victim numbers instead of 'registration-

adjusted' victim numbers in the analysis of casualty risk has influence on the resulting description of the development of casualty risk and the estimated effects of influence factors.

Comparison of two linear regression models, one on the basis of registered KSI numbers and one on the basis of adjusted numbers, showed differences in the description of casualty risk, which led to the formulation of this hypothesis. However, because uncertainty in the registration level is high and furthermore registration level is not known or still more unreliable for conflict types and further subdivisions, we do not think it is wise to use the adjusted numbers for modelling casualty risk. H82-83.1.A: The considerable decrease of pedestrian-car KSIs on urban

intersections and the considerable increase on urban road


sections both in 1982-1983 were caused by a change in the definition of intersection and road section pedestrian-car crashes.

This hypothesis was proven by the analysis of disaggregated data (see Section 2.3.1). H90-91.1.B: Pedestrian-car KSI risk has decreased more rapidly than

normal in 1990-1991, because of the introduction of the free public transport pass for students and the beginning of economic recession.

H90-91.2.C: The introduction of free public transport for students in

November 1990 and the start of economic recession caused a shift of car trips to public transport for the age group 12-24. Due to this, the number of pedestrian trips for age group 12-24 increased, which compensated the risk decrease as described in hypothesis H90-91.1.B.

With state space analysis we found a significant trend break in pedestrian-car KSI risk for all age groups, except the 12-24 aged, around 1990. The literature scan found the following two most probable explanations: 1. the introduction of the free public transport pass for students, through

which many young, inexperienced people exchanged car by public transport;

2. the start of a period of low economic activity and increase of unemployment, which must have resulted into less car use during the rush hours;

3. the increase of pedestrian kilometres by 18-24 aged, which probably annuled the risk-decreasing effect of (1) for the 12-24 aged.

The linear regression model on the basis of registered victims includes a trend break variable representing the introduction of this public transport pass which is negatively related to pedestrian-car KSI casualty risk. To conclude: to our opinion we have collected sufficient arguments to make these hypotheses plausible. H99-01.2.A: The transformation of more and more 50 km/h roads into 30

km/h roads as part of the Sustainable Safe program, which started in 1997, resulted in the decrease of pedestrian-car KSI victims of 0-11 years old.

In the descriptive analysis, we found a considerable decrease of 0-11 aged pedestrian-car KSIs in 2000 and 2001. The state space analysis showed a significant downward trend break in pedestrian-car KSI risk for 0-11 aged in 2000. In literature we found that the Sustainable Safe program, with a lot of infrastructural measures as the construction of 30 km/h zones, started in 1997 and probably had its strongest effects after 2000. The latter was confirmed by the development of KSI numbers on 30 km/h roads in relation to those on 50 km/h roads. In the linear regression for 0-11 aged KSIs the influence of the construction of 30 km/h zones was confirmed. All together, we think there is sufficient evidence for the plausibility of this hypothesis.


H00-02.1.B: Pedestrian-car KSI casualty risk has decreased more rapidly than normal in 2000-2001 because of the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession.

In the literature scan the three mentioned developments were found as possible factors which could result into considerable risk decrease from 2000. However, the state space analysis found a significant downward trend break of risk only for 0-11 aged children. As such, the hypothesis is not supported by the model results. H04-04.1.D: The fall of registered pedestrian-car KSI victims from 2003 to

2004 is partly due to the drop of the registration level and partly due to one or more unknown factors which made pedestrian-car KSI victim numbers in 2003 relatively large.

The 20% fall of registered pedestrian-car KSI victims from 2003 to 2004 reduced to a 10% fall when the adjusted numbers were considered. An explanatory linear regression model on the basis of the adjusted numbers could well predict the 2004 KSI numbers but clearly underestimated the 2003 numbers, which is an indication of relatively large KSI numbers in 2003.

6.2. Main conclusions

Pedestrian-car risk has considerably decreased in the period 1978 to 2003, from 153 to 29 casualties per million inhabitants. Pedestrian-car risk has already shown a strong decrease in the early and mid-seventies when the level of implementation of formal road safety measures directed at pedestrian safety has been quite low. The decrease in pedestrian-car risk in the seventies cannot likely be explained by road safety measures, and more likely has to be explained in terms of generational learning or informal social protective mechanisms. The differences between pedestrian-car risk for different age groups have over time considerably closed and are quite small after 2000. The decrease in risk for young pedestrians (age 0-11 years) already started in the seventies before large-scale infrastructure measures were implemented. A combination of supportive social mechanisms, such as voluntary traffic guards, increased collective safety awareness in the seventies, and increased attention by schools and municipalities themselves to the safety of school environment may have contributed to the decrease in the seventies. In the eighties and nineties, the new city planning constructed safer neighbourhoods where walking and driving space were more separated from another. In the late nineties and after 2000, the Sustainable Safe program, the continued work of the voluntary traffic guards, and the action campaign "Schools have started again", presumably have contributed to further risk decrease for young children. The risk decrease for the elderly (75+) starting from the seventies is also remarkable. Again we have to state that this decrease already occurred before infrastructural measures or changes in city planning could have had much effect on pedestrian safety. The most general alternative explanation


for the risk decrease is that the elderly have become more healthy and vital, and therefore less vulnerable or incapacitated in traffic. Starting in the late nineties, the elderly have also increasingly used walking support means such as the well-known rollator. For several subdivisions of pedestrian-car crashes, the most frequently found trend break years have been 1986 and 1991. The trend break in pedestrian-car risk in 1991 is most likely due to combined influences of the introduction of the public transport pass for students and economic recession. With regard to changes in the seasonal component of risk, the results show that starting in the late nineties, differences in the for general trend corrected risk between specific seasons tend to become larger. In particular, the risk differences between winter and summer has tended to become larger. When over time, risk differences between seasons show a trend toward larger or smaller differences, the first type of explanations that comes to mind is general changes in climate that may affect seasonal weather and consequently choices and behaviour in traffic. As a result of global warming, especially winter temperature will increase, more rain will fall in both summer and winter, and there will be more cases of heavy rainfall. It can be hypothesized that especially in winter both drivers and pedestrians have difficulties to adapt to these changing weather circumstances. A second type of explanation would be a general change in lifestyles and time use (including vacation planning) that may have a particular bearing on choices in traffic in particular seasons.

6.3. Recommendations

Section 6.3.1 contains a discussion about steps to be taken to arrive at explanatory models including effects of weather and economy. Section 6.3.2 discusses our choice of exposure measure and recommends two alternative measures. Section 6.3.3 describes the prospects of comparative analyses. Finally, Section 6.3.4 concludes this chapter with some briefly-worded recommendations.

6.3.1. Steps towards explanatory models

The state space models fitted in the present study include no explanatory variables. In this research we have assumed that both weather and economy have an indirect effect on risk through influence on amount and type of mobility of specific user groups and in the case of weather also a direct influence on risk through factors that directly affect circumstances surrounding a crash. In the case of weather, we found disruptions in the risk pattern in 1979 and 1985-1986 that very likely have to do with the exceptional winter periods in those years. In the linear regression analyses, which was applied to monthly data, several weather related variables as frost temperature, fraction of warm and dry days, the extreme winter circumstances in 1979, and the length of daylight were included as significant variables in the model. In the case of economy, for one particular year of economic recession, 1990, we found that there was a downward trend break in pedestrian-car KSI casualty risk. For another year of starting economic recession, 2000, we


found no such effect for this particular crash group, but for another crash group, moped-car, such an effect was found (Blois, Goldenbeld and Bijleveld, 2007). This presumed association between extreme values of one variable (harsh winter, bad economy) and extreme values on another variable (risk) is only 'visible' for a few particular years. We have put 'visible' between quotes since this association has not been formally tested but is assumed. It can be assumed that economical variables have different effects on mobility and risk dependent on day of the week (workday versus weekend day), time of day (rush hour versus non-rush hour), and age group of drivers. Also, the extent to which particular risk groups, such as young drivers, are affected by economical variables may be different per recession. An economic recession that starts out with a high level of youth unemployment may have different consequences for particular accident groups than a recession that starts with an impact on elderly working population. Thus the presumed association between trend breaks in particular years and some aggregate variables such as 'harsh winter' or 'start of economic recession' does not constitute enough knowledge to simply include these variables into a model. Clearly, the knowledge of road safety experts about the relationship between economical activity and traffic risk or between state of the weather and traffic risk, is far less than, say, our knowledge about the relationship between drink driving and traffic risk or between speeding and traffic risk. Second, even if we would know approximately as much about the relationship between economical variables and traffic risk, even then we could not just simply add an economical variable to our structural time series model. In testing out state space models on accident risk, Bijleveld and Commandeur (2005) introduced 'proportion of drink driving' and 'proportion of days with wet weather' as explanatory variables in a model about risk of one-sided accidents. Doing this, they found no relationship between risk and drink driving and they found, contrary to expectation, an increase in risk as the proportion of days with wet weather decreases. This example shows that just adding a simple continuous variable to structural time series models does not automatically lead to results that can be expected according to theory and evidence. An alternative method of including these two variables was based on a division of total exposure according to the states of the explanatory variable (e.g. exposure wet weather versus dry weather) and to model the observed number of accidents as a product of exposure in wet weather conditions and the risk in wet weather conditions. The observed number of one-sided accidents is then a function of the product between relative exposure (total exposure times proportion of exposure when wet) and comparative risk (risk times the factor to explain accidents KSI that occurred in wet weather conditions). According to this model an increased risk was found for wet weather conditions. In order to add economic or weather variables to structural time series analysis, we need to know more about the pathways through which these variables reach their effects, and then we need to understand better how we can attune our models to incorporate these pathways.


6.3.2. Choice of exposure measure

We selected population size as measure of the exposure to pedestrian-car KSI casualty risk on the basis of considerations of representativeness for the presence of pedestrians and cars in the same area and time, data availability over 1976-2004, data accuracy, comparibility to other crash type studies, and the possibility to create subdivisions. Main advantages of this choice are the good availability and accuracy of the population numbers. Main drawbacks are its rigidity with respect to changes in the mobility other than caused by population growth, e.g. seasonal effects, the restricted possibilities of subdividing, and the lack of comparibility to other crash type studies in which the product of vehicle kilometres was used as exposure measure. In principle, measures as 'pedestrian kilometres times car kilometres inside urban area' and 'number of trips times car kilometres inside urban area' are better with respect to representativeness, comparability, and the possibility to create subdivisions, but their availability and accuracy is insufficient. We would especially recommend to find a method to reliably determine the car kilometres inside urban area, which would make the use of these alternative measures as proxy variables for exposure to pedestrian-car risk much more attractive.

6.3.3. Prospects of comparative analyses

Only one crash type, pedestrian-car crashes, was considered in this report. Other, related crash types as bicycle car and pedestrian-lorry can be assumed to share underlying causative patterns with pedestrian-car. Therefore, we recommend to perform comparative analyses of the risk of several, related crash types so as to improve our insight in risk development and its determinants. In the state space analysis, we can use the concept of 'comparative' or 'relative' risk for such comparative analyses. This risk refers to how one smoothed trend of risk (or crashes) over time differs from another trend of risk (or crashes) over time. The comparative risk is to be understood as the way this difference evolves over time. This concept of comparative risk offers the opportunity to perform risk analyses without using exposure data. In that case it is essential that the analysis uses comparison groups that have similar exposure over time so that the differences in trends of crashes reflect the differences in risk rather than those in exposure. More generally, common factor models could be used to investigate joint developments in risk and exposure of the different crash types. The correlation of those developments among crash types, which is commonly observed, can well be exploited by these common factor models. The recent application of the common factor approach to road safety in state space environment (Gould et al., in prep.) is a promising development for the future.


6.3.4. Other recommendations

We found clear changes in seasonal pattern of pedestrian-car KSI casualty risk and differences in seasonal pattern between male and female, age groups, inside and outside urban area, and intersections and road sections and slight changes between working day and weekend. As literature did not provide plausible explanations of these changes and differences, we recommend to further investigate these seasonal developments in order to arrive at a well-underpinned description of the underlying factors. We used the registered number of casualties in the model analyses. However, changes in the registration level can affect the description of casualty risk development as well as the estimated influence of interventions or social developments on casualty risk. Therefore, the possibility of incorporating this possible effect should be studied. The analysis of casualty data showed that injury severity decreased from half seventies to end nineties, on both intersections and road sections. This decrease is strongest for the younger age groups (0-50). Since end nineties, injury severity has steadily increased again. Possible explanations can be looked after in the area of technical improvement of cars (e.g. ABS), better checks on maximum speed in urban areas, improvement of health care, etc. The increase of injury severity after 1998 might be explained by the use of more and more heavy cars (SUVs), increase of efficiency in health care, diminishing registration of less severe injury accidents, etc. We recommend to further investigate the background of these developments so as to get more insight in the possible explanations.


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Vis, A.A. & Kaal, I (1993). De veiligheid van 30 km/uur gebieden; Een analyse van letselongevallen in 151 heringerichte gebieden in Nederlandse gemeenten. R-93-17. SWOV, Leidschendam. (http://www.rws-avv.nl/verkeersveiligheid/ rapporten/bromfietsoprijbaan.html) Wegman, F.C.M., Dijkstra, A., Schermers, G. & P. van Vliet (2005). Sustainable Safety in the Netherlands: the Vision, the Implementation and the Safety Effects. 3rd International Symposium on Motorway Geometry Design. Wijnen, W. (in prep.). Omgevingsverkenning Economie en Verkeersveiligheid. Report (in Dutch with English Summary). Institute for Road Safety Research SWOV, Leidschendam.


Appendix 1 Database sources

The database scan produced several documents on national statistics. Below the references to these documents are presented: AVV, Transport Research Centre, Rotterdam. AVV, Gebruikershandleiding Nationaal Wegenbestand. AVV Transport Research Centre, Rotterdam, 2003. CBS, Statline databank. Statistics Netherlands, Voorburg/Heerlen. CBS, Het bezit en gebruik van bedrijfsvoertuigen 1993. Statistics Netherlands, Voorburg/Heerlen, 1994. CBS, Bevolking der gemeenten van Nederland op 1 januari 2000. Statistics Netherlands, Voorburg/ Heerlen, 1996. CBS, Statistiek van het binnenlands goederenvervoer 1997. Statistics Netherlands, Voorburg/Heerlen, 1998. CBS, Statistiek van het personenvervoer 1999. Statistics Netherlands, Voorburg/Heerlen, 1999. CBS, De mobiliteit van de Nederlandse bevolking in 1998. Statistics Netherlands, Heerlen/Voorburg, 1999b. CBS, Maandstatistiek verkeer en vervoer. Statistics Netherlands, Heerlen/Voorburg, 1999c. CBS, Statistiek van de motorvoertuigen 1 januari 2000. Statistics Netherlands, Voorburg/Heerlen, 2000. CBS, Statistiek van het Nederlandse motorvoertuigenpark. Statistics Netherlands, Voorburg/Heerlen, 2001. CBS, Onderzoek Verplaatsingsgedrag 2001. Documentatie voor tape-gebruikers. Statistics Netherlands, Heerlen/Voorburg, 2002. CBS, Onderzoek Verplaatsingsgedrag 2003. Documentatie voor tape-gebruikers. Statistics Netherlands, Heerlen/Voorburg, 2004. CBS, Nationale rekeningen 2004. Statistics Netherlands, Voorburg/Heerlen, 2004. De Blois, C.J., Modellen voor mobiliteitsprognoses: Ten behoeve van Verkeersveiligheidsbalansen en –verkenningen, in het bijzonder Modelontwikkeling. Internal memo, SWOV Institute for Road Safety Research, Leidschendam, 2005.


Hendrikx, F.W.M., "Het meten van de mobiliteit van de Nederlandse bevolking" in CSB, Maandstatistiek verkeer 1989/1. Statistics Netherlands, Heerlen/Voorburg, 1989. Kadrouch, S. en Moritz, G., Redesign onderzoek verplaatsingsgedrag OVG: verschillen tussen het onderzoek verplaatsingsgedrag OVG en het Neu KONTIV Design NKD. Statistics Netherlands, Heerlen, 1998. NAP, Nationale AutoPas, Schiphol-Triport. NWO, WSA – Catalogus databestanden. Netherlands Organisation for Scientific Research (NWO), Den Haag, 2004. Swinkels, H.A.M. en Konen, H.J., Trendbreukanalyse Onderzoek Verplaatsingsgedrag. Statistics Netherlands, Heerlen, 2002. SWOV, Kennisbank. SWOV Institute for Road Safety Research, Leidschendam, 2003.


Appendix 2 Overview of results of descriptive analysis

Legend of the tables on the following pages: '-' / '+' = significant decline / increase, confidence 95%; '--' / '++' = significant decline / increase, confidence 99%; '---' / '+++' = significant decline / increase, confidence 99.9%; NA = no (valid) data available.

SWO

V pu

blic

atio

n A-

2006

-4 C

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entia

l

177

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V In

stitu

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r Roa

d S

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y R

esea

rch

- Lei

dsch

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m, t

he N

ethe

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s

Pede

stria

nPe

dest

rian

Pede

stria

nB

icyc

leC

arPe

dest

rian-

Car

Pede

stria

n-C

arPe

dest

rian-

Car

Car

Mop

ed

Bic

ycle

Car

Car

Dea

dH

ospi

tal

Slig

htly

in

jure

d U

rban

Rur

al50

km

/h

80 k

m/h

10

0 +

120

km/h

Con

trib

utio

n (%

)47

02

2533

538

5785

1481

132

Ove

rall

tren

d (%

)-6

,0-6

,1-3

,6-3

,4-2

,4-3

,4-5

,9-6

,6-5

,9-5

,5-6

,4-6

,7--

1976

1977

+++

1978

1979

---

---

---

---

---

---

---

----

---

-19

80++

++

1981

---

+19

82--

---

---

--19

83++

+--

1984

---

---

--

----

1985

---

---

---

--

--

1986

+19

87-

-++

---

---

1988

---

+++

1989

--++

+-

--

1990

---

1991

---

---

--

---

-19

92-

-++

-++

1993

+--

--19

9419

95-

---

-19

96-

1997

--

--

-19

98--

-++

1999

-+

2000

--20

01-

-20

0220

03-

---

2004

----

---

---

----

---

2005

178

SW

OV

publ

icat

ion

A-20

06-4

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fiden

tial

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V In

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te fo

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ethe

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s

Pede

stria

n-C

arPe

dest

rian-

Car

, int

erse

ctio

nPe

dest

rian-

Car

, int

erse

ctio

nPe

dest

rian-

Car

, int

erse

ctio

nPe

dest

rian-

Car

, roa

d se

ctio

n

Inte

r-se

ctio

nR

oad

sect

ion

Dea

dSe

rious

ly

Inju

red

Slig

htly

Inju

red

Urb

anR

ural

On/

near

pe

dest

rian

cros

sing

O

ther

cro

ssin

g U

rban

Rur

al

Con

trib

utio

n (%

)72

284

3660

666

355

235

Ove

rall

tren

d (%

)-6

,2-5

,3-6

,5-5

,2-3

,1-5

,1-6

,3-4

,9-0

,1-6

,2-5

,119

7619

7719

7819

79--

---

--

---

---

---

---

1980

+++

1981

1982

--

1983

+++

---

+++

+++

+++

+--

-19

84--

--19

85--

--

----

--

1986

1987

-+

1988

+19

89-

-19

90--

--

1991

-19

92-

+19

93--

-19

94-

--

1995

1996

++

1997

--19

98-

--19

99-

2000

--20

0120

0220

0320

04--

---

2005

SWO

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2006

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l

179

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te fo

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rch

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m, t

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s

Pede

stria

n-C

arPe

dest

rian-

Car

Pede

stria

n-C

ar

Win

ter

Sprin

gSu

mm

erAu

tum

nM

on-T

huFr

i-Sun

22.0

0-7.

00

7.00

-9.0

0 9.

00-1

3.00

13

.00-

16.0

0 16

.00-

19.0

0 19

.00-

22.0

0

Con

trib

utio

n (%

)27

2520

2857

4312

516

2232

14O

vera

ll tr

end

(%)

-5,6

-5,8

-6,5

-6,1

-6,2

-5,7

-5,2

-5,2

-6,9

-6,0

-6,4

-4,9

1976

1977

+19

78-

1979

---

---

---

---

---

1980

+++

---

1981

+-

1982

---

---

1983

--

1984

---

--

-19

85--

--

---

1986

++

1987

---

-19

88+

1989

1990

--19

91-

--

--

1992

+-

+19

93-

--

1994

1995

-19

96+

1997

--

-19

98-

+19

9920

00-

2001

-20

0220

03-

-20

04--

---

-20

05

180

SW

OV

publ

icat

ion

A-20

06-4

Con

fiden

tial

SWO

V In

stitu

te fo

r Roa

d S

afet

y R

esea

rch

- Lei

dsch

enda

m, t

he N

ethe

rland

s

Pede

stria

n-C

ar, v

ictim

Mal

eFe

mal

e0-

1112

-17

18-2

425

-39

40-5

960

+M

ale

Fem

ale

18-2

425

-29

30-3

940

-49

Con

trib

utio

n (%

)60

4038

87

1012

2678

2026

1623

15O

vera

ll tr

end

(%)

-6,5

-5,9

-7,9

-6,0

-5,0

-3,5

-4,1

-5,4

-6,6

-3,9

-7,4

-7,0

-5,9

-5,1

1976

1977

1978

1979

----

---

---

---

--

----

1980

1981

-+

1982

---

----

1983

--

1984

--

--

---

1985

--

---

---

-19

86++

++19

87-

--

1988

1989

--

---

-19

90++

--19

91-

--

1992

-+

+-

-19

93-

--19

94-

1995

-19

9619

97--

--

---

1998

1999

2000

--+

--20

01--

-20

02-

+20

03-

2004

---

---

--20

05

Pede

stria

n-C

ar, c

ar d

river

Pede

stria

n-C

ar, c

ar d

river

Pede

stria

n-C

ar, v

ictim

SWO

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2006

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stitu

te fo

r Roa

d S

afet

y R

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Car

Pede

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ar

No

alco

hol

Alco

hol

Day

light

D

ark

- with

st

reet

lig

htin

g D

ry -

dry

Dry

- w

et

Rai

n - w

et

Con

trib

utio

n (%

)91

966

2471

1412

Ove

rall

tren

d (%

)-5

,9-5

,5-6

,2-5

,3-5

,9-6

,5-5

,619

7619

7719

7819

79--

---

--

---

----

1980

-19

81-

++19

82--

----

---

1983

1984

----

-19

85--

--

---

1986

++

1987

--

1988

----

+++

+19

89--

-+

----

-19

90-

1991

--

-19

92-

--+

1993

1994

1995

----

1996

1997

--

1998

-++

1999

2000

2001

2002

2003

--20

0420

05


Appendix 3 Scan of interventions

Year Date Category Measure Relevance for pedestrian-car crashes1

1975 1-feb Mopeds Introduction of helmet wearing law 0

1976 15-sep General Recognition of home zones 0 / +

1977

16-apr

Cars Parking lights forbidden in urban area, dimmed headlights in bad weather circumstances during the day

0 / +

1978 1-apr

Trucks and public buses

Regulation governing driving hours trucks: Tachograph 0

1978

1-apr


On the sides of long vehicles, trailers and semi-trailers, approved of orange reflectors should be installed

0

1978 1-okt Cars Yellow licence plates 0

1979 1-nov Bicyclists Reflectors on pedals 0

1979 1-nov Bicyclists Red approved back-reflector (nu ook gele toegestaan) 0

1979 1-nov

Mo- and slopeds

Red reflectors on back and on pedals 0

1979 Cars Fog lamps allowed at sight less than 50 metres 0

1984

15-jun

Mo- and Slopeds

Equipment requirements relaxed (pedals not compulsory; eel size allowed)

0

1984 Alcohol Introduction electronic breath testing devices for selection

of alcohol suspects on the street 0/+

1985 1-sep

Motorcycles Driving exam A elaborated and level raised, training licence lapsed

0

1985 General Gradual transition from selective to a-select police checks 0/+

1985 15-jul Cars MOT test for all cars 10 years and older 0/+

1986 1-jul

Driving requirements

Theory-exam driving licence possible at 17 years 0

1986 1-jul

Driving requirements

Duration of driving licence till 70 years 0/+

1986 General Start publicity campaigns VVN en WVC 0/+

1987 1-jan Bicyclists Side reflection on wheels 0

1987 1-jan Mo- and

slopeds Side reflection on wheels 0

1987 1-jan Cars MOT test for all cars from3 years old +

1987 1-okt Alcohol Introduction breath-analysis for evidentiary purposes 0/+

1987 General Introduction of a national quantitative road safety target 0

1988 General Introduction tit-for-tat policy for light offenders 0

1988

1-mei Speed limit motorways cars from 100km/u to 120km/u on part of motorways combined with special enforcement and publicity

0

1988 General Introduction special transport for visitors discos/café's 0/+

1990 1-jan Cars Presence of seat belt at the back of a car obliged 0



1990 1-sep

General Introduction administrative law for minor traffic violations (The Mulder Act)

0

1990 1-okt

General Introduction public transport pass for students 17 years and older who have an university grant

0/+

1991 General Further extension tit-for-tat policy 0/+

1991

1-nov

General Revision Traffic Code ( Motorway Code) RVV and decision administrative regulations road traffic BABW

0

1992 Alcohol More severe penalties for drink driving 0/+

1992 1-apr


Compulsory seat belt usage (if present) 0

1992 1-apr

Cars Wearing seat belt in the back of the car obligatory (if present)

0

1993 Driving

requirements Revision of Motor Vehicle Driver Instruction Code 0

1994 1-nov General New Vehicle Instruction 0

1994 1-jan

Speed Speed limiter: Trucks over 12 ton, public buses over 10 ton in newly sold vehicles

0

1994 May/June

General National street play day 0/+

1994

General Limitation public transport pass for students: choice between weekends and working days

0/+

1994 Cars Third braking light on new cars 0

1995 1-jan Driving

requirements Revision and extension of Motor Vehicle Driver Instruction Code

0

1995 1-jan


Compulsory side-underrun-protection for new trucks 0

1995 1-jan Speed Speed limiter: Trucks over 12 ton, public buses over 10 ton for all new vehicles

0

1996 General Start project 'Safe on the road'; to teach children from primary school how to take into account trucks in traffic

0

1996

1-jan

Speed

Speed limiter: Trucks over 12 ton, public buses over 10 ton for vehicles registered after 1 jan. 1988 .

0

1996 Driving

requirements Introduction 'Regulation measures driving competence and driving suitability'

0

1996 juni

Alcohol New Adm. Requisition procedure; e.g. EMA (Educative Measure Alcohol and Traffic)

0

1996 1-jun Driving

requirements Moped licence; obligatory theory exam for moped and sloped riders

0

1996 Cars Campaign 'Prevent neck injury'; advise on proper use of

head support 0

1997 1-jan

Cars

Part III on front window abolished (introduced 1 febr. 1975) 0

1997 Cars Euro-NCAP 0

1997 General Start program Sustainable Safety +

1997 9-jun Trucks en

public buses Overtaking prohibition for truck on motorways on a number of working days

0



1997 Cars EU Head restraint heigth of 75 cm heigthened. (Head

restraint use is not obligatory) 0

1997

1-jan Mo- and Slopeds

Revision Traffic Code (RVV) on behalf of introduction of one-seat car with moped engine (e.g. minimum speed limit 50km/uur on motorways)

0

1998 1-jan Trucks en

public buses Presence seatbelts in new company vehicles 0

1999 Driving

requirements Driver Training Stepwise (in Dutch abbreviated as RIS) 0

1999 15-nov Mo- and

Slopeds Campaign 'Mopeds more visible more safe' 0

1999 26-aug General Campaign use hands free phone 0/+

1999 6-okt General Statement of the the High Council (Hoge Raad): driving at

a footpace means no faster than15 km/hrs 0

1999 15-dec Mo- and

slopeds MOR (Moped On the Roadway) inside urban areas moped from bicycle path to roadway on 50km/h roads

0

1999 Mo- and

slopeds RVV: helmet use: fit well, well on head by closure 0

1999 General Introduction of regional traffic enforcement teams +

1999 Alcohol BAC limit lowered to 0,2 promille for beginning drivers 0/+

1999 Mo- and

slopeds EU-regulation 97/24/EEG; against tuning up of mopeds 0

2000 General Information campaign 'Schools have started again' +

2000

General Campaigns directed at the use of secondary safety devices such as seat belts, child restraints systems, moped helmets and bicycle helmets for children

0

2000 1-feb Cars New licence plates with unique codes for all new

registered motor vehicles 0

2000

1-sep Trucks and public buses

Covenant Action plan Blind Angle; intention to equip as much as possible trucks with sight area improving systems, on voluntary basis

0

2001 Cars ACC 0/+

2001 Cars ABS 0/+

2001 Cars Navigation systems 0

2001 Trucks and

public buses Damage monitor for transport companies (precursor of safety scan (2004))

0

2001 Trucks and

public buses Rollover warning system trucks; 6 weight measurement loops installed on the road for detection of overloading

0

2001 Trucks and

public buses Safe loading trucks 0

2001 11-sep Pedestrians National campaign ‘Lollipop Men’ +

2001

1-okt Trucks and public buses

The RAI commits itself to persuade Dutch manufactures and importers to equip all new truck above 3500 kg with a dobli-mirror

0

2001 dec Alcohol Alcohol campaigns combined with structural, intensive

police enforcement +

2001 mei General right-of-way for cyclists and mopedists coming from right 0



2002 1-jan Vans Vans with a grey registration plate do no longer need blindeeside window on the right (introduced in 1993)

0

2002 2-jan Trucks and

public buses Extension of the overtaking prohibition for truck (now total 1011 km)

0

2002 mrt.april Driving

requirements Introduction special driving licence for beginners 0

2002 1-sep Trailers Owner registration number for pulled vehicles (trailers and semi-trailers) heavier than 750 kg. Incl. max. allowed freight

0

2002 30-Mar General Prohibition of using hand held mobile phone during driving

car or moped 0/+

2002

Mar/April General Regarding prohibition of using hand held mobile phone during driving car or moped, phoning hands free allowed; campaign hands free phoning

0

2002 30-mrt Driving

requirements Provisional driving licence 0

2002 Driving

requirements Plan 17; to change moped licence and way to get it 0

2002 General Campaigns aimed at keeping distance 0 1 0 = no relevance; 0/+ = potential small relevance; + = potential relevance; ++ = large relevance.


Appendix 4 Ranking of interventions

Year Month Dur-ation

Cate- gory

Measure/development 75-80 81-85 86-90 91-95 96-00 01-05

1970? A VRIs ++++ ++++ + + + +

1975? V Crosswalks ++++ ++++ ++ + + +

1976 15-9 A Legal status woonerf-concept ++ + 0 0 0 0

1977 16-4 PA Stadslicht bibeko verboden, dimlicht overdag bij slechte weersomstandigheden

++ + + + + +

1977 1987 Subsidy arrangement BREV ++ + 0 0 0 0

1979 Handbook Trafic Accident Concentrations (AVOC) +++ ++ + + + +

1980 A Roundabouts + ++ ++ ++ +++ ++

1984 Alc Introduction electronic breath testing devices for selection of alcohol suspects on the street

0 0 + + + +

1985 AG Gradual transition from selective to a-select police checks

0 0 + + + +

1985 15-7 PA MOT-test for all cars 10 years old and older 0 0 + + + +

1986 1-7 RE Duration of driving licence till 70 years 0 0 0 0 0 0

1986 A Start publicity campaigns VVN/ WVC +++ ++ + + + +

1987 1-1 PA MOT test for all cars from3 years old 0 ++ ++ ++ ++ ++

1987 1-10 Alc Introduction breath-analysis for evidentiary purposes

0 0 + + + +

1988 A Introduction tit-for-tat policy for light offenders 0 0 0 0 0 0

1988 A Introduction special transport for visitors discos/café's.

0 0 0 0 0 0

1990 1-9 A Introduction administrative law for minor traffic violations (The Mulder Act)

0 0 0 + + +

1990 1-10 A Introduction public transport pass for students 17 years and older who have an university grant

0 0 0 0 0 0

1991 AG Verdere uitbreiding lik-op-stuk beleid 0 0 0 + + +

1991 1-11 AG Herziening RVV en BABW (Besluit Administratieve Bepalingen inzake het wegverkeer)

0 0 0 + 0 0

1992 Alc Verzwaring straffen voor rijden onder invloed 0 0 0 0 0 0

1993 RE Herziening Wet Rijonderricht Motorvoertuigen 0 0 0 0 0 0

1992-1993

Handbook Approach dangerous situations (HAGS) 0 0 0 + + +

1994 1994-heden

AG Road Play Day (in most cities in June) 0 0 0 0 0 0

1994 1-11 AG New Vehicle Instruction 0 0 0 0 0 0

1994 1-11 AG Limitation public transport pass for students: choice between weekends and working days

0 0 0 0

1996 AG Start project 'Safe on the road'; to teach children from primary school how to take into account trucks in traffic

0 0 0 0 + +

1996 RE Introduction 'Regulation measures driving ability and suitability'

0 0 0 0 0 0

1996 juni Alc New administrative claiming procedure; a.o. educative measure alcohol and traffic (EMA)

0 0 0 0 0 0

1997 1997 - heden

PA Euro-NCAP 0 0 0 0 + +


Year Month Dur-ation

Cate- gory

Measure/development 75-80 81-85 86-90 91-95 96-00 01-05

1997 AG Start Program Sustainable Safe 0 0 0 0 ++++ ++++

1999 Increasing use of mobile phones in the car 0 0 0 0 - -

1999 i.i.g. 1999 – heden

RE Driving education in steps (RIS) 0 0 0 0 0 0

1999 26-8 26-8-1999 - 31-7-2000

AG Campaign handfree phoning 0 0 0 0 0 +

1999 6-10 AG Judgement High Council (Hoge Raad): driving at a footpace means not faster than 15 km/h

0 0 0 0 0 0

1999 AlG Introduction regional (police) teams for traffic enforcement

0 0 0 0 0 +++

1999 AG BAG limit lowered to 0,2 permile for starting car drivers

0 0 0 0 0 +

2000 i.i.g. 2000 - heden

AG Information campaign 'Schools have started again'

2001 i.i.g. 2001 - heden

PA ACC 0 0 0 0 0 0

2001 i.i.g. 2001 - heden

PA ABS 0 0 0 0 0 +

2001 i.i.g. 2001 - heden

PA Navigation systems 0 0 0 0 0 +

2001 dec i.i.g. 2001 - heden

Alc Alcohol campaigns in combination with structural, strong police surveillance

2002 mrt.april 2002 AG Regarding prohibition of using hand held mobile phone during driving car or moped, phoning hands free allowed; campaign hands free phoning

0 0 0 0 0 0

2002 30-3 RE Provisional driving licence 0 0 0 0 0 0

2003 RE 'New driving' style educated by driving schools 0 0 0 0 0 0

2003 juni AG Special law 'Road widening' 0 0 0 0 0 0

2003 aug RE Use driving simulator at driving education 0 0 0 0 0 0

2003 2003 - 2007

AG Campaign 'De Rode Draad'; Long-range plan road safety campaigns

0 0 0 0 0 0


Appendix 5 Smoothed model states 'Total'

1980 1990 2000

2.0

2.5

3.0

3.5 Level risk

1980 1990 2000

−0.020

−0.015

−0.010Slope risk

1980 1990 2000

−0.25

0.00

0.25Seasonal risk

1980 1990 2000

2.65

2.70

2.75

2.80Level population

1980 1990 2000

0.001

0.002Slope population

Figure A.5.1. Smoothed state 'Total'


Appendix 6 Smoothed model states 'Male - Female'

1980 1990 2000

2.0

2.5

3.0

3.5

4.0Level risk

1980 1990 2000

−0.0200

−0.0175

−0.0150Slope risk

1980 1990 2000

−0.25

0.00

0.25Seasonal risk

1980 1990 2000

1.95

2.00

2.05


1980 1990 2000

0.001


Figure A.6.1. Smoothed state 'Male'

1980 1990 2000

2.0

2.5

3.0

3.5Level risk

1980 1990 2000

−0.04

−0.02

0.00Slope risk

1980 1990 2000

−0.5

0.0

0.5Seasonal risk

1980 1990 2000

1.95

2.00

2.05

2.10 Level population

1980 1990 2000

0.0010

0.0015

0.0020


Figure A.6.2. Smoothed state 'Female'


Appendix 7 Smoothed model states 'Age groups'

1980 1990 2000

3

4Level risk

1980 1990 2000

−0.015

−0.010

−0.005Slope risk

1980 1990 2000

0.0

0.5Seasonal risk

1980 1990 2000

0.8

0.9Level population

1980 1990 2000

−0.005

0.000


Figure A.7.1. Smoothed state 'Age 0-11 years'

1980 1990 2000

2.5

3.0Level risk

1980 1990 2000

−0.02

−0.01

0.00 Slope risk

1980 1990 2000

−0.5

0.0

0.5 Seasonal risk

1980 1990 2000

0.9

1.0

1.1

1.2Level population

1980 1990 2000

−0.005

0.000

0.005 Slope population



1980 1990 2000

1.5

2.0

2.5Level risk

1980 1990 2000

−0.004

−0.002

0.000Slope risk

1980 1990 2000

0.0

0.5Seasonal risk

1980 1990 2000

1.9

2.0

2.1 Level population

1980 1990 2000

0.000

0.002



1980 1990 2000

2.5

3.0

3.5 Level risk

1980 1990 2000

−0.0175

−0.0150

−0.0125

−0.0100Slope risk

1980 1990 2000

−0.5

0.0

0.5

1.0Seasonal risk

1980 1990 2000

0.4

0.5

0.6Level population

1980 1990 2000

0.0000

0.0025




1980 1990 2000

3.0

3.5

4.0

4.5Level risk

1980 1990 2000

−0.02

−0.01

Slope risk

1980 1990 2000

−0.5

0.0

0.5

1.0Seasonal risk

1980 1990 2000

−0.50

−0.25


1980 1990 2000

0.0025

0.0050

0.0075


Figure A.7.5. Smoothed state 'Age 75+ years'


Appendix 8 Smoothed model states 'Inside – Outside urban area'

1980 1990 2000

2.0

2.5

3.0

3.5Level risk

1980 1990 2000

−0.03

−0.02

−0.01

0.00Slope risk

1980 1990 2000

−0.50

−0.25

0.00

0.25 Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.001


Figure A.8.1. Smoothed state 'Inside urban area'

1980 1990 2000

0.5

1.0

1.5 Level risk

1980 1990 2000

−0.020

−0.015

−0.010

−0.005Slope risk

1980 1990 2000

0.0

0.5 Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.8.2. Smoothed state 'Outside urban area'


Appendix 9 Smoothed model states 'Working day - Weekend'

1980 1990 2000

1.5

2.0

2.5

3.0Level risk

1980 1990 2000

−0.015

−0.010

−0.005 Slope risk

1980 1990 2000

−0.25

0.00

0.25Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.001


Figure A.9.1. Smoothed state 'Working day'

1980 1990 2000

1

2

Level risk

1980 1990 2000

−0.03

−0.02

−0.01

0.00Slope risk

1980 1990 2000

−0.25

0.00

0.25Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.9.2. Smoothed state 'Weekend'


Appendix 10 Smoothed model states 'Road section - Intersection'

Figure A.10.1. Smoothed state 'Road section'

1980 1990 2000

1.0

1.5

2.0

2.5Level risk

1980 1990 2000

−0.02

0.00Slope risk

1980 1990 2000

−0.5

0.0

0.5Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.001


Figure A10.2. Smoothed state 'Intersection'

Titel:

Gemaakt door:Ox (C) J.A. Doornik, 1994-2000.Voorbeeld:Deze EPS-figuur is niet opgeslagenmet een ingesloten voorbeeld.Commentaar: Dit EPS-bestand kan worden afgedruktop een PostScript-printer, maar nietop een ander type printer.


Appendix 11 Smoothed model states 'Day - Night'

1980 1990 2000

2.0

2.5

3.0 Level risk

1980 1990 2000

−0.02

−0.01Slope risk

1980 1990 2000

−0.25

0.00

0.25 Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.001


Figure A.11.1. Smoothed state 'Day'

1980 1990 2000

1

2Level risk

1980 1990 2000

−0.03

−0.02

−0.01

0.00

0.01Slope risk

1980 1990 2000−1

0

1Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.11.2. Smoothed state 'Night'


Appendix 12 Smoothed model states 'Road and weather conditions'

1980 1990 2000

2

3Level risk

1980 1990 2000

−0.02

−0.01

0.00Slope risk

1980 1990 2000

−0.25

0.00

0.25Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.12.1. Smoothed state 'Dry weather – dry road surface'

1980 1990 2000

−1

0

1Level risk

1980 1990 2000

−0.020

−0.015

−0.010Slope risk

1980 1990 2000

0

1 Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.12.2. Smoothed state 'Dry weather – wet road surface'


1980 1990 2000

0.0

0.5

1.0

1.5Level risk

1980 1990 2000

−0.02

0.00

0.02Slope risk

1980 1990 2000

−0.5

0.0

0.5

1.0Seasonal risk

1980 1990 2000

2.65

2.70

2.75


1980 1990 2000

0.0010

0.0015

0.0020


Figure A.12.3. Smoothed state 'Rain– wet road surface'


Appendix 13 Smoothed model predictions 'Total'

1980 1985 1990 1995 2000 2005

250

500

750KSI totaal Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.13.1. Smoothed prediction 'Total'


Appendix 14 Smoothed model predictions 'Male - Female'

1980 1985 1990 1995 2000 2005

100

200

300

400KSI male Victims pedestrian−car

1980 1985 1990 1995 2000 2005

7.0

7.5

8.0 Population

Figure A.14.1. Smoothed prediction 'Male'

1980 1985 1990 1995 2000 2005

100

200

300KSI female Victims pedestrian−car

1980 1985 1990 1995 2000 2005

7.0

7.5

8.0

Population

Figure A.14.2. Smoothed prediction 'Female'


Appendix 15 Smoothed model predictions 'Age groups'

1980 1985 1990 1995 2000 2005

100

200

300KSI 0−11 Victims pedestrian−car

1980 1985 1990 1995 2000 2005

2.2

2.4

2.6 Population

Figure A.15.1. Smoothed prediction 'Age 0-11 years'

1980 1985 1990 1995 2000 2005

50


1980 1985 1990 1995 2000 2005

2.50

2.75

3.00

3.25 Population 12−24



1980 1985 1990 1995 2000 2005

50

100

KSI 25−59 Victims pedestrian−car

1980 1985 1990 1995 2000 2005

6

7

8

Population 25−59


1980 1985 1990 1995 2000 2005

50


1980 1985 1990 1995 2000 2005

1.4

1.6

1.8Population 60−74



1980 1985 1990 1995 2000 2005

50

100

KSI 75+ Victims pedestrian−car

1980 1985 1990 1995 2000 2005

0.6

0.8

1.0 Population 75+

Figure A.15.5. Smoothed prediction 'Age 75+ years'


Appendix 16 Smoothed model predictions 'Inside – Outside urban area'

1980 1985 1990 1995 2000 2005

200

400

KSI bibeko Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.16.1. Smoothed prediction 'Inside urban area'

1980 1985 1990 1995 2000 2005

50

100

KSI bubeko Victims pedestrian−car

1980 1985 1990 1995 2000 2005

13

14

15

16 Population

Figure A.16.2. Smoothed prediction 'Outside urban area'


Appendix 17 Smoothed model predictions 'Working day - Weekend'

1980 1985 1990 1995 2000 2005

100

200

300

KSI werkdag Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.17.1. Smoothed prediction 'Working day'

1980 1985 1990 1995 2000 2005

100

200

300KSI weekend Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.17.2. Smoothed prediction 'Weekend'


Appendix 18 Smoothed model predictions 'Road section - Intersection'

1980 1985 1990 1995 2000 2005

200

400KSI weg Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.18.1. Smoothed prediction 'Road section'

1980 1985 1990 1995 2000 2005

50

100

150

KSI kruis Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.18.2. Smoothed prediction 'Intersection'


Appendix 19 Smoothed model predictions 'Day - Night'

1980 1985 1990 1995 2000 2005

200

400KSI dag Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.19.1. Smoothed prediction 'Day'

1980 1985 1990 1995 2000 2005

100

200KSI nacht Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.19.2. Smoothed prediction 'Night'


Appendix 20 Smoothed model predictions 'Road and weather conditions'

1980 1985 1990 1995 2000 2005

200

400KSI drdr Victims pedestrian−car

1980 1985 1990 1995 2000 2005

14

15

16Population

Figure A.20.1. Smoothed prediction 'Dry weather – dry road surface'

1980 1985 1990 1995 2000 2005

50

100

150KSI drnat Victims pedestrian−car

1980 1985 1990 1995 2000 2005

13

14

15

16 Population

Figure A.20.2. Smoothed prediction 'Dry weather – wet road surface'


1980 1985 1990 1995 2000 2005

50

100KSI renat Victims pedestrian−car

1980 1985 1990 1995 2000 2005

13

14

15

16 Population

Figure A.20.3. Smoothed prediction 'Rain – wet road surface'


Appendix 21 Results linear regression analyses

Model 1: registered victims 1976-2004 by month

0

2

4

6

8

10

12

14

16

18

20

jan-

76

jul-7

6

jan-

77

jul-7

7

jan-

78

jul-7

8

jan-

79

jul-7

9

jan-

80

jul-8

0

jan-

81

jul-8

1

jan-

82

jul-8

2

jan-

83

jul-8

3

jan-

84

jul-8

4

jan-

85

jul-8

5

pede

stria

n-ca

r KSI

risk

Observed Predicted

Figure A.21.1. Pedestrian-car KSI risk 1976-1985 on the basis of registered number of victims, observed and predicted by Model 1.

-3

-2

-1

0

1

2

3

jan-

76

jul -7

6

jan-

77

jul-7

7

jan -

78

jul-7

8

jan -

79

jul-7

9

jan -

80

jul-8

0

jan -

81

jul-8

1

jan -

82

jul -8

2

jan-

83

jul -8

3

jan-

84

jul -8

4

jan-

85

jul -8

5

stan

dard

ized

err

or

Figure A.21.2. Standardized error 1976-1985, Model 1.


0

2

4

6

8

10

12

jan-

86

jul-8

6

jan-

87

jul-8

7

jan-

88

jul-8

8

jan-

89

jul-8

9

jan-

90

jul-9

0

jan-

91

jul-9

1

jan-

92

jul-9

2

jan-

93

jul-9

3

jan-

94

jul-9

4

jan-

95

jul-9

5

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

86

jul -8

6

jan-

87

jul-8

7

jan -

88

jul-8

8

jan -

89

jul-8

9

jan -

90

jul-9

0

jan -

91

jul-9

1

jan -

92

jul -9

2

jan-

93

jul -9

3

jan-

94

jul -9

4

jan-

95

jul -9

5

stan

dard

ized

err

or



0

1

2

3

4

5

6

jan-

96

jul-9

6

jan-

97

jul-9

7

jan-

98

jul-9

8

jan-

99

jul-9

9

jan-

00

jul-0

0

jan-

01

jul-0

1

jan-

02

jul-0

2

jan-

03

jul-0

3

jan-

04

jul-0

4

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

96

jul -9

6

jan -

97

jul-9

7

jan -

98

jul-9

8

jan-

99

jul -9

9

jan-

00

jul -0

0

jan-

01

jul-0

1

jan -

02

jul -0

2

jan -

03

jul -0

3

jan -

04

jul -0

4

stan

dard

ized

err

or



-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

normal probability value

sort

ed s

tand

ardi

zed

erro

r

Figure A.21.7. Normal probability plot, Model 1.

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Figure A.21.8. Correlogram, Model 1.

Error test Variable Result

Normal probability Pst.dev > 1.96 0.94

Varobs.1-174 / Varobs.175-348 0.36

Varobs.175-348 / Varobs.1-174 2.79

F174, 174, 0.95 0.78

Homoscedasticy

1 / F174, 174, 0.95 1.28


Model 2: underregistration-adjusted number of victims 1976-2004 by month

0

5

10

15

20

25

jan-

76

jul-7

6

jan-

77

jul-7

7

jan-

78

jul-7

8

jan-

79

jul-7

9

jan-

80

jul-8

0

jan-

81

jul-8

1

jan-

82

jul-8

2

jan-

83

jul-8

3

jan-

84

jul-8

4

jan-

85

jul-8

5

pede

stria

n-ca

r KSI

risk

Observed Predicted

Figure A.21.9. Pedestrian-car KSI risk 1976-1985 on the basis of adjusted number of victims, observed and predicted by Model 2.

-3

-2

-1

0

1

2

3

jan-

76

jul -7

6

jan-

77

jul-7

7

jan -

78

jul-7

8

jan -

79

jul-7

9

jan -

80

jul-8

0

jan -

81

jul-8

1

jan -

82

jul -8

2

jan-

83

jul -8

3

jan-

84

jul -8

4

jan-

85

jul -8

5

stan

dard

ized

err

or



0

2

4

6

8

10

12

14

16

jan-

86

jul-8

6

jan-

87

jul-8

7

jan-

88

jul-8

8

jan-

89

jul-8

9

jan-

90

jul-9

0

jan-

91

jul-9

1

jan-

92

jul-9

2

jan-

93

jul-9

3

jan-

94

jul-9

4

jan-

95

jul-9

5

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

86

jul -8

6

jan-

87

jul-8

7

jan -

88

jul-8

8

jan -

89

jul-8

9

jan -

90

jul-9

0

jan -

91

jul-9

1

jan -

92

jul -9

2

jan-

93

jul -9

3

jan-

94

jul -9

4

j an-

95

jul -9

5

stan

dard

ized

err

or

Figure A21.12. Standardized error 1986-1995, Model 2.


0

1

2

3

4

5

6

7

8

9

jan-

96

jul-9

6

jan-

97

jul-9

7

jan-

98

jul-9

8

jan-

99

jul-9

9

jan-

00

jul-0

0

jan-

01

jul-0

1

jan-

02

jul-0

2

jan-

03

jul-0

3

jan-

04

jul-0

4

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

96

jul -9

6

jan -

97

jul-9

7

jan -

98

jul-9

8

jan-

99

jul -9

9

jan-

00

jul -0

0

jan-

01

jul-0

1

jan -

02

jul -0

2

jan -

03

jul -0

3

jan -

04

jul -0

4

stan

dard

ized

err

or



-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3


sort

ed s

tand

ardi

zed

erro

r


-0,3

-0,2

-0,1

0

0,1

0,2

0,3

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35




Varobs.1-174 / Varobs.175-348 0.42

Varobs.175-348 / Varobs.1-174 2.37

F174, 174, 0.95 0.78

Homoscedasticy

1 / F174, 174, 0.95 1.28


Model 3: registered 0-11 aged victims 1976-2004 by month

0

5

10

15

20

25

30

35

40

45

50

jan-

78

mei

-78

sep-

78

jan-

79

mei

-79

sep-

79

jan-

80

mei

-80

sep-

80

jan-

81

mei

-81

sep-

81

jan-

82

mei

-82

sep-

82

jan-

83

mei

-83

sep-

83

jan-

84

mei

-84

sep-

84

jan-

85

mei

-85

sep-

85

jan-

86

mei

-86

sep-

86

pede

stria

n-ca

r KSI

risk

Observed Predicted

Figure A.21.17. Pedestrian-car KSI risk for 0-11 aged 1978-1986 on the basis of registered number of victims, observed and predicted by Model 3.

-3

-2

-1

0

1

2

3

jan-

78

jul -7

8

jan -

79

jul-7

9

jan -

80

jul-8

0

jan-

81

jul -8

1

jan-

82

jul -8

2

jan-

83

jul-8

3

jan -

84

jul -8

4

jan -

85

jul -8

5

jan -

86

jul -8

6

stan

dard

ized

err

or



0

5

10

15

20

25

30

jan-

87

mei

-87

sep-

87

jan-

88

mei

-88

sep-

88

jan-

89

mei

-89

sep-

89

jan-

90

mei

-90

sep-

90

jan-

91

mei

-91

sep-

91

jan-

92

mei

-92

sep-

92

jan-

93

mei

-93

sep-

93

jan-

94

mei

-94

sep-

94

jan-

95

mei

-95

sep-

95

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

87

jul -8

7

jan -

88

jul-8

8

jan -

89

jul-8

9

jan-

90

jul -9

0

jan-

91

jul -9

1

jan-

92

jul-9

2

jan -

93

jul -9

3

jan -

94

jul -9

4

jan -

9 5

jul -9

5

stan

dard

ized

err

or



0

2

4

6

8

10

12

14

16

jan-

96

jul-9

6

jan-

97

jul-9

7

jan-

98

jul-9

8

jan-

99

jul-9

9

jan-

00

jul-0

0

jan-

01

jul-0

1

jan-

02

jul-0

2

jan-

03

jul-0

3

jan-

04

jul-0

4

pede

stria

n-ca

r KSI

risk

Observed Predicted


-3

-2

-1

0

1

2

3

jan-

96

jul -9

6

jan -

97

jul-9

7

jan -

98

jul-9

8

jan-

99

jul -9

9

jan-

00

jul -0

0

jan-

01

jul-0

1

jan -

02

jul -0

2

jan -

03

jul -0

3

jan -

0 4

jul -0

4

stan

dard

ized

err

or



-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3


sort

ed s

tand

ardi

zed

erro

r


-0,3

-0,2

-0,1

0

0,1

0,2

0,3

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35




Varobs.1-162 / Varobs.163-324 0.20

Varobs.163-324 / Varobs.1-162 5.06

F162, 162, 0.95 0.77

Homoscedasticy

1 / F162, 162, 0.95 1.30


Appendix 22 Overview hypotheses

Period Descriptive analysis Literature scan State space modelling Exploratory regression

1974 - 2004 Because of changes in pattern of life, e.g. the greater participation of women in the labour market, pedestrian-car KSI casualty risk decreased more slowly for females than for males. (H76-04.1.A)

Literature confirms the growing participation of women in the labour market since the eighties.

Hypothesis not supported. Risk development of women is comparable to men until 2001, while changes in pattern of life of women started in the eighties.

No data.

Young car drivers are compared to their part in road traffic relatively often involved in pedestrian-car crashes and other crashes with car as opponent. (H76-04.2.A)

No data. No data. No data.

The consequent application of new, safe city and road design principles in Flevoland has resulted in considerable lower pedestrian-car KSI risk than in the other provinces. (H76-04.3.A)

Hypothesis supported. Sustainable Safe measures safe casualties.

No data. No data.

Periods of (strongly lowered) economic activity (1981, 1990 and 2000) are associated with trend breaks in pedestrian-car KSI risk. (H76-04.4.B)

Hypothesis not supported. Only in 1990 a significant trend break is distinguished. In 1981 or 2000 there are no significant trend breaks

Hypothesis not supported.

Extreme winter weather, severe frost, and the combination of warmth and drought are associated with low pedestrian-car KSI casualty risk. (H76-04.5.D)

1976 - 2004

Using registered victim numbers instead of 'registration-adjusted' victim numbers in the analysis of crash risk has influence on the resulting description of the development of crash risk and the estimated effects of influence factors. (H76-04.6.D)



1979 + 1985 The winter drop of the number of pedestrian-car KSI victims in 1979 and 1985 was caused by a temporarily seasonal effect, probably winter weather circumstances. (H79&85.1.A)

The number of pedestrian-car KSI casualties in 1979, 1985, 1986, and 1987 was relatively low because of the severe winter weather in those years. (H79-87.1.B)

Hypothesis supported. There are significant changes in the seasonal pattern in 1979 and around 1985.

Hypothesis supported, several (winter) weather variables included in the models.

1980-1989 The considerable decrease of pedestrian-car KSIs on urban road sections and the considerable increase on urban intersections both in 1982-1983 were caused by a change in the definition of road section and intersection crashes. (H82-83.1.A)

No data. Hypothesis supported by modelling results (Figure 4.9).

No data.

The transformation of more and more 50 km/h roads into 30 km/h roads as part of the Sustainable Safe program, which started in 1997, resulted in the strong decrease of pedestrian-car KSI victims of 0-11 years old in 2000 and 2001. (H99-01.1.A)

Hypothesis supported. Accelarating growth of 30 km/h roads since 2000 and we estimated a reducing effect of replacement of 50 km/h roads by 30 km/h roads in the number of KSI casualties.

Hypothesis supported. Modelling results show a significant downward trend break in risk in 2000 for 0-11 aged pedestrians

Hypothesis supported. Accelarating growth of 30 km/h roads since 2000 decreased the number of 0-11 aged KSIs.

1990-1999

Pedestrian-car KSI casualty risk has decreased more rapidly than normal in 1990-1991 because of the introduction of the free public transport pass for students and the beginning of economic recession. (H90-91.1.B)

Hypothesis supported and additional hypothesis formulated: The introduction of free public transport for students in November 1990 and the start of economic recession caused a shift of car trips to public transport for the age group 12-24. Due to this, the number of pedestrian trips for age group 12-24 increased, which compensated the risk decrease as described in hypothesis H90-91.1.B. (H90-91.2.C)

Hypothesis H90-91.1.B partly supported. No data on H90-91.2.C



Pedestrian-car KSI casualty risk has decreased more rapidly than normal in 2000-2002 because of the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession. (H00-02.1.B)

Hypothesis not supported. Only the age group 0-11 years shows a significant decrease

Hypothesis partly supported. The influence of part of the Sustainable Safe program, i.e. the transition of 50 km/h into 30 km/h roads, is acknowledged by the model for 0-11 aged.

2000-2004

The fall of registered pedestrian-car KSI victims from 2003 to 2004 is partly due to the drop of the registration level and partly due to one or more unknown factors which made pedestrian-car KSI victim numbers in 2003 relatively large. (H03-04.1.D)


Period Descriptive analysis Literature scan State space modelling

Exploratory regression

1980-1989

The considerable decrease of pedestrian-car KSIs on urban road sections and the considerable increase on urban intersections both in 1982-1983 were caused by a change in the definition of road section and intersection crashes. (H82-83.1.A)

No data.

Hypothesis supported by modelling results (Figure 4.9).

No data.

The transformation of more and more 50 km/h roads into 30 km/h roads as part of the Sustainable Safe program, which started in 1997, resulted in the strong decrease of pedestrian-car KSI victims of 0-11 years old in 2000 and 2001. (H99-01.1.A)

Hypothesis supported. Accelarating growth of 30 km/h roads since 2000 and we estimated a reducing effect of replacement of 50 km/h roads by 30 km/h roads in the number of KSI casualties.

Hypothesis supported. Modelling results show a significant downward trend break in risk in 2000 for 0-11 aged pedestrians

Hypothesis supported. Accelarating growth of 30 km/h roads since 2000 decreased the number of 0-11 aged KSIs.

1990-1999

Pedestrian-car KSI casualty risk has decreased more rapidly than normal in 1990-1991 because of the introduction of the free public transport pass for students and the beginning of economic recession. (H90-91.1.B)

Hypothesis supported and additional hypothesis formulated: The introduction of free public transport for students in November 1990 and the start of economic recession caused a shift of car trips to public transport for the age group 12-24. Due to this, the number of pedestrian trips for age group 12-24 increased, which compensated the risk decrease as described in hypothesis H90-91.1.B. (H90-91.2.C)

Hypothesis H90-91.1.B partly supported. No data on H90-91.2.C


Period Descriptive analysis Literature scan State space modelling

Exploratory regression

Pedestrian-car KSI casualty risk has decreased more rapidly than normal in 2000-2002 because of the implementation of Sustainable Safe, intensified police enforcement, and the beginning of economic recession. (H00-02.1.B)

Hypothesis not supported. Only the age group 0-11 years shows a significant decrease

Hypothesis partly supported. The influence of part of the Sustainable Safe program, i.e. the transition of 50 km/h into 30 km/h roads, is acknowledged by the model for 0-11 aged.

2000-2004

The fall of registered pedestrian-car KSI victims from 2003 to 2004 is partly due to the drop of the registration level and partly due to one or more unknown factors which made pedestrian-car KSI victim numbers in 2003 relatively large. (H03-04.1.D)

goldenbeld pedestrian car crashes a 2006 04

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