prospects for a danish model: human factors ivanka orozova-bekkevold [email protected] 2nd european...

Prospects for a Danish model: human factors

Ivanka Orozova-Bekkevold [email protected]

2nd European Workshop, Modelling National or Regional Road Safety Performance

30-31 May 2007, Arcueil, France

2nd European Workshop, Modelling National or Regional Road Safety Performance, 30-31 May 2007, Arcueil, France

Ivanka Orozova-Bekkevold, DTF, Denmark

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Road transport system production system

Road accident: a failure of the transport system

Road accident – interaction of three principal elements

• The human being (the active road user);

• The vehicle;

• The surroundings – infrastructure, weather, time of the day.

Vehicles and infrastructure – man-made controllable

The road user important, complex, difficult to deal with

[ Topic – slide to proper position ]



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Human aspects of road accidents:

• Mistakes;

• Lapses of attention;

• Misjudgements;

• Medical conditions (stroke);

• Risky behaviour: speeding, drink driving etc.

Main road safety tasks related to the human factor

• Preventing accident occurrence (active safety)

• Mitigating / Limiting the consequences (passive safety)




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The questions we ask:

Are there any specific socio-economic groups in the

population who are more prone to provoke an accident than

others?

Can we quantify the eventual risk of these groups?

The goal:

Specific road safety interventions for specific groups




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Available personal data in Denmark

National Registries (all individuals above 15 years of age):

• Age, Gender, Origin, Family Status, nr Kids, etc.;

• Income, Profession, Employment, etc.;

• Criminal record: violations, sanctions, court sentences;

• Driving license registry (with some limitations);

• Police records on road accidents with injury

National Transport Survey (about 15,000 randomly selected)

• Type and purpose of the last trip (the day before);

• The means of transport used;

• Travelled distance;

• Road user type (driver, pedestrian, bicyclist etc);

• Availability of cars in the family




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NOTA BENE: All the personal data are anonymous!

Time Span (for the moment)

• Registry data: from 1993 to 2003

• Survey data: from 1992 to 2004

Wishes and possibilities for additional data:

• National Hospital Registry;

• Accidents recorded by the insurance companies;

• Car ownership Registry;

• Individual’s exposure (?)




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What we have done until now

Using only Registry data

In total: 5,072,871 different individuals (aged > 15)

156, 277 were involved in road accidents from 1993 to 2003

5993 (3.8%) were in 2; 446 (0.3%) were in 3 or more accidents

For about 1,881,000 (~37%) we were able to trace the parents

(family history on accidents, crime etc.)




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Some descriptive statistics

Age and number of accidents of active road users*

(passengers excluded): In total 150,264 individuals (86.57% of

all in accidents)

Nr Acc Nr Obs Mean Age STD

1 143,583 38.08 17.96

2 5,933 34.53 14.85

3 405 31.85 12.46

4 50 32.08 11.88

5 5 31.00 7.44

* Was an active road user in all accidents




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Age and number of accidents of passengers*

In total 23,320 individuals (13.43% of all in accidents)

Nr Acc Nr Obs Mean Age STD

1 22,925 31.00 22.22

2 193 27.08 14.56

3 3 21.33 2.08

* Was passenger in all accidents




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Number of accidents and employment

Minors Unemployed Other Employed

(< 18 y) (St/Pens/Leave)

(% of pop) 0.6 4.0 36.3 59.2

Nr Acc Number (% of the group)

1 848 (0.6) 5,707 (4.1) 39,755 (28.4) 93,661 (67.0)

2 83 (1.5) 250 (4.4) 1,522 (26.6) 3,858 (67.5)

3 8 (2.1) 21 (5.6) 132 (35.2) 214 (57.1)

4 3 (6.5) 6 (13.) 20 (43.5) 1

5 17 (37.0) 4




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Number of accidents and Income (average over FUP)

Nr Acc Nr Obs Mean (DKK)

1 139,967 199,135

2 5,713 200,259

3 375 174,498

4 46 140,423

5 5 265,547

All 146,110 199,099




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Modelling: !!! TENTATIVE !!! VERY FIRST TRY !!!

Assumption

Since only people above 15 years of age are considered, who

supposedly move independently in the traffic,

I assume they all are equally exposed to the risk to be involved

in a road accident.

The responsibility for the accident is not investigated here, neither

the type of road user (active, passive), but only the odds to be

in an accident.

In this case, INVOLVEMENT = REGISTRATION in the police accident

reports




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Model: Multivariate logistic regression;

“Static” (any time dependency not considered for the

moment)

The person as whole – Max Education, Max Income over FUP

Independent variables (classes): Gender, Origin,

Income, Education, Criminal behaviour,

[Parents – Crime, Accidents]

Dependent variable (binary): being involved in accident

(yes/no) at least once during the study period.

Accident risk: expressed as Odds Ratio (O.R.)




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Modelling

Groups according to:

• Gender [M ; F]; Parent (having kids) [yes,no]

• Max Education: Basic (9 y), medium (12 y), bachelor, master, unknown

• Origin: Dane, Non-Dane, Descendent (born in DK);

• Max Income: low (< 0.5*mean), average, high (> 2*mean);

• Criminal behaviour: none, only traffic-related, only non-traffic, both traffic and non-traffic;

• [Parental involvement in accidents & crimes: yes/no]




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Test A: Without information on parental involvement in Acc & Crime

Independent variables: Gender, Origin (3 groups), Crime Type (4 levels),

Max Income (3 levels), MaxEdu (5 levels), Parent (yes/no)

Nr of records with all non-missing values: 4,999,304 (98% of all)

Test B: With information on parental involvement in Acc & Crime

Independent variables: Gender, Origin (3 groups), Crime Type (4 levels),

Max Income (3 levels), MaxEdu (5 levels), Parent (yes/no),

MotherAcc (yes/no), MotherCrime(yes/no), FatherAcc(yes/no),

FatherCrime(yes/no)

Nr of records with non-missing values: 1,880,998 (37% of all)

NB: Here many non-Danes and old people are excluded, because info on

their parents is missing




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Preliminary results: !!! VERY FIRST TRY !!!

TEST A (n=4,999,304) TEST B (n=1,880,998)

Variable O.R. 95% CI O.R. 95% CIGender: M vs F 1.362 1.35-1.38 1.360 1.34-1.38

Origin: Non vs DK 0.765 0.75-0.78 0.824 0.78-0.87

Desc vs DK 0.967 0.91-1.03 ns 0.826 0.77-0.89

CrimType : Road vs Non 4.564 4.50-4.63 3.622 3.55-3.69

Crime vs No 2.255 2.21-2.30 2.011 1.96-2.06

Road+Cr vs No 8.199 8.06-8.34 6.769 6.62-6.92

Income: Low vs Middle 0.726 0.71-0.74 0.769 0.75-0.79

High vs Middle 0.721 0.71-0.73 0.689 0.67-0.71

MaxEdu: Unkn vs Middle 0.679 0.66-0.70 1.228 1.15-1.32

Basic vs Middle 1.132 1.12-1.15 1.199 1.18-1.22

Bache vs Mid 0.944 0.93-0.96 0.867 0.85-0.89

Master vs Mid 0.868 0.85-0.89 0.784 0.76-0.81

Parent: Yes vs No 0.769 0.76-0.78 0.759 0.74-0.78

MotherAcc: Yes vs No 1.715 1.64-1.79

MotherCrim: Yes vs No 1.081 1.05-1.11

FatherAcc: Yes vs No 1.341 1.30-1.39

FatherCrim: Yes vs No 1.133 1.11-1.53




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Data mining test: Alternate Decision Tree

Sample with parental information: 1,880,998 persons

10% persons extracted randomly.

Used variables: 18 in total (FU; Origin; CrimeTYPE; MaxEdu;

Income; Gender; Parent; Mother&Father-ACC,Crime; FRAK(y/n);

nr Sanctions for: FERDALK, FERDVEL,FERDGEN,SERLOV,

STRAFLOV,UOPLLOV

The proportions of the classes (Accident & Crime, Gender etc.) is

preserved.




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No initial hypothesis

Input file: 188,100 obs; 18 variables

Response variable (been in accident): FU (yes=1, no=0)

66% of the data – for training, 34% of the data – for testing

Results: 95.53% - correctly classified

Mean abs error: 0.186

RMS error: 0.233




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-1.541 (negative: FU=0; positive: FU=1)

CrimeType MaxEdu

FU=0(-0.026)

FU=1(0.056)

FU=0(-0.009)

FU=1(0.340)

FU=1(0.476)

FU=0(-0.275)

Gender NrTrafGeneric FRAK Income FatherCrime

FU=0(-0.179)

FU=1(0.332)

FU=1(0.021)

FU=0(-0.094)

FU=1(0.093)

FU=0(-0.086)

FU=1(0.386)

FU=0(-0.105)

FU=1(0.078)

FU=0(-0.024)

CrimeType NrTrafAlcohol

FU=1(0.224)

FU=0(-0.028)

FU=0(-0.025)

FU=1(0.526)

None Crime Any Crime yes no Basic Other

yes noM K 0 >0 yes no Middle Other

0 > 0Other than both

Both: Traffic+Other

MotherAcc



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Final remarks - 1

For the moment, the models presented here should be

considered preliminary; they must be refined.

I just wanted to show what data are available and what can be

done.

Data mining can be used as prescreening and hypothesis

generating tool.




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Final remarks -2

In order to account for the human factor in road safety, other

approaches and techniques (data mining pattern recognition)

could and should be combined with econometric modelling.

The human factor can be modelled also numerically.

The DK register data rich possibilities for cross-sectional &

longitudinal studies, as well as time-series analysis.

Register data can be linked easily to survey data.


Thank you for your attention! Thank you for your attention! Ivanka Orozova-Bekkevold

[email protected]

prospects for a danish model: human factors ivanka orozova-bekkevold [email protected] 2nd european...

Documents

france slide

active road user

proper position slide

main road safety tasks

road user important

family topic slide

danish model

european workshop