crash vehicle person roadway mmucc model minimum uniform crash criteria

Crash Vehicle Person Roadway

MMUCCModel Minimum Uniform Crash Criteria

Impact of threshold adjustments

Sketch and narrative

http://www.nhtsa-tsis.net/crashforms/

Storage/retrieval

• <500 annually may be filed (paper) with summary tables

• Increasingly, all data are input into a database (and forms scanned)

• Feeds state and national databases

Old Location Process

Data Collection Technologies

• TraCS: Traffic and Criminal Software

TraCS data entry form

Incident Location Tool (and IMAT)

Easy Street Draw & Visio

Florida TraCS show

Data limitations Ogden)• Systematic reporting bias

– Database not truly reflective of crash situation

• Random bias– Under-reporting can result in distorted picture of road

crash situation• Numerically• Nature of the crashes• Not recording particular factor, means it was not present• Factor was present, but police officer did not think that it is not

important

Data limitations (continued)• Coding errors• Location errors• Discontinuities

– Data from one time period can not be compared to another time period

• Delays– Takes too long to have data available for analysis, so

countermeasures development is responding to historical crashes which may be out of date

Data limitations (continued)• Hidden problems

– It is assumed that database is good indicator of road safety problems

– There might some masked problems• Pedestrians avoid using an area because perceived safety problem

– This kind of problems need to be tackled through a road safety audit or identified through community consultation

Case study – access management

From …

Use and Abuse of Crash Data in Roadway Access Management

A Workshop at the National Access Management Conference

Baltimore, Maryland July 13, 2008

Data-Driven Access Management• Access management treatments and plans should be directly tied

to measurable objectives such as crash rate or crash cost reduction• Access management treatments proposed should be appropriate

given the types of crashes and pattern of crashes being experienced in a corridor

• Access management treatment costs need to be justifiable based upon the expected benefits of crash reductions and other objectives • Stakeholders and decision-makers must be convinced that the “gain”

of access management is worth the “pain” • Confidence in both past (“before treatment”) and expected future

crash rates (“after treatment”) should be high• You want to be very sure that any treatments

will produce a noticeable and positive result

23

Access Management and Safety• Most access-management related

crashes occur on urban and suburban arterial roadways at speeds of 35 to 55 miles per hour

• Up to half of all crashes in urban areas are related to issues of access (minor public road intersections, traffic signal spacing, driveways)

• Although most access-related crashes occur in urban or suburban areas, access-related crashes in rural areas tend to be severe crashes due to higher travel speeds

• Access-related crashes occur at conflict points

• The diagram represents one crash data point

24

Problem 1: Fix This Mess South Ankeny Blvd., Ankeny, Iowa

25

What Do Crash Data Really Look Like?

26

What’s On Your Table …

27

Land Use

Crash data tables and charts

Crash data stack mapLaminated base map

Traffic over time

Corridor photos

27

An Example Plan …

28

Crash Data Allow Better …

• Problem Identification• Understanding of the problem before

jumping into exploring and designing solutions

• Focus on severe crashes rather than all (minor) crashes

However …

29

You Need Good Quality Data

The Ingredients Matter: Quality Control30

The Characteristics of Data Quality (The “Six-Pack”)

31

FMCSA Data Quality

Crash Data Quality: Timeliness• Sometimes crash data are not available for months or

even years• Varying timeliness of different jurisdictions can cause

issues for comparative analysis• Time itself is important – did something change

during the analysis period?• Also – the time period is important … one year of data

are probably not enough!

33

Crash Data Quality: Accuracy

• Spatial Location• Attributes, e.g.,

severity, crash type, roadway info

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Crash Data Quality: Completeness

• Missing data can lead to a misleading picture and erroneous conclusions

• Some crash records have “unknown” or “other” fields

• Some crash records are missing altogether

• Variations between jurisdictions (county level, state level) can lead to inaccuracies in comparative analysis

35

Collision TypeNum of Crashes Percentage

Non-collision 17854 32.6%

Head-on 1006 1.8%

Rear-end 12143 22.2%Angle, oncoming left turn 3528 6.4%

Broadside 10192 18.6%Sideswipe, same direction 5035 9.2%Sideswipe, opposite direction 1145 2.1%

Unknown 3538 6.5%

Not Reported 374 0.7%

Total 54815 100.0%

Crash Data Quality: Consistency/Uniformity

• Across jurisdictions

• Across time• Consistent

severities

36

Consistency

• Although the need for data is universally recognized, there is little consistency in collected data (Ogden)– Comparative study of eleven European countries found

that• Only two variables (date & hour) were collected in all eleven

countries• 7 percent of items were recorded in three countries• 70 percent recorded in only one country

– There is no nationwide crash data reporting system in US• Little consistency within states for recorded data elements

Crash Data Quality: Integration

• Integration provides a ‘richer’, more complete source of information (e.g., integration with roadway features)

• Double check on accuracy (including severity)• Privacy is a tough issue• Another tough issue is multiple offices and even

agencies being in charge of various parts of safety data

38

Crash Data Quality: Accessibility

• How can you get crash data?• How easy is it to get?• What form do you want it in?• Liability and perception is an issue.• Continuum:

not available … special request w/delay … regular updates … service … instant web access

39

Typical Crash Data IssuesThese may not be apparent to the data user

Changes in Crash Forms• Content

– Addition/elimination of attributes collected– Change in definitions (values)

Non-collision

Head-on

Rear-end

Angle, oncoming left turn

Broadside

Sideswipe, same direction

Sideswipe, opposite direction

Head-on

Broadside/Left Turn

Rear End

Rear End/Right Turn

Rear End/Left Turn

Sideswipe/Opposite Direction

Sideswipe/Same Direction

Sideswipe/Right Turn

Sideswipe/Left Turn

Sideswipe/Dual Left Turn

Sideswipe/Dual Right Turn

Broadside/Right Angle

Broadside/Right Entering

Broadside/Left Entering

Head-on/Left Entering

Sideswipe/Both Left Turning

Single

Pedestrian

Bicycle

Parked Vehicle

Before After

Collision Type

41

Changes in Crash Forms, cont.Impacts:• Difficult to perform direct comparisons over analysis

period.• May result in systematic change in apparent crash

performance, e.g. crash reduction.

Year

Cras

h Ra

te

StatewideYear

Cras

h Ra

teSite #1

Change in crash form

42

Cartographic (Base Map) Changes

• Shift, update to reference road network

Impact: Challenging to systematically assign crash location.

43

Location Accuracy

• How are the crashes located? – GPS (where?)– Manually derived, based on literal description– LRS, Link-node, other?

• What reference networks are used?– GIS– LRS– Link-node

44

Location Accuracy, cont.

• How do accuracies vary among location methods and reference networks?– Ex. GPS ±5m v. GIS-based road network ±10m

Impact: type I or type II errors – you’d not know

X

Actual crash location

Crash may be locatedanywhere within this area.

Roadway may be presentedanywhere within this area.

X

Geocoded crash location

GIS road network

45

Changes in Statute

• Reportable crash definition– Property damage threshold, e.g. $500 v. $1000– Injury crash

• Reporting requirements– Driver report “…is not required when the accident

is investigated by a law enforcement agency.”

Impact: May result in systematic change in apparent crash performance, e.g. crash reduction.

46

Reporting Extent & Completeness

• All public roads• Private property• State-maintained roads only• Jurisdiction, agency dependent

Impacts:• Incomplete crash history skews findings.• Difficult to compare different locations.

47

Multiple Data Sources

• Local law enforcement• State DOT• Other agencies, e.g. taxi authority

Impact: Difficult to access and integrate all crash data, i.e. difficult to create a comprehensive, useable data set.

48

How Crash Data Are Abused

49

Limited Frame of Reference

• Limited, no comparison to similar locations.• No comparison to “expected” conditions

(comparables).

Impact:• What may appear to be a problem site, in isolation,

may be performing as well as, or better than, similar locations.– However, this does not imply that a location is performing

well and/or can not be improved.

50

Limited Perspective• Decisions made, almost exclusively, based on crash history.• Little consideration given to

changes during analysisperiod…– Land use and development– Infrastructure– Traffic patterns– Other, e.g. construction

during an analysis year

Impact: • Factors significantly impacting

crash history are ignored.• Solution no longer fits the

problem51

Regression to the Mean

• Crashes are random.• Extreme conditions will generally return to

“normal” state.

Source: Safe Speed Source: Safe Speed

Impact: Overestimates effectiveness of treatment; focus on the wrong sites (should use EB or at least more data) 52

Analysis Period Shortcomings

• Limited (short) analysis period • “Dated” crash data

Impacts:• May not accurately represent the performance of a

site. Similar to regression to the mean.• May not accurately reflect the existing conditions.

53

General Crash Data Issues

• Change in crash form• Cartographic (base

map) changes• Location accuracy

• Change in statute• Reporting extent &

completeness• Multiple data

sources

Impact: Not being aware of these issues – is it your responsibility?

54

Problem 2: Fix This Mess Lincoln Way, Ames, Iowa

55

Data On Your Tables …

1. Complete set of data2. 25 meter buffer vs. “Functional area”3. Crash frequency only vs. AADT and crash

type4. 1 year of data vs. 10 years of data5. Older data vs. recent data6. Current aerial photo only vs. past

development trend and detailed land use data

56

Locational Challenges for Next Generation of Crash Data

Systems

SAFETEA-LU Section 1401 (Highway Safety Improvement Program)

ID of top 5% of public hazardous locations on all roads

Local Road GIS Data

Where some states are now

Inventory data on all roads?

The “quadrennial needs” legacy

YesSome, quality issue, or working on itNoNo Response

State system as a percent of all public roads

Can 1401 be met without GIS?

Kansas, for example …• Has crashes on system only • Has ≈ 70% of crashes located to

road by route milepost• Does sliding spot (nongraphical)

& “named intersection” (program)

• Assuming the 30% missing does not affect the outcome

• No brainer to do top 5%

Mason City

Waterloo

Cedar Rapids

Quad CitiesDes Moines

Council Bluffs

IowaCity

Ames

SiouxCity

DubuqueFort Dodge

Ottumwa

Marshalltown

Spencer

Clinton

1 yrof data

Crash Density – 1 Year AverageAnnual Fatal and Major Injury Crashes Per Mile

Sample

- DRAFT

Sample

- DRAFT

Mason City

Waterloo

Cedar Rapids

Quad CitiesDes Moines

Council Bluffs

IowaCity

Ames

SiouxCity

DubuqueFort Dodge

Ottumwa

Marshalltown

Spencer

Clinton

3 yrsof data

Crash Density – 3 Year AverageAnnual Fatal and Major Injury Crashes Per Mile

Sample

- DRAFT

Sample

- DRAFT

Mason City

Waterloo

Cedar Rapids

Quad CitiesDes Moines

Council Bluffs

IowaCity

Ames

SiouxCity

DubuqueFort Dodge

Ottumwa

Marshalltown

Spencer

Clinton

5 yrsof data

Crash Density – 5 Year AverageAnnual Fatal and Major Injury Crashes Per Mile

Sample

- DRAFT

Sample

- DRAFT

Mason City

Waterloo

Cedar Rapids

Quad CitiesDes Moines

Council Bluffs

IowaCity

Ames

SiouxCity

DubuqueFort Dodge

Ottumwa

Marshalltown

Spencer

Clinton

10 yrsof data

Crash Density – 10 Year AverageAnnual Fatal and Major Injury Crashes Per Mile

Sample

- DRAFT

Sample

- DRAFT

Location methods• address• offset from known point

(intersection, bridge, crossing, milepost)

• Literal description• Smart map• Lat/long or other coordinates (GPS)• Aerial photo

Multiple methods required

Spatial analysis methods

• Spot/Intersection Analysis • Strip Analysis • Cluster Analysis • Sliding-Scale Analysis • Corridor Analysis

Spatial statistics is an emerging area

But …some technical issues

Some not-so “simple” questions

Feature not represented

Feature under

construction

Alignment OK

Alignment Off

Where are the roads? (Incorrect or incomplete cartography)

Where are the roads? (Improving cartography)

Alignment moves

Alignment stays put

Where are the crashes?• Crashes are not

necessarily point events• Some crashes may be

located using different methods and degree of accuracy – Temporal (e.g. link node

to lat long)– Spatial (e.g., state police

v. local)– Techno (GPS v. smart

map)

?

What’s “the” traffic volume on “the” road?

• Need traffic level for the year the crash happened

• Requires multiple files – in Iowa, working on going back past 1998 – difficult to do

• Was the road even there then? Is the road still there?

How to segment the road system?

• Requirements– Logical breaks (engineering and

public)– Relationship to inventory data– Long enough for manageability

and presentation– Short enough to reflect

important changes– Clear and understandable to use

• Facility location and type– What is rural/urban? Character is

important …Designated

“rural”

Can use attributes and/or proximity…Red: probable, Yellow: spatial @ 75’, Blue: possible + spatial

What is an intersection crash?

Where to go from here …