working with crash data

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Working with crash data

Module 2

Safety Analysis in a Data-limited, Local Agency Environment:

July 22, 2013 - Boise, Idaho

Learning Objectives

Identify potential crash data sources Value of identifying overrepresented fatal

and serious injury crashes Common considerations for using crash

data Reading a crash report Understanding regression to the mean

(RTM)2

POTENTIAL CRASH DATA SOURCES

When crash data are not readily available…

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Potential crash data sources

State crash data systems GIS layers of geolocated crashes Local law enforcement offices Non-traditional resources that can give

insight into particular collision types or contributing factors: EMS, law enforcement, DPW workers, maintenance workers

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If we don’t have access to a state or regional crash database Fatality Analysis Reporting System (FARS)

Online database with all fatal collisions across the U.S.

Online query toolsOnline mapping toolActual data downloads available (raw data)

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FARS mapping tool (pin map) 6

Over-represented crash locations Overarching trends

30% of fatal crashes occur on minor arterial and collector roadways

Fatal and serious injury crashes are overrepresented on local two-lane rural roads and four-lane undivided roads

What does this mean?Safety improvements are necessary across

local, regional, and state facilities8

Crash data considerations

Timeliness Consistency Completeness Accuracy Accessibility Value added by data Integration

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Timeliness

Timely crash data supports decisions that will optimize safety investments – the network, vehicle fleet, social norms, and technology changes over time.

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Data Consistency

Collect the same data elements over time and for various classes of roadways

Collect the same data as partner agencies 

Changes to data elements should be clearly documentedExample – road names/ route numbersChanges to the state crash report form

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Completeness High value data elements include:

Crash data: Location reference for the crash Contributing circumstances Characteristics that can identify behavioral and

roadway related factors for targeted solutionsOther data include: traffic volume, roadway

cross-section and alignment data, presence and control type of intersections, posted speeds

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Accuracy

Reliable information is key to success High value:

Quality control features where crash data are collected electronically (verification with other available information systems)

Employing methods for collecting, verifying and maintaining roadway data

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Accessibility

Access to the dataRaw data is better than no dataPeriodic standard reports are particularly

valuableEase of use (GIS data, query tools, data

export)Availability and access to data dictionaries

and coding manuals

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Data Integration

Link crash data, traffic volume, roadway characteristics

Integrating data systems at state and local levelConsistent data elementsConsistent data structuresConsistent quality control measures

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Commonly reported crash report errors - NCHRP Synthesis Project 367

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Reading a Crash Report: Background Many to One Relationships (example)

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Crash Report ElementsWhere, when, how, who & what Location Date and time of day Lighting, roadway and

other roadway environment factors

Involvement of vulnerable users (pedestrians, bicyclists, motorcyclists, and older users)

Vehicle type(s) Driver information, Reportable truck and

bus information Injury severity of the

crash Crash type (mechanism

of crash) Contributing factors

(BAC or other drug use, speeding, etc.)

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Crash Data ElementsWhere and when

Crash locationCritical for being able to understand how

different locations on the roadway network are performing with regards to safety

Time of dayUseful for understanding if there are periods

of the day that are over represented in terms of the frequency or severity of crashes

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Crash Data Elements Environmental Factors

Environmental factors can include: Weather conditions Pavement conditions (e.g., wet, dry, icy) Visibility conditions Lighting conditions

Improve understanding of potential contributing factors and in turn mitigations

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Crash Data ElementsUser and Vehicle Type(s)

UsersPedestrians, bicyclists, and the particularly

vulnerable (the young and older users) Vehicles

Single Vehicle vs. Multiple Vehicle collisionVehicle types: large trucks, buses

Pedestrian or bicycle involvement Reportable trucks or buses

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Crash Data ElementsDriver Information and Contributing Factors

Driver InformationAgeConditions that can increase crash risk

Blood alcohol level Excessive speed Distraction Fatigue Failure to yield right-of-way or other traffic

violations associated with fatal and serious injury collisions

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Crash Data ElementsInjury Severity KABCO Scale

K – Fatal Crash A – Serious Injury B – Evident Injury C – Possible Injury O – No Apparent Injury

Crash injury severity vs. Individual injury severity level Fatality: when a person dies within 30 days of the crash

because of injuries sustained in the crash Fatal crash: at least one fatality but may include other

injuries23

Crash Data ElementsCrash Type/ Manner of Collision

Examples of categories of manner of collision: Rear-end Angle Sideswipe Run off the road (these crashes may involve impacts

with fixed objects such as guardrail) Head on Pedestrian or Bicycle

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READING A CRASH REPORTPractical exercise

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READING A CRASH DIAGRAMPractical exercise

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REGRESSION TO THE MEAN (RTM)

Key to continued success of targeted solutions to reduce fatalities and serious injuries

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Why is regression to the mean such a big deal? Crash history is a snapshot of short term

crash averagesAverages will change over time Short term averages are not indicative of the

actual long term crash average for a site By accounting for RTM

Funds will be invested where it is most needed to improve safety

Reliable indications of the effectiveness of countermeasures will be known

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Regression-to-Mean (Site selection Bias)

AFTER

BEFORE

Site Selected for Treatment

due to Short-Term Trend

Perceived Effectiveness of Treatment

This change would have happened without the treatment!

RTM Reduction

Actual Reduction due to Treatment

Obs

erve

d C

rash

Fre

quen

cy

Source: Adapted from NCHRP 17-38 32

How do we account for regression to the mean (RTM)? Using advanced methods

Predictive methods such as those in the Highway Safety Manual

Assisted by statistical equations that represent the performance of safety at similar facilities, such as:

Rural two-lane roads 4-lane freeways Signalized intersections

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Summary Crash data and key supporting data are

the foundation for many of our safety related decisions

Better data will enable us to make better decisions with limited resources

We can account for RTM by using statistical methods

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EndModule 2

Questions?

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