Working with crash data
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Transcript of 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
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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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