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Module 2. Working with crash data. 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

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working with crash data
Module 2

Working with crash data

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

July 22, 2013 - Boise, Idaho

learning objectives
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)

potential crash data sources1
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

if we don t have access to a state or regional crash database
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 tools
  • Online mapping tool
  • Actual data downloads available (raw data)
over represented crash locations
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 facilities
crash data considerations
Crash data considerations






Value added by data Integration


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

data consistency
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 documented

  • Example – road names/ route numbers
  • Changes to the state crash report form

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 solutions
  • Other data include: traffic volume, roadway cross-section and alignment data, presence and control type of intersections, posted speeds

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

Access to the data

  • Raw data is better than no data
  • Periodic standard reports are particularly valuable
  • Ease of use (GIS data, query tools, data export)
  • Availability and access to data dictionaries and coding manuals
data integration
Data Integration

Link crash data, traffic volume, roadway characteristics

Integrating data systems at state and local level

  • Consistent data elements
  • Consistent data structures
  • Consistent quality control measures
reading a crash report background
Reading a Crash Report: Background

Many to One Relationships (example)

crash report elements where when how who what
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.)
crash data elements where and when
Crash Data ElementsWhere and when

Crash location

  • Critical for being able to understand how different locations on the roadway network are performing with regards to safety

Time of day

  • Useful for understanding if there are periods of the day that are over represented in terms of the frequency or severity of crashes
crash data elements environmental factors
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

crash data elements user and vehicle type s
Crash Data ElementsUser and Vehicle Type(s)


  • Pedestrians, bicyclists, and the particularly vulnerable (the young and older users)


  • Single Vehicle vs. Multiple Vehicle collision
  • Vehicle types: large trucks, buses

Pedestrian or bicycle involvement

Reportable trucks or buses

crash data elements driver information and contributing factors
Crash Data ElementsDriver Information and Contributing Factors

Driver Information

  • Age
  • Conditions 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
crash data elements injury severity
Crash Data ElementsInjury Severity


  • 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 injuries
crash data elements crash type manner of collision
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
why is regression to the mean such a big deal
Why is regression to the mean such a big deal?

Crash history is a snapshot of short term crash averages

  • Averages 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
regression to mean site selection bias
Regression-to-Mean (Site selection Bias)

Site Selected

for Treatment

due to Short-Term Trend

RTM Reduction

Observed Crash Frequency

Perceived Effectiveness of Treatment


Actual Reduction due to Treatment


This change would have happened without the treatment!

Source: Adapted from NCHRP 17-38

how do we account for regression to the mean rtm
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

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

end module 2

EndModule 2