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The National Academies

BTSCRP BTS-14 [Anticipated]

Understanding and Analyzing Crash Contributing Factors (Joint project with NCHRP 22-45)

  Project Data
Funds: $50,000
Staff Responsibility: William C. Rogers
Comments: Joint project with NCHRP 22-45
Fiscal Year: 2020

This project has been tentatively selected and a project statement (request for proposals) is expected to be available on this website. The problem statement below will be the starting point for a panel of experts to develop the project statement.

Successful safety management practices require a thorough understanding of the factors contributing to crashes. The continuous advancements in the science of data-driven safety analysis, as well as the countermeasures and technologies available for addressing crashes, create challenges in maintaining a safety workforce that is always proficient in the state of the practice. In many cases, agencies continue to use approaches, such as descriptive statistics and anecdotal information to perform this diagnostic assessment without a thorough understanding of what should be expected for a given context or road type. A secondary issue is that once the nature of the crashes at a location are assessed, choosing an effective countermeasure requires an examination of the human factors, behavioral factors, future development, prevailing or predicted crash type(s) or mix of road users to determine the most appropriate treatments to apply. Doing so allows the selected countermeasure to reduce crashes to the greatest extent possible. However, in many cases, practitioners have limited experience and background to assess these contributing factors, reducing the likelihood of safety investment success. Further, the practitioner may have limited understanding of the potential for a treatment to increase exposure to the more vulnerable road users. For instance, installing a turn lane might also increase vehicle speeds or crossing distance. By having a better understanding of these tradeoff, changes can be made in the design and operations of facilities upfront, rather than waiting for crashes to occur before addressing the less than optimal road design.

The objective of this research is to assess best practices in crash diagnosis across crash types in modal diverse contexts, recognizing that vehicle and mode mix matters in the success of investment strategies. The research will then develop additional diagnostic tools that leverages the availability of crash, roadway, traffic volume, human factors, behavioral, socioeconomic, and demographic data to advance the art of the practice in crash diagnostics that consider both modal priority and facility context. It is anticipated that the research effort to meet the objective would include the following tasks:
1.     Identify the previous research on crash diagnosis and countermeasure selection for various modes of transportation and determine if the necessary research literature is coordinated, not coordinated, or contradictory.
2.     Outline a process or procedure for identifying what steps are needed to develop enhanced or new comprehensive diagnostic assessment methods for determining crash-contributing factors across modes and in different roadway contexts using crash type, severity, and integrated safety and other data for the system.
3.     Identify a technical working group of safety engineers, designers, traffic engineers, planners, behavioral experts, and others to provide input to the project and to review these diagnostic and countermeasure selection tools as they are proposed and developed. Note that the focus of the project is not to capture existing practice but to advance the limited approaches currently deployed.
4.     Meet with selected transportation and safety organizations, such as state DOTs, state highway safety offices, FHWA, NHTSA, TRB committee(s), and others, to identify needed skills for understanding crash diagnostics and countermeasure selection that is responsive to the needs of mixed modes of traffic across the five contexts used in the AASHTO Green Book.
5.     Develop new diagnostic assessment and countermeasure selection tools considering, multiple factors, multimodal needs and multiple contexts, along with draft instructions for users and materials describing the basis for the tools, assumptions, and limitations for application.
6.     Plan and conduct two pilot data–driven diagnostic assessments and countermeasures selection tools to: (1) illustrate how multimodal road use safety needs can be addressed using the newly developed tools; and (2) how other existing safety tools (including the human factors guides and mode specific tools) can be used to supplement these activities. Use the pilots to update and clarify new or existing materials and analysis approaches (e.g., Every Day Counts initiative on Data–Driven Safety Approaches, and road safety audits).
7.     Update the diagnostic assessment and countermeasure selection tools, accompanying documentation (including the basis for the tools, assumptions, and limitations for application). 
8.     Determine the most effective method to disseminate and share this information, including how to incorporate this research when using these diagnostic and countermeasure selection tools with data driven safety analysis, from the development through implementation of safety projects.

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