Pedestrian Exposure for Crash Prediction
This follow-on builds upon the framework established in a previous task order. Using data from Seattle/King County, Washington, the team developed and tested models of pedestrian exposure. The next step is to explore crash likelihood. At the intersection level, the relationship between pedestrian exposure measures (variables) and crash likelihood will be investigated. Analytical models will be developed that predict likelihood of pedestrian-vehicle crashes given pedestrian exposure measures. In addition, a sample of intersections will be used to examine associations between equity related factors (e.g., median household income, presence of sidewalks) and crashes. This knowledge will help in future efforts in the development of tailored safety programs that encourage a Safe System approach wherein traffic safety professionals and other stakeholders can work together to implement multi-component countermeasure solutions to protect all road users.
Language
- English
Project
- Status: Active
- Funding: $175358
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Contract Numbers:
GSA Contract # GS00F084CA; Task Order # 693JJ923F000304
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Sponsor Organizations:
National Highway Traffic Safety Administration
1200 New Jersey Avenue, SE
Washington, D.C. United States 20590 -
Project Managers:
Hamel, Rachel
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Performing Organizations:
Battelle Memorial Institute
Columbus Laboratories
Columbus, OH United States 43201 - Start Date: 20230929
- Expected Completion Date: 20250331
- Actual Completion Date: 0
- USDOT Program: Behavioral Safety Research
Subject/Index Terms
- TRT Terms: Crash risk forecasting; Equity; Intersections; Pedestrian safety; Pedestrian vehicle crashes
- Identifier Terms: Safe System
- Geographic Terms: King County (Washington)
- Subject Areas: Highways; Pedestrians and Bicyclists; Safety and Human Factors;
Filing Info
- Accession Number: 01895007
- Record Type: Research project
- Source Agency: National Highway Traffic Safety Administration
- Contract Numbers: GSA Contract # GS00F084CA; Task Order # 693JJ923F000304
- Files: RIP, USDOT
- Created Date: Oct 2 2023 7:47AM