Uncovering Information Hidden in Crash Narratives: Enhancing Safety Reporting for Pedestrians and Bicyclists in Transit Bus Collisions
Pedestrians and bicyclists are among the least protected road users in traffic environments, disproportionately affected by traffic crashes. In 2022, the National Highway Traffic Safety Administration reported 42,795 traffic fatalities in the United States, including 7,522 pedestrians (18 percent) and 1,105 bicyclists (3 percent). Transit buses were involved in 6,731 crashes in 2022, including collisions with pedestrians and bicyclists. These statistics emphasize the critical safety risks faced by pedestrians and bicyclists, particularly in interactions with transit buses. These interactions often result in severe outcomes due to the physical characteristics of buses and the increased exposure of pedestrians and bicyclists. Despite ongoing improvements in crash reporting, existing datasets lack detailed behavioral and contextual factors that can inform targeted interventions. Structured data alone often fails to capture important elements such as pedestrian/bicyclist distraction, alcohol impairment, pedestrian crossing violations, or unusual crash scenarios. As a result, safety professionals and transit agencies face limitations when developing targeted strategies to reduce injury severity and prevent future incidents. This project addresses these deficiencies by leveraging the rich, crash narrative data in the National Transit Database Major Safety Events Dataset. This project applies advanced artificial intelligence and natural language processing methods, including text mining, topic modeling (Latent Dirichlet Allocation), hierarchical clustering, and contextual analysis, to extract meaningful patterns from thousands of crash narratives involving pedestrians and bicyclists. Initial results from over 5,600 crash narratives demonstrate that 20 distinct crash topics can be identified using topic modeling. These topics represent a wide range of behavioral and situational factors, such as pedestrian and bicyclist distraction, limited visibility, and boarding or alighting incidents. By transforming unstructured text into structured indicators, this project enhances transit safety reporting and provides new insights to inform safety practices for pedestrians and bicyclists involved in bus-related crashes.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $60,039.00
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Contract Numbers:
69A3552348336
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
Department of Transportation
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Managing Organizations:
Office of the Assistant Secretary for Research and Technology
Department of Transportation
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Project Managers:
Stearns, Amy
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Performing Organizations:
University of Tennessee, Knoxville
Center for Transportation Research (CTR)
Knoxville, TN United States 37996 -
Principal Investigators:
Brakewood, Candace
Khattak, Asad
- Start Date: 20251201
- Expected Completion Date: 20261130
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Artificial intelligence; Bicycle safety; Bus crashes; Crash reports; Data analysis; Pedestrian safety
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Public Transportation; Safety and Human Factors;
Filing Info
- Accession Number: 01971437
- Record Type: Research project
- Source Agency: Center for Pedestrian and Bicyclist Safety
- Contract Numbers: 69A3552348336
- Files: UTC, RIP
- Created Date: Nov 17 2025 4:09PM