Investigating Transit Characteristics and Road Safety Outcomes
Public transit systems influence roadway safety through changes in travel behavior, congestion levels, and modal distribution, yet the specific mechanisms linking transit characteristics to safety outcomes are not well quantified. While prior studies suggest that higher transit use is associated with improved safety, agencies lack clear guidance on which system features contribute most to these effects. This project addresses that gap through large-scale data integration and predictive modeling. The research will combine crash data, transit system attributes, roadway network characteristics, and demographic indicators from national and regional data sources. Machine learning models will be developed to predict crash rates as a function of transit network size, service intensity, demand, and multimodal shares. Explainable modeling techniques will be used to identify the most influential predictors of safety outcomes at both metropolitan and town levels. Results will provide actionable evidence to support data-driven transit planning and roadway safety strategies.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $50,000.00
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Contract Numbers:
69A3552348301
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Performing Organizations:
University of Massachusetts, Amherst
Department of Civil and Environmental Engineering
130 Natural Resources Road
Amherst, MA United States 01003 -
Principal Investigators:
Oke, Jimi
- Start Date: 20260101
- Expected Completion Date: 20261231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Crash data; Data fusion; Machine learning; Metropolitan areas; Predictive models; Public transit; Traffic safety
- Geographic Terms: New England
- Subject Areas: Data and Information Technology; Operations and Traffic Management; Public Transportation; Safety and Human Factors;
Filing Info
- Accession Number: 01974415
- Record Type: Research project
- Source Agency: New England University Transportation Center
- Contract Numbers: 69A3552348301
- Files: UTC, RIP
- Created Date: Dec 18 2025 2:54PM