Learning a Spatial Crash Typology Representation for Analyzing and Improving Multimodal Road Safety in New England
The research project titled "Learning a Spatial Crash Typology Representation for Analyzing and Improving Multimodal Road Safety in New England" aims to address the persistent issue of roadway crashes and fatalities by developing a comprehensive understanding of crash patterns and drivers. Leveraging data-driven spatial crash typology, this study seeks to analyze key patterns influencing roadway crashes, considering an array of factors including geography, topology, mode of transit, vehicle type, and driver behavior. The project will collect and integrate crash data from various New England states, applying machine learning and geospatial methods to develop a typology of census tracts based on crash characteristics. The research comprises six tasks, including data collection, dimensionality reduction, clustering, typology pattern analysis, model estimation for crash type prediction, and dissemination of findings. The analysis will utilize supervised learning methods to predict crash type classification of a given census tract, considering network topology, socioeconomic data, and travel behavior. The outcomes of the research will be shared through a published dataset, a dashboard for exploring typology, and model results, enhancing the usability and relevance for state agencies in crash monitoring and mitigation.
Language
- English
Project
- Status: Active
- Funding: $150000
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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: 20240101
- Expected Completion Date: 20241231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
- Subprogram: University Transportation Centers
Subject/Index Terms
- TRT Terms: Classification; Crash causes; Crash types; Highway safety; Machine learning; Monitoring; Multimodal transportation
- Geographic Terms: New England
- Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01905082
- Record Type: Research project
- Source Agency: New England University Transportation Center
- Contract Numbers: 69A3552348301
- Files: UTC, RIP
- Created Date: Jan 19 2024 10:33AM