Pedestrian Crashes at High-Speed Intersections: Applying AI for Crash Narrative Analysis to Identify Common and Edge Cases
High-speed intersections, with speed limits of 35 mph or higher, and long crossings are particularly risky for pedestrians. Tragically, in 2022, 983 pedestrians were killed at signalized intersections, representing about 16% of all pedestrian fatalities. Intersections are concerning due to pedestrians' difficulty navigating them and the numerous conflict points they present. The proposed research addresses a critical gap: understanding pedestrian crash descriptors using structured and unstructured (narrative) data on high-speed intersections and identifying simple and complex (or edge) cases—unusual or extreme crashes that deviate substantially from the typical ones. Complex cases represent exceptional circumstances with many contributing factors. Understanding them will help inform current pedestrian safety strategies and help improve how autonomous vehicle algorithms anticipate potential pedestrian conflicts. The research question is: What are the different types of pedestrian crashes and injuries at intersections, and what are their complexity levels? To answer this question, the team will use data from crashes from Tennessee’s Integrated Traffic Analysis Network (TITAN) and Wisconsin’s WisTransPortal, for which the research team has access to police crash narratives. The team will separate pedestrian crashes at high-speed signalized intersections (with speed limits of 35 mph or higher) and compare them to those at intersections with speed limits of less than 35 mph. The team will use AI to develop high-quality, detailed crash descriptors from the narratives of police reports and quantitative crash data. Natural language processing and feature extraction techniques will categorize pedestrian crashes into specific types based on detailed pre-crash actions, human errors, and circumstances obtained from structured and unstructured data. The study will identify edge cases and relevant safety countermeasures (e.g., conflict reductions) while providing a nuanced understanding of crash circumstances (relative to current practice). The study will create a unique and comprehensive crash database that can provide deep insights into the range of injuries, crash attributes (e.g., crash location within the intersection or pedestrian and driver actions), precrash positions, driver and pedestrian impairment, and roadway conditions, and design (e.g., visibility, number of lanes, pedestrian crossing facilities). The study will apply rigorous analysis methods, including unsupervised learning techniques to identify complex cases and inference-based frequentist methods to quantify key correlates of crash injuries. Cluster analysis, specifically through hierarchical or k-means techniques, will differentiate complex crash cases from more common ones, effectively isolating extreme cases deviating from typical patterns. In addition to highlighting the issue of pedestrian crashes at intersections and their correlates, a unique aspect of this study is the identification of complex cases. By doing so, the study aims to uncover the underlying patterns and risk factors that contribute to complex and unusual pedestrian crashes at intersections. Rather than focusing solely on the common crash situations, considering a wide range of possibilities and using a unique database helps to understand and address common and rare cases for high-speed and low/medium-speed intersections (especially relevant, given the adoption of vehicle automation and higher safety standards), ensuring a safer environment for vulnerable road users.
- Record URL:
Language
- English
Project
- Status: Active
- Funding: $59,117.00
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Contract Numbers:
69A3552348336
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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:
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:
Schneider, Robert
Khattak, Asad
- Start Date: 20251201
- Expected Completion Date: 20261130
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Crash data; Data analysis; Pedestrian safety; Pedestrian vehicle crashes; Signalized intersections
- Subject Areas: Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors;
Filing Info
- Accession Number: 01971439
- Record Type: Research project
- Source Agency: Center for Pedestrian and Bicyclist Safety
- Contract Numbers: 69A3552348336
- Files: UTC, RIP
- Created Date: Nov 17 2025 4:29PM