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Unsupervised Approach to Investigate Urban Traffic Crashes Based on Crash Unit, Crash Severity, and Manner of Collision
Principal Investigator:
Farzin Maniei, University of Texas, Arlington
Co-Investigators:
- Stephen Mattingly, University of Texas, Arlington
Summary:
Both crash frequency analysis (CFA) and real-time crash prediction models (RTCPMs) typically divide a highway into segments with a constant length for data aggregation. Despite the significant impact of the segment length in several previous traffic safety studies, no standard approach exists for determining a recommended segment length for crash data aggregation. This research investigates the im...
Both crash frequency analysis (CFA) and real-time crash prediction models (RTCPMs) typically divide a highway into segments with a constant length for data aggregation. Despite the significant impact of the segment length in several previous traffic safety studies, no standard approach exists for determining a recommended segment length for crash data aggregation. This research investigates the impact of fragment size (segment length) on traffic crash data aggregation and establishes a methodology for determining a recommended fragment size (RFS).
The study defines featured traffic crash rates using three major traffic crash characteristics: number of vehicles in crash, manner of collision, and crash severity. The analysis uses K-means to cluster highway segments based on the featured crash rates (FCRs) from fragment sizes (segment lengths) ranging from 0.10 to 0.25 mile with an increment of 0.01 mile and evaluates the clustering results using their silhouette coefficients.
The RSL method provides a standardized approach for selecting segment length, which benefits CFA studies. Also, crash hotspots will be identified using traffic crash count models developed based on traffic crash data aggregation for each fragment size ranging from 0.1 to 0.25 mile with an increment of 0.01 mile.
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Project Details
| Project Type: | Dissertation |
|---|---|
| Project Status: | Completed |
| End Date: | December 31, 2023 |
| UTC Funding: | $15,000 |