Integrating Congestion Factors into Traffic Safety Analysis to Improve Crash Risk Identification in California

Research Team: Julia Griswold (lead) and Jean Doig Godier

UC Campus(es): UC Berkeley

Problem Statement: Crashes in congested traffic conditions fundamentally differ from those in free-flowing traffic, yet traditional crash monitoring often overlooks congestion as a factor.

Project Description: This research project aims to understand how reduced congestion during the COVID-19 recovery impacted serious injury crashes in California. Utilizing data from Caltrans' PeMS and UC Berkeley SafeTREC's TIMS platforms, the study will incorporate congestion metrics into predictive models for identifying high-risk road segments, enhancing methodologies prescribed in the Highway Safety Manual. The project team will calibrate and test regression models, including congestion metrics, to identify congestion significance as a predictor of traffic crashes. The new models will be compared with traditional methods to evaluate their effectiveness in detecting high-risk locations. Expected outcomes include better-targeted safety interventions, cost savings, and enhanced traffic safety policies in California.

Status: In Progress

Budget: $30,000