Reyya, Shriyan (University of Maryland), Cheng, Yao (University of Maryland)

ROADFIRST: An Enhanced Systemic Approach to Safety for Comprehensive Identification and Evaluation of Risk Factors

Scheduled for presentation during the Poster Session "Data Mining and Data Analysis" (FrAT14), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

As an essential supplement to the traditional hotspot crash analysis, the systemic approach to traffic safety, which develops region-wide safety projects based on identified risk factors, has been widely adopted. However, this approach focuses on specific crash and facility types, causing inefficient use of crash and inventory data and a non-optimal risk evaluation and countermeasure selection for each location. To improve the comprehensiveness of the systemic approach to safety, we develop an enhanced process, ROADFIRST, that allows users to identify all potential crash-contributing factors at any location. As the knowledge base for such a process, the quantitative relationships between the contributing factors and features of interest, including traffic-related and environment-related features, are identified using Random Forest and analyzed with the SHapley Additive exPlanations (SHAP) analysis. This study identifies and ranks features impacting the likelihood of three sample contributing factors, namely alcohol-impaired driving, distracted driving, and speeding, according to crash and road inventory data from North Carolina. The introduced models and methods serve as a sample for the further development of ROADFIRST by state and local agencies, which benefits the planning of more comprehensive region-wide safety improvement projects.