Modeling Real-Time Cycle-Level Crash Risk at Signalized Intersections Based on High-Resolution Event-Based Data

Abstract

In the context of pro-active traffic management, real-time crash risk evaluation is one of the most critical components. Signalized intersections are well-known high-risk locations because of the variety of traffic movements, modes, and their interactions. Unlike access-controlled freeways, the traffic flow at signalized intersections presents cyclical characteristics, which are temporally separated by traffic signals. Therefore, the data preparation for real-time crash risk prediction at signalized intersections should be based on the signal cycle rather than a predefined fixed time interval (e.g., 5 minutes). In this research, the corresponding signal cycles where crashes occurred were verified based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). Six types of real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather. Two undersampling strategies, matched case-control and random undersampling, were utilized to develop conditional logistic and binary logistic models, respectively. Model results indicate that the random undersampling performs better than the matched case-control method. It was found that higher cycle volume, overall average flow ratio across lanes, arrivals on yellow ratio, traffic volatility across approach sections, as well as longer cycle length and lower green ratio could significantly increase the odds of crash occurrence at signalized intersections. Moreover, longer maximum queue length, bigger shockwave, and higher absolute queuing shockwave speed tend to increase the odds of crash occurrence.