Real-Time Crash-Risk Optimization at Signalized Intersections

Abstract

Over the past few decades, numerous adaptive traffic signal control (ATSC) algorithms have been proposed to alleviate traffic congestion and optimize traffic mobility using real-time traffic data, such as data from connected vehicles (CVs). However, most of the existing ATSC algorithms do not consider optimizing traffic safety, likely because of the lack of tools to evaluate safety in real time. In this paper, we propose a novel ATSC algorithm for real-time safety optimization. The algorithm utilizes a traditional Reinforcement Learning approach (i.e., Q-learning) as well as recently developed extreme value theory (EVT) real-time crash prediction models. The algorithm was validated using real-world traffic video data collected from two signalized intersections in British Columbia. The results indicated that, compared with an existing fully actuated signal controller, the developed algorithm can significantly reduce the real-time crash risk by 43% to 45% at the intersection’s approaches even at low CVs market penetration rates.

Use of Universal Simulation Software Tools for Optimization of Signal Plans at Urban Intersections

“…One of the approaches for optimizing traffic light schedules is multicriteria evaluation, which has been addressed by several authors in their publications [10][11][12][13]. Another approach is developing a novel self-learning Adaptive Traffic Signal Control algorithm to reduce traffic accidents on the intersections, which can be found in various studies [14,15]. Integrated Coordinated Traffic Management was also addressed by Gomes G, Gang Qiijan, and Bayen A as part of a performance assessment methodology for model traffic forecasting systems [16].…”