Preemptive crash risk reduction through a real-time cost-based safety prediction model (RECOSAM) for traffic signal control

Abstract

This paper proposes a novel real-time cost-based safety prediction model (RECOSAM) and incorporating it in intersection traffic signal control optimisation, complementing the recent advances in deep reinforcement learning (RL)-based adaptive traffic signal control (ATSC). The primary contribution is the development of RECOSAM, a model designed to predict traffic safety risks one step ahead of time, for various signal phase configurations at intersections. The proposed model offers a dynamic safety evaluation strategy, estimating near-future safety metrics for seamless integration into machine learning-based ATSC systems. Extensive experiments validate the model's effectiveness, demonstrating..