Safety-Aware Reinforcement Learning Model for Adaptive Traffic Signal Optimization in Work Zone Environments

Work zones reduce roadway capacity and create unstable merging, queue spillback, and stop-and-go conditions that degrade traffic operations while elevating crash risk. Conventional fixed-time, actuated, and adaptive controllers are poorly suited to these non-stationary conditions, and most reinforcement learning approaches optimize mobility while treating safety only as a post hoc evaluation measure. This study develops a safety-aware Deep Q-Network framework for adaptive signal control at intersections operating near work zone activity areas. Merge conflict risk, upstream spillback propagation, and stop-and-go instability are embedded directly into both the state representation and the reward formulation, alongside operational objectives. A merge-conflict model based on relative spacing, relative speed, and acceleration characterizes unsafe interactions in the merge region, and a Pareto-based procedure samples reward-weight vectors to identify non-dominated policies. The framework was evaluated in a SUMO microscopic simulation of a signalized intersection under lane closure. Relative to default fixed-time control, the selected policy increased throughput by 24.6–37.3% across vehicle classes (p < 0.001; Cohen’s d = 0.53–1.29), with the largest gains for trucks and buses, and reduced maximum queue length by 39.1% and spillback distance by 45.8%. The findings show that a single controller trained with surrogate safety indicators as learning objectives can improve operational performance while reducing safety-critical instability in work zones.