Explainable Reinforcement Learning for Adaptive Traffic Signal Control

arXiv:2605.08516v1 Announce Type: new Abstract: Transparent decision-making is essential for traffic signal control (TSC) systems to earn public trust. However, traditional

arXiv:2607.22525v1 Announce Type: new Abstract: To effectively integrate AI into high-stakes, critical environments such as healthcare, autonomous driving, and aviation--and to

arXiv:2508.20784v2 Announce Type: replace Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely

arXiv:2603.18266v1 Announce Type: cross Abstract: Traffic microsimulators are widely used to evaluate road network performance under various ``what-if" conditions. However, the

arXiv:2604.27753v1 Announce Type: new Abstract: This article outlines a new framework of traffic light optimization through a digital twin of the transport infrastructure, managed

arXiv:2603.25328v1 Announce Type: new Abstract: Automated Vehicle (AV) control in mixed traffic, where AVs coexist with human-driven vehicles, poses significant challenges in

arXiv:2607.01639v1 Announce Type: new Abstract: Universal traffic laws describe recurrent patterns in congestion, mobility and driving behavior across cities, providing a

arXiv:2606.27381v1 Announce Type: cross Abstract: Queue overflow, a severe consequence of urban traffic congestion, occurs when vehicle queues exceed intersection capacity,