CATS: Context-Aware Traffic Signal Control with Road Navigation Service for Connected and Automated Vehicles

Urban intersection traffic signals play a crucial role in managing traffic flow and ensuring road safety. However, traditional actuated signal controllers make phase-switching decisions based on limited local traffic information, without leveraging network-wide context from navigation services. In this paper, we propose CATS, a Context-Aware Traffic Signal control system that jointly optimizes intersection signal control and road navigation for Connected and Automated Vehicles (CAVs). CATS integrates two key components: a Best-Combination CTR (BC-CTR) scheme and the Self-Adaptive Interactive Navigation Tool (SAINT). BC-CTR enhances the original Cumulative Travel-time Responsive (CTR) scheme by selecting the phase with the highest cumulative travel time (CTT) first and then identifying the compatible phase combination with the greatest group CTT, allowing more accurate response to real-time intersection demand. SAINT provides congestion-aware route guidance via a congestion aware mechanism, directing vehicles away from congested segments while signal timings simultaneously adapt to incoming traffic. By comparing with other baselines, our simulation results show that under moderate-to-heavy traffic conditions, CATS reduces mean end-to-end travel time by up to 23.72% and improves throughput by up to 93.19% over the baselines, confirming that the co-design of navigation and signal control produces complementary benefits.