MARL-based cooperative transit signal priority for the arterial road to reduce schedule delay
Document Type
Journal Article
Publication Date
2025
Subject Area
mode - bus, infrastructure - bus/tram priority, infrastructure - traffic signals, operations - traffic, operations - coordination
Keywords
Transit signal priority, traffic signal control, multi-agent reinforcement learning, arterial road
Abstract
Transit signal priority (TSP) is an effective strategy to reduce transit delays and improve intersection efficiency. This paper introduces a Cooperative TSP strategy of Variable phase (CTSPV) using multi-agent reinforcement learning (MARL) to minimize transit schedule delays on arterial roads. The agents adjust phase sequences and durations based on real-time traffic, balancing transit and non-transit vehicle needs, resolving conflicting bus requests, and ensuring agent cooperation. Invalid action masking ensures compliance with green time and phase-skipping rules. Simulation results show CTSPV reduces person delay, queue lengths, and lateness by 8.7%, 31.6%, and 17.0%, respectively, compared to fixed-time signals. Testing different green time constraints highlights the importance of proper restrictions for efficient learning. Analysis of CTSPV's signal timing reveals agents prioritize phases with high traffic demand and bus priority, skipping phases with lower demand. Evaluation results of generalized rule-based strategies based on those RL-derived patterns demonstrate the good performance of RL-learned knowledge.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Long, M., Wang, R., Chen, J., Chung, E., & Oguchi, T. (2025). MARL-based cooperative transit signal priority for the arterial road to reduce schedule delay. Transportmetrica B: Transport Dynamics, 13(1), 2564703.
