Reinforcement Learning for Efficient Bus Priority Systems: Balancing Transit Efficiency and Traffic Impact

We present two innovative and adaptive bus prioritization strategies, namely the Adaptive Bus Lanes with Intermittent Priority (A-BLIP) and the Bus Lane Density Control (BLDC) that harness the potential of connected vehicles and Vehicle-to-Everything (V2X) communication and aim to enhance the reliability and efficiency of bus operations. Trained through reinforcement learning, they consider busses as intelligent local regulators of traffic, making informed decisions to strike a balance between optimizing bus operations and ensuring the smooth flow of vehicular traffic, based on the specific priorities set by the urban transportation authorities. The intelligent A-BLIP bus agent is trained to dynamically adjust the length of “exclusion zones” based on real-time information on bus delays and traffic conditions, while the BLDC agent controls the density of cars within bus lane sections. The strategies are evaluated in a multi-agent setting under various traffic and public transport demand scenarios and compared against the dedicated bus lanes strategy and mixed traffic conditions. The results demonstrate that both A-BLIP and BLDC achieve improved bus service, while having limited negative impact on traffic conditions, comparable to that of mixed traffic with no dedicated bus lane. Both strategies exhibit adaptability and performance in extreme demand scenarios, making them promising solutions for enhancing urban transportation management and promoting more sustainable mobility choices.