Real-Time Lane Configuration with Coordinated Reinforcement Learning

Changing lane configuration of roads, based on traffic patterns, is a proven solution for improving traffic throughput. Traditional lane-direction configuration solutions assume pre-known traffic patterns, hence are not suitable for real-world applications as they are not able to adapt to changing traffic conditions. We propose a dynamic lane configuration solution for improving traffic flow using a two-layer, multi-agent architecture, named Coordinated Learning-based Lane Allocation (CLLA). At the bottom-layer, a set of reinforcement learning agents find a suitable configuration of lane-directions around individual road intersections. The lane-direction changes proposed by the reinforcement learning agents are then coordinated by the upper level agents to reduce the negative impact of the changes on other parts of the road network. CLLA is the first work that allows city-wide lane configuration while adapting to changing traffic conditions. Our experimental results show that CLLA can reduce the average travel time in congested road networks by 20% compared to an uncoordinated reinforcement learning approach.