Cooperative adaptable lanes for safer shared space and improved mixed-traffic flow

With the rapid increase in the percentage of the world's population living in cities, the design of existing transportation infrastructure requires serious consideration. Current road networks, especially in large cities, face acute pressures due to increased demand for vehicles, cyclists, and pedestrians. Although much attention has been given to improve traffic management and accommodate the increased demand via coordinating and optimizing traffic signals, research focused on adapting the static allocation of street spaces and right-of-way dynamically based on mixed traffic flow is still scarce. This paper proposes a multi-agent reinforcement learning (RL) agent approach that cooperatively adapts the individual lane widths and right-of-way access permissions based on real-world mixed traffic flow. In particular, multiple cooperative agents are trained with mixed temporal data that learn to decide suitable lane widths for motorized vehicles, bicycles, and pedestrians, along with whether co-sharing space between pedestrians and cyclists is safe. Using a microscopic traffic simulator model of a four-legged intersection, we trained our RL agent on synthetic data, and tested it on realistic multi-modal traffic data. The proposed approach reduces the overall average waiting time and queue length by 48.9% and 37.7%, respectively, compared to the Static (baseline) street design. Additionally, we observe CALM's ability to gradually adjust lane widths, contrasting with the Heuristic implementation's erratic lane adjustments, which pose potential safety concerns. Notably, the model learns to adaptively toggle the co-sharing of street space between cyclists and pedestrians as one co-shared lane, ensuring comfort and maintaining the level of service according to the designer's policy. Finally, we demonstrate CALM's scalability on a simulated large-scale traffic network.