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<\/figure>\n\n\n\nConceptual framework of Semantic Urban Elements for adaptable services and infrastructures based on
citizen participation. <\/p>\n\n\n\n
With the rapid increase in the percentage of the world\u2019s 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 significantly reduced average waiting time by 73.3%, 5.9%, and 36.6% and reduces the average queue ength by 56.1%, 4.4%, and 36.9% with respect to Static, Heuristic, and PPO-based adaptive models, respectively. Moreover, the model learned to adaptively toggle co-sharing of the street space between cyclists and pedestrians as one co-shared lane, keeping the comfort and level of service in accordance with the designer\u2019s policy.<\/p>\n\n\n\n