Dynamic traffic flow and route choice modelling represent a vibrant research area that examines how travellers adjust their selected paths over time amid complex transport networks. By integrating ...

Traffic flow modelling and dynamics are at the forefront of efforts to understand and optimise urban transport systems. The field integrates theoretical and computational approaches to depict and ...

The MARL-OD-DA framework redesigns multi-agent reinforcement learning by using OD-pair–level agents and Dirichlet-based continuous routing actions, enabling scalable and stable traffic assignment in ...