5. Reinforcement Learning for Mobility Under Traffic Disruptions

Traffic networks can be significantly affected by unexpected events such as accidents, road closures or sudden congestion. Conventional route-planning approaches may struggle to adapt efficiently to these changes. This project investigates whether reinforcement learning can learn adaptive routing strategies that respond to unexpected disruptions. The learned approach will be evaluated in a simulated transport network by introducing different disruptions and measuring how effectively it can redirect travellers while avoiding the creation of additional congestion.