Ride-Hailing Vehicle Rebalancing Strategies Under Disruptions: A Case Study in Athens

The global ride-hailing (RH) industry plays an essential role in multi-modal transportation systems by improving user mobility, particularly as first- and last-mile solutions. While many RH rebalancing studies focus on nominal scenarios with regular demand patterns, it is crucial to consider disruptions that negatively impact operational efficiency. This study examines how RH rebalancing strategies can strengthen the resilience of multimodal transportation systems against supply-side disruptions. We incorporate RH services into systems where users choose and switch transportation modes based on their preferences, accounting for uncertainties in demand predictions. To address the stochastic supply-demand dynamics, we propose a multi-agent reinforcement learning (MARL) strategy, specifically utilizing a multi-agent deep deterministic policy gradient (MADDPG) approach. The results demonstrate significant improvements in key performance indicators, including user waiting time, resilience metrics, total travel time, and travel distance.