Publication

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

2025

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/97ed2413-78ee-4002-9bb4-203a448a73a7

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2025

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/97ed2413-78ee-4002-9bb4-203a448a73a7

About this result

Related concepts (30)

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

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