IEEE Access (Jan 2024)

Dynamic Matching Optimization in Ridesharing System Based on Reinforcement Learning

  • Hiba Abdelmoumene,
  • Chemesse Ennehar Bencheriet,
  • Habiba Belleili,
  • Islem Touati,
  • Chayma Zemouli

DOI
https://doi.org/10.1109/ACCESS.2024.3369041
Journal volume & issue
Vol. 12
pp. 29525 – 29535

Abstract

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Modern urban transportation, has concurrently posed environmental challenges such as traffic congestion and increased greenhouse gas emissions. In response to these issues, ridesharing systems have emerged as a viable solution. By fostering ridesharing among individuals with similar travel routes, ridesharing, effectively, optimizes vehicle utilization, offering a sustainable and practical alternative to address contemporary transportation challenges. In this work, we delve into intricacies of dynamic ridesharing systems. Focusing on the dynamic matching problem within ridesharing, we propose a solution leveraging reinforcement learning. Our contribution involves the distinct modeling of two scenarios: one-to-one and one-to-many ridesharing. In the one-to-one scenario, spatiotemporal constraints are considered with the objective of minimizing passengers’ waiting times. In the more complex one-to-many scenario, additional constraints are introduced focusing on both minimizing passengers’ waiting times and drivers’ detour times. The proposed modeling is time-focused assuming that time is a cutting parameter in the decision-making. The results obtained through our experiments demonstrate the system’s effectiveness, robustness and adaptability to diverse constraints.

Keywords