Open AccessDynamic Matching Optimization in Ridesharing System based on Reinforcement Learning
Author(s) -
Hiba Abdelmoumene,
Chemesse Ennehar Bencheriet,
Habiba Belleili,
Islem Touati,
Chayma Zemouli
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3369041
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.