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Two-Phase Scheduling for Efficient Vehicle Sharing

Authors :
Patrick Valduriez
Lei Mo
Ji Liu
Carlyna Bondiombouy
Baidu Research
African Institute for Mathematical Sciences (AIMS)
Southeast University [Jiangsu]
Scientific Data Management (ZENITH)
Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)
Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Source :
IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Transportation Systems, 2022, 23 (1), pp.457-470. ⟨10.1109/TITS.2020.3011952⟩, IEEE Transactions on Intelligent Transportation Systems, IEEE, In press, pp.1-14. ⟨10.1109/TITS.2020.3011952⟩
Publication Year :
2022
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2022.

Abstract

International audience; Cooperative Intelligent Transport Systems (C-ITS) is a promising technology to make transportation safer and more efficient. Ridesharing for long-distance is becoming a key means of transportation in C-ITS. In this paper, we focus on private long-distance ridesharing, which reduces the total cost of vehicle utilization for long-distance journeys. In this context, we investigate journey scheduling problem with shared vehicles to reduce the total cost of vehicle utilization. Most of the existing works directly schedule journeys to vehicles with long scheduling time and only consider the cost of driving travellers instead of the total cost. In contrast, to reduce the total cost and scheduling time, we propose a comprehensive cost model and a two-phase journey scheduling approach, which includes path generation and path scheduling. On this basis, we propose two path generation methods: a simple near optimal method and a reset near optimal method as well as a greedy based path scheduling method. Finally, we present an experimental evaluation with different path generation and path scheduling methods with synthetic data generated based on real-world data. The results reveal that the proposed scheduling approach significantly outperforms baseline methods in terms of total cost (up to 69.8%) and scheduling time (up to 84.0%) and the scheduling time is reasonable (up to 0.16s). The results also show that our approach has higher efficiency (up to 141.7%) than baseline methods.

Details

ISSN :
15580016 and 15249050
Volume :
23
Database :
OpenAIRE
Journal :
IEEE Transactions on Intelligent Transportation Systems
Accession number :
edsair.doi.dedup.....e8c333cfd28077153f3aca91d6fa3e12
Full Text :
https://doi.org/10.1109/tits.2020.3011952