Two-Phase Scheduling for Efficient Vehicle Sharing

被引:8
|
作者
Liu, Ji [1 ]
Bondiombouy, Carlyna [2 ]
Mo, Lei [3 ]
Valduriez, Patrick [4 ]
机构
[1] Big Data Lab, Baidu Res, Beijing 100085, Peoples R China
[2] African Ctr Excellence Math Sci IT & Applicat, 01 BP 613, Porto Novo, Benin
[3] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[4] Univ Montpellier, LIRMM, CNRS, INRIA, F-34095 Montpellier, France
关键词
Scheduling; Schedules; Vehicles; Urban areas; Task analysis; Scheduling algorithms; Vehicle sharing; path planning problem; scheduling; optimization; SCIENTIFIC WORKFLOWS; TRAVEL;
D O I
10.1109/TITS.2020.3011952
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页码:457 / 470
页数:14
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