Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (1): 50-55.DOI: 10.11772/j.issn.1001-9081.2019060988

• Artificial intelligence • Previous Articles     Next Articles

Collaborative routing method for operation vehicle in inland port based on game theory

FAN Jiajia1,2, LIU Hongxing1,2, LI Yonghua1,2, YANG Lijin1,2   

  1. 1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan Hubei 430063, China;
    2. Hubei Key Laboratory of Transportation Internet of Things(Wuhan University of Technology), Wuhan Hubei 430070, China
  • Received:2019-06-12 Revised:2019-07-31 Online:2020-01-10 Published:2019-09-27
  • Supported by:
    This work is partially supported by the Fund of Hubei Key Laboratory of Inland Shipping Technology (NHHY2017003), the Fund of Hubei Key Laboratory of Transportation Internet of Things (2017III028-002).

基于博弈论的内河港口作业车辆协同选路方法

范家佳1,2, 刘洪星1,2, 李勇华1,2, 杨丽金1,2   

  1. 1. 武汉理工大学 计算机科学与技术学院, 武汉 430063;
    2. 交通物联网技术湖北省重点实验室(武汉理工大学), 武汉 430070
  • 通讯作者: 李勇华
  • 作者简介:范家佳(1994-),男,重庆人,硕士研究生,主要研究方向:智能交通、调度优化;刘洪星(1963-),男,湖北洪湖人,教授,博士,主要研究方向:智能交通、调度优化;李勇华(1977-),男,湖北武汉人,副教授,博士,主要研究方向:智能交通、调度优化;杨丽金(1994-),女,河北张家口人,硕士研究生,主要研究方向:智能交通、调度优化。
  • 基金资助:
    内河航运技术湖北省重点实验室基金资助项目(NHHY2017003);交通物联网技术湖北省重点实验室基金资助项目(2017III028-002)。

Abstract: Focusing on the traffic congestion problem in inland ports with vehicle transportation and large throughput, a collaborative routing method for operation vehicles in inland port based on game theory was proposed. Firstly, the interaction between the operation vehicles that simultaneously request route planning was modeled as a game with incomplete information and the idea of Satisfaction Equilibrium (SE) was applied to analyze the proposed game. It was assumed that every vehicle has an expected utility for routing result, when all vehicles were satisfied, the game achieved an equilibrium. Then, a collaborative routing algorithm was proposed. In this algorithm, firstly every vehicle selected the route according to greedy strategy, then all vehicles were divided into groups by the rule and vehicles in the group performed adaptive learning based on historical routing results to complete the game. The experimental results show that the collaborative routing algorithm reduces the average driving time of vehicles up to 50.8% and 16.3% respectively and improves the system profit up to 51.7% and 24.5% respectively compared with Dijkstra algorithm and Self-Adaptive Learning Algorithm (SALA) when the number of simultaneously working vehicles in port is 286. The proposed algorithm can effectively reduce the average driving time of vehicles, improve system profit, and is more suitable for the routing problem of vehicles in inland port.

Key words: traffic congestion, vehicle routing problem, route planning, inland port, game theory

摘要: 针对以汽车运输为主且吞吐量较大的内河港口的交通拥堵问题,提出一种基于博弈论的内河港口作业车辆协同选路方法。首先,基于港口路网特征与车辆作业特点,将同时请求路径规划的作业车辆间的交互建模为不完全信息博弈,采用满足均衡(SE)的概念来分析该博弈。假设每个车辆对选路效用都有一个预期,当所有车辆都得到满足时博弈即达到均衡。然后,提出了一种车辆协同选路算法,算法中每个车辆首先按照贪心策略初始选路,之后将所有车辆按规则分组,组内车辆根据历史选路结果进行适应性学习并完成博弈。实验结果表明,当港区同时作业车辆数为286时,协同选路算法的车辆平均行驶时间分别比Dijkstra算法和自适应学习算法(SALA)少50.8%和16.3%,系统收益分别比Dijkstra算法和SALA提高51.7%和24.5%。所提算法能够有效减少车辆平均行驶时间,提高系统收益,更适用于内河港口车辆选路问题。

关键词: 交通拥堵, 车辆选路问题, 路径规划, 内河港口, 博弈论

CLC Number: