Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (11): 3376-3384.DOI: 10.11772/j.issn.1001-9081.2020122004

• Frontier and comprehensive applications • Previous Articles     Next Articles

Time-space distribution identification method of taxi shift based on trajectory data

Fumin ZOU1,2,3, Sijie LUO1,2,3(), Zhihui CHEN1,2,3, Lyuchao LIAO1,2,3   

  1. 1.Fujian Key Laboratory of Automotive Electronics and Electric Drive (Fujian University of Technology),Fuzhou Fujian 350118,China
    2.Beidou Navigation and Smart Traffic Innovation Center of Fujian Province (Fujian University of Technology),Fuzhou Fujian 350118 China
    3.Fujian Provincial Big Data Research Institute of Intelligent Transportation (Fujian University of Technology),Fuzhou Fujian 350118,China
  • Received:2020-12-21 Revised:2021-05-21 Accepted:2021-08-03 Online:2021-11-29 Published:2021-11-10
  • Contact: Sijie LUO
  • About author:ZOU Fumin, born in 1976, Ph. D., professor. His research interests include traffic information processing,mobile application of wireless broadband network
    LUO Sijie,born in 1995,M. S. candidate. His research interests include trajectory data mining
    CHEN Zhihui,born in 1995,M. S. candidate. His research interests include trajectory data mining
    LIAO Lyuchao,born in 1980,Ph. D.,associate professor. His research interests include deep learning,data mining and analysis of massive dynamic information.
  • Supported by:
    the National Natural Science Foundation of China(41971340);the Project of the 2020 Fujian Province “the Belt and Road” Technology Innovation Platform(2020D002);the Municipal Science and Technology Program of Fuzhou Science and Technology Bureau(2019-G-40);the Natural Science Foundation of Fujian Science and Technology Department(2019I0019);the Fujian Provincial Candidates for the Hundred, Thousand and Ten Thousand Talents(GY-Z19113)

基于轨迹数据的出租车交接班时空分布识别方法

邹复民1,2,3, 罗思杰1,2,3(), 陈志辉1,2,3, 廖律超1,2,3   

  1. 1.福建省汽车电子与电驱动技术重点实验室(福建工程学院),福州 350118
    2.福建省北斗导航与智慧交通协同创新中心(福建工程学院),福州 350118
    3.数字福建交通大数据研究所(福建工程学院),福州 350118
  • 通讯作者: 罗思杰
  • 作者简介:邹复民(1976—),男,湖南隆回人,教授,博士,CCF 会员,主要研究方向:交通信息处理、无线宽带网络移动应用
    罗思杰(1995—),男,湖南娄底人,硕士研究生,CCF会员,主要研究方向:轨迹数据挖掘
    陈志辉(1995—),男,福建莆田人,硕士研究生,主要 研究方向:轨迹数据挖掘
    廖律超(1980—),男,福建长汀人,副教授,博士,主要研究方向:深度学习、海量动态信息的数据挖掘与分析。
  • 基金资助:
    国家自然科学基金资助项目(41971340);2020年度福建省“一带一路”科技创新平台项目(2020D002);福州市科技局市级科技计划项目(2019-G-40);福建省科技厅自然科学基金资助项目(2019I0019);福建省百千万人才工程省级人选(GY-Z19113)

Abstract:

Concerning the problem of inaccurate identification of taxi shift behaviors, an accurate identification method of taxi shift behaviors based on trajectory data mining was proposed. Firstly, after analyzing the characteristics of taxi parking state data, a method for detecting taxi parking points in non-operating state was proposed. Secondly, by clustering the parking points, the potential taxi shift locations were obtained. Finally, based on the judgment indices of taxi shift event and the kernel density estimation of the taxi shift time, the locations and times of the taxi shift were identified effectively. Taking the trajectory data of 4 416 taxis in Fuzhou as the experimental samples, a total of 5 639 taxi shift locations were identified. These taxi shift locations are in the main working areas of citizens, transportation hubs, business districts and scenic spots. And the identified taxi shift time is mainly from 4:00 to 6:00 in the morning and from 16:00 to 18:00 in the evening, which is consistent with the travel patterns of Fuzhou citizens. Experimental results show that, the proposed method can effectively detect the time-space distribution of taxi shift, and provide reasonable suggestions for the planning and management of urban traffic resources. The proposed method can also help the people to take a taxi more conveniently, improve the operating efficiency of taxis, and provide references for the site selection optimization of urban gas stations, charging stations and other car related facilities.

Key words: trajectory data, parking point, taxi shift, time-space distribution, urban traffic

摘要:

针对目前出租车交接班行为识别不够精准的问题,提出了一种基于轨迹数据挖掘的出租车交接班行为精准识别的方法。首先,分析出租车停留状态的数据特性后,提出了一种出租车非运营状态停留点检测方法;然后,对停留点进行聚类,从而得出了潜在的出租车交接班地点;最后,基于出租车交接班事件的判断指标与出租车交接班时间的核密度估计,有效地识别出出租车交接班地点和时间。以福州市4 416辆出租车的轨迹数据为实验样本,共识别出了5 639个交接班地点,这些交接班地点在市民主要工作区域、交通枢纽、商圈以及风景名胜。而识别出的交接班时间主要在凌晨4:00—6:00与傍晚16:00—18:00,与福州市民众出行规律相吻合。实验结果表明,该方法能有效地检测出出租车交接班的时空分布,能为城市的交通资源规划与管理提供合理建议,且使公众打车出行更加便捷,提高了出租车的运行效率,为城市加油站、充电站等汽车相关设施的选址优化提供了参考。

关键词: 轨迹数据, 停留点, 出租车交接班, 时空分布, 城市交通

CLC Number: