计算机应用 ›› 2013, Vol. 33 ›› Issue (02): 583-586.DOI: 10.3724/SP.J.1087.2013.00583

• 典型应用 • 上一篇    下一篇

基于手机定位信息的地铁乘客出行路径辨识方法

赖见辉1,陈艳艳1,钟园2,吴德仓3,袁奕芳1   

  1. 1. 北京工业大学 建筑工程学院,北京 100124
    2. 北京市交通信息中心,北京 100073
    3. 北京市轨道交通指挥中心,北京 100101
  • 收稿日期:2012-08-14 修回日期:2012-11-03 出版日期:2013-02-01 发布日期:2013-02-25
  • 通讯作者: 赖见辉
  • 作者简介:赖见辉(1986-),男,江西赣州人,博士研究生,主要研究方向:交通规划、智能交通;
    陈艳艳(1970-),女,河南郑州人,教授,主要研究方向:交通规划、智能交通;
    钟园(1983-),女,北京人,工程师,主要研究方向:智能交通;
    吴德仓(1984-),男,河南郑州人,工程师,主要研究方向:轨道交通管理;
    袁奕芳(1991-),女,江西赣州人,主要研究方向:智能交通。
  • 基金资助:
    国家973计划项目;北京市科技计划项目

Travel route identification method of subway passengers based on mobile phone location data

LAI Jianhui1,CHEN Yanyan1,ZHONG Yuan2,WU Decang3,YUAN Yifang1   

  1. 1. College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China
    2. Beijing Transportation Information Center, Beijing 100073, China
    3. Beijing Metro Network Control Center, Beijing 100101, China
  • Received:2012-08-14 Revised:2012-11-03 Online:2013-02-01 Published:2013-02-25
  • Contact: LAI Jianhui

摘要: 针对复杂轨道网络环境的下出行路径选择问题,传统方法采用理论推算往往与实际偏差较大。基于手机定位信息的出行路径辨识方法,利用手机用户在无线通信网络中产生的信令事件数据,根据其在地铁中的正常位置更新规则得到出行路径,针对信令数据存在缺失的情况,以用户的其他信令事件数据及K短路校核法,对路径的有效性进行检测,进而得到实际出行路径。实测结果表明,用该方法得到的用户出行路径与真实路径偏差较小。。

关键词: CellID定位, 地铁, 出行路径

Abstract: Traditional theory-deduced route choice always has large deviation from the actual one in complex rail transit network. The signaling data were collected from the passengers' mobile phone in rail wireless communication network. According to these data, a new travel route identification algorithm was proposed based on normal location update. Meanwhile, concerning the data missing, a repair algorithm was also put forward by using other signaling data of users to deduce their actual travel route by the K shortest paths. And the route validity would be checked to get the actual travel route. Finally, typical application in Beijing rail transit network was selected to validate this algorithm. The application results show that the algorithm has a good performance in illustrating the actual travelers' travel behaviors.

Key words: CellID location, subway, travel route

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