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基于手机信令数据的出行端点识别效果评估

杨飞 姜海航 姚振兴 刘好德

杨飞, 姜海航, 姚振兴, 刘好德. 基于手机信令数据的出行端点识别效果评估[J]. 西南交通大学学报, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086
引用本文: 杨飞, 姜海航, 姚振兴, 刘好德. 基于手机信令数据的出行端点识别效果评估[J]. 西南交通大学学报, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086
YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086
Citation: YANG Fei, JIANG Haihang, YAO Zhenxing, LIU Haode. Evaluation of Activity Location Recognition Using Cellular Signaling Data[J]. Journal of Southwest Jiaotong University, 2021, 56(5): 928-936. doi: 10.3969/j.issn.0258-2724.20200086

基于手机信令数据的出行端点识别效果评估

doi: 10.3969/j.issn.0258-2724.20200086
基金项目: 国家重点研发计划(2018YF1600900);国家自然科学基金(51678505);中央高校基本科研业务费专项资金(300102219301、300102210204);贵州省交通运输厅科研项目(2018-321-026);教育部人文社会科学基金青年基金项目(20XJCZH011);高等学校学科创新引智计划资助(B20035)
详细信息
    作者简介:

    杨飞(1980—),男,教授,博士生导师,博士,研究方向为交通大数据、智能交通技术与应用等,E-mail:yangfei_traffic@home.swjtu.edu.cn

  • 中图分类号: V491.1

Evaluation of Activity Location Recognition Using Cellular Signaling Data

  • 摘要: 为了研究利用手机信令数据识别个体出行端点的应用效果,开展实地采集手机信令数据的出行试验,且同步采集相应的GPS轨迹数据和出行日志作为算法评估的真实数据,提出出行端点识别的3阶段处理算法. 首先,提出等时距补点算法平衡各信令定位点的时间权重;然后,利用凝聚层次聚类算法将定位点聚类成不同的类簇;最后,针对已有研究中缺乏关注的类簇震荡现象,提出新的震荡修正算法对聚类结果做进一步优化. 案例结果表明:本文提出的方法对出行端点识别的精度、距离误差和时间误差上均有较好的效果,出行端点识别个数的精度在84%以上,端点位置识别距离平均误差在220 m以内,出行端点的离开和到达时间的平均误差分别为7.7 min 和5.3 min;在不同的出行目的的比较中,以工作为目的的端点识别效果最好,以娱乐购物为目的的端点识别效果相对较差.

     

  • 图 1  相邻手机信令数据的时间间隔分布

    Figure 1.  Time-interval distribution of adjacent cellular phone records

    图 2  某用户一天出行的GPS数据和信令数据在地图上轨迹分布

    Figure 2.  User’s all-day traces in GPS data and cellular signaling data on map

    图 3  定位点时间权重示意

    Figure 3.  Time weight Diagram of traces

    图 4  某用户一天定位点时空分布

    Figure 4.  Space-time distribution of user’s all day traces

    图 5  端点震荡修正算法示意

    Figure 5.  Schematic of location oscillation correction method

    图 6  不同距离阈值下出行端点识别效果

    Figure 6.  Activity location recognition results using different distance thresholds

    图 7  某用户出行端点识别结果样例

    Figure 7.  Case study result of user’s activity location recognition

    表  1  手机信令数据样例数据

    Table  1.   Example records of cellular signaling dataset

    全球标识手机号码设备标识位置区基站小区
    460****71130***4926869***66434051167939598
    460****72130***4927869***66534050168004374
    460****73130***4928869***66634051167936011
    通信事件开始时间/
    (时:分:秒)
    结束时间/
    (时:分:秒)
    经度/(°)纬度/(°)
    10512:54:1812:54:18106.7126.60
    10512:54:2012:54:20106.7426.59
    10412:54:2112:54:21106.7226.61
    下载: 导出CSV

    表  2  出行端点识别统计结果

    Table  2.   Recognition results of activity locations

    出行
    目的
    端点
    数/个
    识别比例/%平均时间
    误差/min
    平均距离误差/m
    正确率多识
    别率
    到达
    时间
    离开
    时间
    工作14186.21.35.47.5173.4
    居家18784.71.74.77.6195.7
    娱/购8579.45.26.58.4348.4
    总计41384.12.35.37.7219.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-11
  • 修回日期:  2020-06-08
  • 网络出版日期:  2021-03-23
  • 刊出日期:  2021-10-15

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