• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

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
  • AHAS R, SILM S, JÄRV O, et al. Using mobile positioning data to model locations meaningful to users of mobile phones[J]. Journal of Urban Technology, 2010, 17(1): 3-27. doi: 10.1080/10630731003597306
    ISAACMAN S, BECKER R, CÁCERES R, et al. Identifying important places in people’s lives from cellular network data[C]//International Conference on Pervasive Computing. Berlin, Heidelberg: Springer, 2011: 133-151.
    WANG P, HUNTER T, BAYEN A M, et al. Understanding road usage patterns in urban areas[J]. Scientific Reports, 2012, 2: 1-6.
    JÄRV O, AHAS R, WITLOX F. Understanding monthly variability in human activity spaces:a twelve-month study using mobile phone call detail records[J]. Transportation Research Part C:Emerging Technologies, 2014, 38: 122-135. doi: 10.1016/j.trc.2013.11.003
    XU Y, SHAW S L, ZHAO Z, et al. Another tale of two cities:understanding human activity space using actively tracked cellphone location data[J]. Annals of the American Association of Geographers, 2016, 106(2): 489-502.
    WANG Z, HE S Y, LEUNG Y. Applying mobile phone data to travel behaviour research:a literature review[J]. Travel Behaviour and Society, 2018, 11: 141-155. doi: 10.1016/j.tbs.2017.02.005
    CALABRESE F, COLONNA M, LOVISOLO P, et al. Real-time urban monitoring using cell phones:a case study in Rome[J]. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(1): 141-151. doi: 10.1109/TITS.2010.2074196
    CALABRESE F, DIAO M, DI LORENZO G, et al. Understanding individual mobility patterns from urban sensing data:a mobile phone trace example[J]. Transportation Research Part C:Emerging Technologies, 2013, 26: 301-313. doi: 10.1016/j.trc.2012.09.009
    JIANG S, FIORE G A, YANG Y, et al. A review of urban computing for mobile phone traces: Current methods, challenges and opportunities[C]//Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2013: 1-9.
    WANG F, CHEN C. On data processing required to derive mobility patterns from passively-generated mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2018, 87: 58-74. doi: 10.1016/j.trc.2017.12.003
    HARIHARAN R, TOYAMA K. Project lachesis: parsing and modeling location histories[C]//Interna- tional Conference on Geographic Information Science. Berlin, Heidelberg: Springer, 2004: 106-124.
    ALEXANDER L, JIANG S, MURGA M, et al. Origin-destination trips by purpose and time of day inferred from mobile phone data[J]. Transportation Research Part C:Emerging Technologies, 2015, 58: 240-250. doi: 10.1016/j.trc.2015.02.018
    WIDHALM P, YANG Y, ULM M, et al. Discovering urban activity patterns in cell phone data[J]. Transportation, 2015, 42(4): 597-623. doi: 10.1007/s11116-015-9598-x
    WANG M, CHEN C, MA J. Time-of-day dependence of location variability: application of passively-generated mobile phone dataset[C]//The 94th Annual Meeting of Transportation Research Board. Washington D. C.: [s.n.], 2015: 11-15.
    LEE J K, HOU J C. Modeling steady-state and transient behaviors of user mobility: formulation, analysis, and application[C]//Proceedings of the 7th ACM International Symposium on Mobile ad Hoc Networking and Computing (MobiHoc). New York: Association for Computing Machinery, 2006: 85-96.
    BAYIR M A, DEMIRBAS M, EAGLE N. Mobility profiler:a framework for discovering mobility profiles of cell phone users[J]. Pervasive and Mobile Computing, 2010, 6(4): 435-454. doi: 10.1016/j.pmcj.2010.01.003
    SHAD S A, CHEN E. Cell oscillation resolution in mobility profile building[J]. International Journal of Computer Science Issues (IJCSI), 2012, 9(3): 205-213.
    QI L, QIAO Y, ABDESSLEM F B, et al. Oscillation resolution for massive cell phone traffic data[C]// Proceedings of the 1st Workshop on Mobile Data. New York: Association for Computing Machinery, 2016: 25-30.
    IOVAN C, OLTEANU-RAIMOND A M, COURONNÉ T, et al. Moving and calling: mobile phone data quality measurements and spatiotemporal uncertainty in human mobility studies[J]. Lecture Notes in Geoinformation and Cartography, 2013: 247-265.
    DEMISSIE M G, DE ALMEIDA CORREIA G H, BENTO C. Intelligent road traffic status detection system through cellular networks handover information:an exploratory study[J]. Transportation Research Part C:Emerging Technologies, 2013, 32: 76-88. doi: 10.1016/j.trc.2013.03.010
    WU W, WANG Y, GOMES J B, et al. Oscillation resolution for mobile phone cellular tower data to enable mobility modelling[C]// Proceedings of the 2014 IEEE 15th International Conference on Mobile Data Management. Washington D. C.: IEEE Computer Society, 2014, 1: 317-324.
    CHEN C, MA J, SUSILO Y, et al. The promises of big data and small data for travel behavior (aka human mobility) analysis[J]. Transportation Research Part C:Emerging Technologies, 2016, 68: 285-299. doi: 10.1016/j.trc.2016.04.005
    CHEN C, BIAN L, MA J. From traces to trajectories:How well can we guess activity locations from mobile phone traces?[J]. Transportation Research Part C:Emerging Technologies, 2014, 46: 326-337. doi: 10.1016/j.trc.2014.07.001
  • 加载中
图(7) / 表(2)
计量
  • 文章访问数:  521
  • HTML全文浏览量:  287
  • PDF下载量:  44
  • 被引次数: 0
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

目录

    /

    返回文章
    返回