基于时空相似性的大规模轨迹数据融合技术
CSTR:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金项目(61862066)


Spatial-Temporal Similarity-Based Data Fusion for Large-Scale Trajectories in Metro System
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    如何利用大数据技术来支撑地铁路网规划、运营调度、应急管理和公共服务是当前学术界和工业界的研究热点。该文使用集成电路(Integrated Circuit,IC)卡交易记录和手机 WiFi 信号记录两种不同的数据,提出一种基于时空相似性的设备关联方法来重现乘客的完整轨迹。通过计算 IC 卡和手机 两种不同设备历史轨迹的时空相似性,来关联同一乘客的 IC 卡和对应的手机。基于这种关联可以融合粗粒度的 IC 卡轨迹和细粒度的手机站内轨迹,进而重现乘客在地铁网络里的完整轨迹。实验通过对深 圳地铁连续两个月智能 IC 卡刷卡数据和 WiFi 信号数据进行分析,同时利用最长公共子序列方法,在Spark 集群计算了 728 万张 IC 卡轨迹数据和 4 010 万个移动设备轨迹数据的时空相似性。实验结果显示,该方法可以重现 20.3 万乘客的完整轨迹,足以用来支撑地铁清分和智慧警务等应用。

    Abstract:

    As the metro system becoming more and more important. How to utilize big data technology to support operational and management tasks is a hot topic in academia and industry communities. These tasks include metro network developing, service scheduling, risk response management, and public services. To address these issues, we propose a data fusion-based approach on two sources to rebuild a passenger’s full trip. The key idea is that we leverage the WiFi signal data and the smart card data together. We first calculate the spatiotemporal similarity between of smart card’s trajectories and mobile device’s trajectories. Then, we associate a passenger’s smart card and the corresponding mobile device via their similarity. Finally, we combine the instation trajectory hidden in the WiFi signal record and the coarse-grained trip presented by smart card record. We validate our approach on an extremely large dataset in the Chinese city Shenzhen. We calculate the similarity of trajectories generated by 7.28 million of smart cards and trajectories generated by 40.1 million of mobile devices in a Spark cluster. Experimental results show that this approach can rebuild 203 000 passenger trajectories. These results are enough to support many important applications in metro system.

    参考文献
    相似文献
    引证文献
引用本文

引文格式
熊文,周钱梅,杨昆,等.基于时空相似性的大规模轨迹数据融合技术 [J].集成技术,2019,8(5):26-33

Citing format
XIONG Wen, ZHOU Qianmei, YANG Kun, et al. Spatial-Temporal Similarity-Based Data Fusion for Large-Scale Trajectories in Metro System[J]. Journal of Integration Technology,2019,8(5):26-33

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2019-10-09
  • 出版日期:
文章二维码