交通运输系统工程与信息 ›› 2018, Vol. 18 ›› Issue (3): 42-47.

• 智能交通系统与信息技术 • 上一篇    下一篇

基于轨迹大数据的城市交通感知和路网关键节点识别

冯慧芳*,柏凤山,徐有基   

  1. 西北师范大学 数学与统计学院,兰州 730070
  • 收稿日期:2017-11-06 修回日期:2018-02-16 出版日期:2018-06-25 发布日期:2018-06-25
  • 作者简介:冯慧芳(1971-),女,甘肃古浪人,教授,博士.
  • 基金资助:

    国家自然科学基金/National Natural Science Foundation of China (71561024, 71761031, 61363081).

Urban Traffic Perception and Critical Node Identification of Road Network Based on Trajectory Big Data

FENG Hui-fang, BAI Feng-shan, XU You-ji   

  1. College of Mathematics and Statistics, Northwest Normal University, Lanzhou 730070, China
  • Received:2017-11-06 Revised:2018-02-16 Online:2018-06-25 Published:2018-06-25

摘要:

结合城市道路网络的拓扑结构特征和交通流特性,建立基于有向加权复杂网络的城市交通网络关键节点识别模型.以兰州市连续7天的出租车GPS数据为基础,分析并可视化呈现兰州市在工作日和非工作日的城市交通流状态,并采用基于DWNodeRank的有向加权复杂网络关键节点识别方法对兰州市路网关键节点进行识别研究.本文研究方法和结果可为交通管理部门的规划、设计和管理提供科学指导.

关键词: 城市交通, 关键节点, 有向加权, 复杂网络, 轨迹大数据

Abstract:

A model of critical node identification in urban road network based on directed weighted complex network is proposed, which includes the road network topology and traffic flow characteristics. The traffic flow state of Lanzhou is analyzed and visualized in working days and non-working days according to seven consecutive day taxis GPS data. The critical node of urban road network in Lanzhou is studied using the DWNodeRank critical node identification method in directed weighted network. The research methodology and results provide a scientific guidance for planning, design and management to traffic department.

Key words: urban traffic, critical nodes, directed weighted, complex networks, trajectory big data

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