| 基于时空轨迹数据的出行特征挖掘方法 |
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| 引用本文： | 张健钦,仇培元,杜明义.基于时空轨迹数据的出行特征挖掘方法[J].交通运输系统工程与信息,2014,14(6):72-78. |
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| 作者姓名： | 张健钦 仇培元 杜明义 |
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| 作者单位： | 1. 北京建筑大学测绘与城市空间信息学院，北京100044；2. 现代城市测绘国家测绘地理信息局重点实验室， 北京100044；3.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京100101 |
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| 基金项目： | 现代城市测绘国家测绘地理信息局重点实验室开放基金项目(20111216N)；北京市优秀人才培养资助个人项目 |
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| 摘 要： | 在车联网应用发展的背景下，许多城市的私家车和出租车上安装了配备GPS 设备的智能终端, 产生着大量的时空轨迹数据.为挖掘这些数据蕴含的驾驶员出行特征， 本文以北京市出租车时空轨迹数据为例，基于时空GIS 的视角提出并实现了驾驶员居住 地挖掘方法和作息规律性分析方法. 样本实验结果一方面展示了驾驶员居住地空间分 布，另一方面表明作息规律性总相似度在0.6–1之间的驾驶员数量较多，占到了总数的 73.75%.通过本文方法挖掘的信息可为出租车的管理提供辅助决策，方法同样适用私家 车时空轨迹数据的挖掘，对私家车出行规律的研究和掌握更有意义.
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| 关 键 词： | 城市交通 信息技术 出行特征 时空数据挖掘 出租车时空轨迹 |
| 收稿时间： | 2014-05-08 |
Mining Method of Travel Characteristics Based on Spatio-temporal Trajectory Data |
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| ZHANG Jian-qin,QIU Pei-yuan,DU Ming-yi.Mining Method of Travel Characteristics Based on Spatio-temporal Trajectory Data[J].Transportation Systems Engineering and Information,2014,14(6):72-78. |
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| Authors: | ZHANG Jian-qin QIU Pei-yuan DU Ming-yi |
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| Institution: | 1. Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2. Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation，Beijing 100044, China; 3. State Key Lab of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China |
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| Abstract: | With the development and application of mobile positioning technology, more and more private cars and taxis are equipped with GPS, and produce a great deal spatio-temporal trajectory data. In order to mine the characteristics of drivers based on these data. This paper studies spatio-temporal trajectory data of taxi in Beijing city from the perspective of time geography，the driver residence mining method and rule analyzing method of work and rest is put forward and is realized, and the experimental results are analyzed. Sample experimental results show the space distribution of the driver residence, and show that the number of driver routines of the total similarity between 0.6–1, accounted for 73.75% of the total. The information mined through the method can provide decision support for the management of the taxi, and the method application for private car has important significance. |
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| Keywords: | urban traffic information technology trip characteristics spatio-temporal data mining taxi spatio-temporal trajectories |
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