地球信息科学学报 ›› 2015, Vol. 17 ›› Issue (10): 1136-1142.doi: 10.3724/SP.J.1047.2015.01136

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轨迹数据挖掘城市应用研究综述

牟乃夏1,2(), 张恒才2, 陈洁2, 张灵先1, 戴洪磊1   

  1. 1. 山东科技大学 山东省基础地理信息与数字化技术重点实验室,青岛 266590
    2. 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
  • 收稿日期:2015-04-29 修回日期:2015-08-06 出版日期:2015-10-10 发布日期:2015-10-10
  • 作者简介:

    作者简介:牟乃夏(1973-),男,博士,副教授,山东平度人,研究方向为时空信息挖掘与推荐。E-mail:mounaixia@163.com

  • 基金资助:
    山东省“泰山学者”建设工程专项经费;资源与环境信息系统国家重点实验室开放基金;国家自然科学基金项目(41271408、41101149);国家“863”计划项目(2013AA120305)

A Review on the Application Research of Trajectory Data Mining in Urban Cities

MOU Naixia1,2,*(), ZHANG Hengcai2, CHEN Jie2, ZHANG Lingxian1, DAI Honglei1   

  1. 1. Shandong Provincial Key Laboratory of Geomatics and Digital Technology of Shandong Province, Shandong niversity of Science and Technology, Qingdao 266590, China
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • Received:2015-04-29 Revised:2015-08-06 Online:2015-10-10 Published:2015-10-10
  • Contact: MOU Naixia E-mail:mounaixia@163.com
  • About author:

    *The author: CHEN Nan, E-mail:fjcn99@163.com

摘要:

轨迹数据作为泛在地理信息环境中社会遥感数据的主要表现形式之一,为从个体的视角研究群体的空间移动规律,提供了新的数据支撑和研究思路。特别是在当前的大数据背景下,通过轨迹数据发掘人类的移动规律和活动模式,进而探求蕴含的深层次知识,是解决城市问题的重要途径,轨迹数据挖掘也由此成为地理信息科学及相关学科的研究热点。本文首先阐述了人类移动规律研究常用的轨迹数据集及在该数据集上开展的相关研究和典型应用;然后从城市空间结构功能单元的识别及城市韵律分析、人类活动模式的发现与空间移动行为预测、智能交通的时间估算与异常探测、城市计算的其他4个方面,综述了轨迹数据挖掘在城市中的应用;最后,指出了轨迹数据挖掘面临的挑战和进一步的发展方向。

关键词: 轨迹, 数据挖掘, 城市计算, 人类移动, 人类活动模式

Abstract:

The trajectory datasets record a series of position information at different times, so they become the new data sources to study the laws of human mobility. As a main form of social remote sensing data, trajectory datasets also bring a new individual viewpoint to study geographical phenomena. With the emergence of big data, trajectory data mining becomes a hot topic in geographical information science, urban computing and other correlative disciplines. In this paper, we gave a brief review on trajectory data mining and its applications in cities. First, we listed the data sets frequently adopted by human mobility research, gave the classification and their typical applications using FCD data, mobile phone data, smart cards data, check-in data, etc. Then, we summarized its application in solving cities’ problems from four aspects: (1) the identification of urban spatial structure and function unit; (2) the patterns recognition of human activity and the behavior prediction of human movement; (3) the traffic time estimation and the anomaly detection of intelligent transportation; (4) other applications in urban computing such as in urban air and noise pollution, disaster prevention and rescue, even in intelligent tourism and information recommendation. At the end, we pointed out the challenges and further research directions of trajectory data mining.

Key words: trajectory data, data mining, urban computing, human mobility, human activity patterns