Journal of Geo-information Science ›› 2015, Vol. 17 ›› Issue (10): 1136-1142.doi: 10.3724/SP.J.1047.2015.01136

• Orginal Article •     Next Articles

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

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