IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v501y2018icp24-41.html
   My bibliography  Save this article

Spatial–temporal travel pattern mining using massive taxi trajectory data

Author

Listed:
  • Zheng, Linjiang
  • Xia, Dong
  • Zhao, Xin
  • Tan, Longyou
  • Li, Hang
  • Chen, Li
  • Liu, Weining

Abstract

Deep understanding of residents’ travel patterns would provide helpful insights into the mechanisms of many socioeconomic phenomena. With the rapid development of location-aware computing technologies, researchers have easy access to large quantities of travel data. As an important data source, taxi trajectory data are featured by their high quality, good continuity and wide distribution, making it suitable for travel pattern mining. In this paper, we use taxi trajectory data to study spatial–temporal characterization of urban residents’ travel patterns from two aspects: attractive areas and hot paths. Firstly, a framework of trajectory preprocessing, including data cleaning and extracting the taxi passenger pick-up/drop-off points, is presented to reduce the noise and redundancy in raw trajectory data. Then, a grid density based clustering algorithm is proposed to discover travel attractive areas in different periods of a day. On this basis, we put forward a spatial–temporal trajectory clustering method to discover hot paths among travel attractive areas. Compared with previous algorithms, which only consider the spatial constraint between trajectories, temporal constraint is also considered in our method. Through the experiments, we discuss how to determine the optimal parameters of the two clustering algorithms and verify the effectiveness of the algorithms using real data. Furthermore, we analyze spatial–temporal characterization of Chongqing residents’ travel pattern.

Suggested Citation

  • Zheng, Linjiang & Xia, Dong & Zhao, Xin & Tan, Longyou & Li, Hang & Chen, Li & Liu, Weining, 2018. "Spatial–temporal travel pattern mining using massive taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 24-41.
    • Handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:24-41
    DOI: 10.1016/j.physa.2018.02.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118301419
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.02.064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    2. Cai, Hua & Zhan, Xiaowei & Zhu, Ji & Jia, Xiaoping & Chiu, Anthony S.F. & Xu, Ming, 2016. "Understanding taxi travel patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 590-597.
    3. Jiang, Shixiong & Guan, Wei & Zhang, Wenyi & Chen, Xu & Yang, Liu, 2017. "Human mobility in space from three modes of public transportation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 227-238.
    4. Wang, Wenjun & Pan, Lin & Yuan, Ning & Zhang, Sen & Liu, Dong, 2015. "A comparative analysis of intra-city human mobility by taxi," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 134-147.
    5. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dokuz, Yesim & Dokuz, Ahmet Sakir, 2023. "Time-persistent regions discovery of taxi trajectory big datasets based on regional spatio-temporal velocity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    2. Chen, Li & Zheng, Linjiang & Xia, Li & Liu, Weining & Sun, Dihua, 2021. "Detecting and analyzing unlicensed taxis: A case study of Chongqing City," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    3. Tang, Jinjun & Bi, Wei & Liu, Fang & Zhang, Wenhui, 2021. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    4. Fu, Xin & Xu, Chengyao & Liu, Yuteng & Chen, Chi-Hua & Hwang, F.J. & Wang, Jianwei, 2022. "Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    5. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Dokuz, Ahmet Sakir, 2022. "Weighted spatio-temporal taxi trajectory big data mining for regional traffic estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    7. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xia, Dawen & Jiang, Shunying & Yang, Nan & Hu, Yang & Li, Yantao & Li, Huaqing & Wang, Lin, 2021. "Discovering spatiotemporal characteristics of passenger travel with mobile trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    2. He, Zhengbing, 2020. "Spatial-temporal fractal of urban agglomeration travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    3. Jiang, Shixiong & Guan, Wei & Zhang, Wenyi & Chen, Xu & Yang, Liu, 2017. "Human mobility in space from three modes of public transportation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 227-238.
    4. Tang, Jinjun & Zhang, Shen & Zhang, Wenhui & Liu, Fang & Zhang, Weibin & Wang, Yinhai, 2016. "Statistical properties of urban mobility from location-based travel networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 694-707.
    5. Zhang, Shen & Tang, Jinjun & Wang, Haixiao & Wang, Yinhai & An, Shi, 2017. "Revealing intra-urban travel patterns and service ranges from taxi trajectories," Journal of Transport Geography, Elsevier, vol. 61(C), pages 72-86.
    6. Li, Ze-Tao & Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2023. "Exploring the topological characteristics of urban trip networks based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    7. Ting Wang & Yong Zhang & Meiye Li & Lei Liu, 2019. "How Do Passengers with Different Using Frequencies Choose between Traditional Taxi Service and Online Car-Hailing Service? A Case Study of Nanjing, China," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    8. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    9. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    10. Zhao, Pengxiang & Kwan, Mei-Po & Qin, Kun, 2017. "Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel," Journal of Transport Geography, Elsevier, vol. 62(C), pages 122-135.
    11. Tong Zhou & Xintao Liu & Zhen Qian & Haoxuan Chen & Fei Tao, 2019. "Dynamic Update and Monitoring of AOI Entrance via Spatiotemporal Clustering of Drop-Off Points," Sustainability, MDPI, vol. 11(23), pages 1-20, December.
    12. Pattama Krataithong & Chutiporn Anutariya & Marut Buranarach, 2022. "A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    13. Xia, Dong & Zheng, Linjiang & Tang, Yi & Cai, Xiaolin & Chen, Li & Sun, Dihua, 2022. "Dynamic traffic prediction for urban road network with the interpretable model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    14. Xu Mengqiao & Zhang Ling & Li Wen & Xia Haoxiang, 2017. "Mobility Pattern of Taxi Passengers at Intra-Urban Scale: Empirical Study of Three Cities," Journal of Systems Science and Information, De Gruyter, vol. 5(6), pages 537-555, December.
    15. Mepparambath, Rakhi Manohar & Soh, Yong Sheng & Jayaraman, Vasundhara & Tan, Hong En & Ramli, Muhamad Azfar, 2023. "A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    16. Yanyan Chen & Zheng Zhang & Tianwen Liang, 2019. "Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns," Sustainability, MDPI, vol. 11(19), pages 1-15, October.
    17. Fangye Du & Jiaoe Wang & Yu Liu & Zihao Zhou & Haitao Jin, 2022. "Equity in Health-Seeking Behavior of Groups Using Different Transportations," IJERPH, MDPI, vol. 19(5), pages 1-16, February.
    18. Zhang, Xiaohu & Xu, Yang & Tu, Wei & Ratti, Carlo, 2018. "Do different datasets tell the same story about urban mobility — A comparative study of public transit and taxi usage," Journal of Transport Geography, Elsevier, vol. 70(C), pages 78-90.
    19. Nie, Wei-Peng & Cai, Shi-Min & Zhao, Zhi-Dan & Zhou, Tao, 2022. "Revealing mobility pattern of taxi movements with its travel trajectory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    20. Wang, Minjie & Yang, Su & Sun, Yi & Gao, Jun, 2017. "Discovering urban mobility patterns with PageRank based traffic modeling and prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 485(C), pages 23-34.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:501:y:2018:i:c:p:24-41. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.