Activity Modelling Using Journey Pairing of Taxi Trajectory Data

Shuhui Gong, John Cartlidge, Ruibin Bai, Yang Yue, Qingquan Li, Guoping Qiu

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
201 Downloads (Pure)

Abstract

Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, the raw data does not provide a link between outbound and return journeys of individual travellers. Without this information, it is not possible to track individual behaviours. In this study, we propose a method for pairing taxi journeys and apply it to taxi trajectory data for the city of Shenzhen, China. Journeys related to three activities are considered: shopping, medical, and work. Results, validated using questionnaire data collected in Shenzhen, reveal behavioural patterns and suggest possibilities for applications in urban design.
Original languageEnglish
Title of host publication2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019)
Subtitle of host publication Proceedings of a meeting held 9-12 March 2018, Shanghai, China
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages236-240
Number of pages5
ISBN (Electronic)9781728112824
ISBN (Print)9781728112831
DOIs
Publication statusPublished - Jun 2019
Event4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
Duration: 15 Mar 201918 Mar 2019

Conference

Conference4th IEEE International Conference on Big Data Analytics, ICBDA 2019
CountryChina
CitySuzhou
Period15/03/1918/03/19

Keywords

  • Monte Carlo simulation
  • Power law distance decay function
  • travel behaviour analysis

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  • Cite this

    Gong, S., Cartlidge, J., Bai, R., Yue, Y., Li, Q., & Qiu, G. (2019). Activity Modelling Using Journey Pairing of Taxi Trajectory Data. In 2019 IEEE 4th International Conference on Big Data Analysis (ICBDA 2019): Proceedings of a meeting held 9-12 March 2018, Shanghai, China (pp. 236-240). [8712832] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICBDA.2019.8712832