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/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Taxi GPS data offers an opportunity to discover behavioural patterns in urban populations. However, raw taxi journey 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 novel 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, quantitatively reveal behavioural patterns and suggest possibilities for applications in urban design.

Original languageEnglish
Title of host publication2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages236-240
Number of pages5
ISBN (Electronic)9781728112824
DOIs
Publication statusPublished - 10 May 2019
Event4th IEEE International Conference on Big Data Analytics, ICBDA 2019 - Suzhou, China
Duration: 15 Mar 201918 Mar 2019

Publication series

Name2019 4th IEEE International Conference on Big Data Analytics, ICBDA 2019

Conference

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

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty

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