Spatio-temporal prediction of shopping behaviours using taxi trajectory data

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

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

Abstract

Taxi trajectory data (GPS data collected for 15,000 taxis at intervals of 30 seconds across three million journeys over eight days) is used to generate a spatio-temporal prediction of shopping behaviours in the emerging metropolitan city of Shenzhen, China. Two approaches are compared: time-series forecasting using ARIMA; and a gravity model approach, using the Huff model calibrated with Geographical Weighted Regression. Results demonstrate that ARIMA performs with significantly higher accuracy than the more traditional Huff model method. Further, it is demonstrate that while the accuracy of the Huff model is constrained by model assumptions, applying time-series methods to the underlying data directly (i.e., the ARIMA method) has no such constraints, and is limited only by the amount of data available. This suggests that, as richer data sets become available, spatio-temporal modelling of this kind will become more accurate.

Original languageEnglish
Title of host publication2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages112-116
Number of pages5
ISBN (Electronic)9781538647936
DOIs
Publication statusPublished - 25 May 2018
Event3rd IEEE International Conference on Big Data Analysis, ICBDA 2018 - Shanghai, China
Duration: 9 Mar 201812 Mar 2018

Publication series

Name2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018

Conference

Conference3rd IEEE International Conference on Big Data Analysis, ICBDA 2018
Country/TerritoryChina
CityShanghai
Period9/03/1812/03/18

Keywords

  • ARIMA
  • Geographically Weighted Regression
  • Huff model
  • Taxi trajectory data
  • shopping behaviour
  • time-series

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management

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