Abstract
We use taxi trajectory data (GPS data collected for 15,000 taxis at intervals of 30 seconds across three million journeys over eight days) 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, we 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.
Original language | English |
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Title of host publication | 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA 2018) |
Subtitle of host publication | Proceedings of a meeting held 9-12 March 2018, Shanghai, China |
Publisher | IEEE Computer Society |
Pages | 112-116 |
Number of pages | 5 |
ISBN (Electronic) | 9781538647943 |
ISBN (Print) | 9781538647950 |
DOIs | |
Publication status | Published - Jun 2018 |
Event | IEEE International Conference on Big Data Analysis - East China University of Science and Technology, Shanghai, China Duration: 9 Mar 2018 → 12 Mar 2018 Conference number: 3 http://www.icbda.org |
Conference
Conference | IEEE International Conference on Big Data Analysis |
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Abbreviated title | ICBDA 2018 |
Country/Territory | China |
City | Shanghai |
Period | 9/03/18 → 12/03/18 |
Internet address |
Keywords
- Taxi trajectory data
- Time-series analysis
- ARIMA
- Huff model
- Geographically weighted regression
- Shopping behavior