TY - GEN
T1 - Spatio-temporal prediction of shopping behaviours using taxi trajectory data
AU - Cartlidge, John
AU - Gong, Shuhui
AU - Bai, Ruibin
AU - Yue, Yang
AU - Li, Qingquan
AU - Qiu, Guoping
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - 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.
AB - 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.
KW - ARIMA
KW - Geographically Weighted Regression
KW - Huff model
KW - Taxi trajectory data
KW - shopping behaviour
KW - time-series
UR - http://www.scopus.com/inward/record.url?scp=85048497442&partnerID=8YFLogxK
U2 - 10.1109/ICBDA.2018.8367660
DO - 10.1109/ICBDA.2018.8367660
M3 - Conference contribution
AN - SCOPUS:85048497442
T3 - 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018
SP - 112
EP - 116
BT - 2018 IEEE 3rd International Conference on Big Data Analysis, ICBDA 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Big Data Analysis, ICBDA 2018
Y2 - 9 March 2018 through 12 March 2018
ER -