Prediction of urban human mobility using large-scale taxi traces and its applications

Authors:
Xiaolong Li;Gang Pan;Zhaohui Wu;Guande Qi;Shijian Li;Daqing Zhang;Wangsheng Zhang;Zonghui Wang
Affiliations:
Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Institut TELECOM SudParis, Evry Cedex, France 91011;Department of Computer Science, Zhejiang University, Hangzhou, China 310027;Department of Computer Science, Zhejiang University, Hangzhou, China 310027
Venue:
Frontiers of Computer Science in China
Year:
2012

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Abstract

This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting humanmobility fromdiscovering patterns of in the number of passenger pick-ups quantity (PUQ) from urban hotspots. This paper proposes an improved ARIMA based prediction method to forecast the spatial-temporal variation of passengers in a hotspot. Evaluation with a large-scale realworld data set of 4 000 taxis' GPS traces over one year shows a prediction error of only 5.8%. We also explore the application of the prediction approach to help drivers find their next passengers. The simulation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next passenger, by 37.1% and 6.4%, respectively.