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Prediction of urban human mobility using large-scale taxi traces and its applications |
Xiaolong LI1, Gang PAN1, Zhaohui WU1, Guande QI1, Shijian LI1, Daqing ZHANG2, Wangsheng ZHANG1, Zonghui WANG1( ) |
| 1. Department of Computer Science, Zhejiang University, Hangzhou 310027, China; 2. Institut TELECOM SudParis, 91011 Evry Cedex, France |
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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.
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| Keywords
urban traffic
GPS traces
hotspots
human mobility prediction
auto-regressive integrated moving average (ARIMA)
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Corresponding Author(s):
WANG Zonghui,Email:zjuzhwang@zju.edu.cn
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Issue Date: 01 February 2012
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