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

被引：293

作者：

Li, Xiaolong ^{[1
]}

Pan, Gang ^{[1
]}

Wu, Zhaohui ^{[1
]}

Qi, Guande ^{[1
]}

Li, Shijian ^{[1
]}

Zhang, Daqing ^{[2
]}

Zhang, Wangsheng ^{[1
]}

Wang, Zonghui ^{[1
]}

机构：

[1] Zhejiang Univ, Dept Comp Sci, Hangzhou 310027, Zhejiang, Peoples R China

[2] Inst TELECOM SudParis, F-91011 Evry, France

来源：

FRONTIERS OF COMPUTER SCIENCE | 2012年 / 6卷 / 01期

关键词：

urban traffic; GPS traces; hotspots; human mobility prediction; auto-regressive integrated moving average (ARIMA);

D O I：

10.1007/s11704-011-1192-6

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：111 / 121

页数：11

共 25 条

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