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

• Published in: Frontiers of Computer Science 2012-02, Vol.6 (1), p.111-121
• Main Authors: LI, Xiaolong, PAN, Gang, WU, Zhaohui, QI, Guande, LI, Shijian, ZHANG, Daqing, ZHANG, Wangsheng, WANG, Zonghui
• Format: Article
• Language: English
• Subjects: ARIMA模型; auto-regressive integrated moving average (ARIMA); Computer Science; GPS traces; hotspots; human mobility prediction; Passengers; Research Article; Taxicabs; Transportation systems; urban traffic; 出租车; 城市人; 应用; 时空变化; 模式预测; 流动模式; 运输系统
• ISSN: 1673-7350; 2095-2228; 1673-7466; 2095-2236
• DOI: 10.1007/s11704-011-1192-6

This paper investigates human mobility patterns in an urban taxi transportation system. This work focuses on predicting human mobility from discovering 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 real- world data set of 4 000 taxis＇ GPS traces over one year shows a prediction error of only 5.8%. We also explore the applica- tion of the pl~di~fioti approach to help drivers find their next passetlgerS, The sinatllation results using historical real-world data demonstrate that, with our guidance, drivers can reduce the time taken and distance travelled, to find their next pas- senger＋ by 37.1% and 6.4% respectively,