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

作     者：Xiaolong LI Gang PAN Zhaohui WU Guande QI Shijian LI Daqing ZHANG Wangsheng ZHANG Zonghui WANG 

作者机构：Department of Computer ScienceZhejiang UniversityHangzhou 310027China Institut TELECOM SudParis91011 Evry CedexFrance 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2012年第6卷第1期

页      面：111-121页

核心收录：

学科分类：080901[工学-物理电子学] 0809[工学-电子科学与技术（可授工学、理学学位）] 082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：Supported by the Fundamental Research funds for the Central Universities Qianjian Talent Program Zhejian Provincial Natural Scinesce Foundation of China 

主　　题：urban traffic GPS traces hotspots human mo-bility prediction auto-regressive integrated moving average(ARiMA) 

摘      要：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,