A data-driven approach for convergence prediction on road network

Authors:
Qiulei Guo;Jun Luo;Guiqing Li;Xin Wang;Nikolas Geroliminis
Affiliations:
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China,South China University of Technology, Guangzhou, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China,Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen, China;South China University of Technology, Guangzhou, China;Department of Geomatics Engineering, University of Calgary, Canada;Urban Transport Systems Laboratory (LUTS), Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Venue:
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
Year:
2013

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Abstract

With the rapid development of location sensing technology such as GPS, huge amount of location data through GPS are produced every day. The flood of taxi GPS data make it possible to predict the plentitude of traffic events on road network. In this paper, we propose a data-driven approach for traffic state convergence prediction on road network. We introduce a new method predicting the future location of taxis on road network. Furthermore we propose a statistical model to predict real time convergence on road network. We experimentally demonstrated that our approach achieves high prediction precision on the real world massive taxi GPS data.