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.
This research has been partially funded by the International Science & Technology Cooperation Program of China (2010DFA92720) and NSF of China under poject 11271351.
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Guo, Q., Luo, J., Li, G., Wang, X., Geroliminis, N. (2013). A Data-Driven Approach for Convergence Prediction on Road Network. In: Liang, S.H.L., Wang, X., Claramunt, C. (eds) Web and Wireless Geographical Information Systems. W2GIS 2013. Lecture Notes in Computer Science, vol 7820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37087-8_4
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DOI: https://doi.org/10.1007/978-3-642-37087-8_4
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