The purpose of this paper is to understand urban mobility model.
The authors have used deep learning as tools of analysis and taxi transportation data as sources of mobility.
The authors have found urban mobility model of weekdays and weekends for a metropolitan city.
There could be many sources of transportation data but the authors have used public taxi data solely.
With the urban mobility model proposed in this paper, other researchers and industries can improve their own service based on urban mobility model.
The result would be a good model for urban traffic control or traffic modeling.
This works is an improvement of the paper published in The 15th International Conference on Advances in Mobile Computing & Multimedia (MoMM2017) by recommendation of conference editor, Ismail Khalil, IJPCC editor-in-chief.
This work was supported by the National Research Foundation of Korea grant funded by the Korean Government (MEST) (NRF-2017R1D1A1B03029788).
Song, H. and You, D. (2018), "Modeling urban mobility with machine learning analysis of public taxi transportation data", International Journal of Pervasive Computing and Communications, Vol. 14 No. 1, pp. 73-87. https://doi.org/10.1108/IJPCC-D-18-00009Download as .RIS
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