Modeling urban mobility with machine learning analysis of public taxi transportation data
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 3 April 2018
Abstract
Purpose
The purpose of this paper is to understand urban mobility model.
Design/methodology/approach
The authors have used deep learning as tools of analysis and taxi transportation data as sources of mobility.
Findings
The authors have found urban mobility model of weekdays and weekends for a metropolitan city.
Research limitations/implications
There could be many sources of transportation data but the authors have used public taxi data solely.
Practical implications
With the urban mobility model proposed in this paper, other researchers and industries can improve their own service based on urban mobility model.
Social implications
The result would be a good model for urban traffic control or traffic modeling.
Originality/value
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.
Keywords
Acknowledgements
This work was supported by the National Research Foundation of Korea grant funded by the Korean Government (MEST) (NRF-2017R1D1A1B03029788).
Citation
Song, H.Y. 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-18-00009
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited