Modeling urban mobility with machine learning analysis of public taxi transportation data

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.