Modeling urban mobility with machine learning analysis of public taxi transportation data. Issue 1 (3rd April 2018)

• Record Type: Journal Article
• Authors: Song, Ha Yoon; You, Dabin
• Is Part Of: International journal of pervasive computing and communications. Volume 14:Issue 1(2018)
• Page Start: 73
• Page End: 87
• Publication Date: 2018-04-03
• Subjects: GMM -- Mobility model -- DBSCAN -- Taxi transportation data -- TrajectoryPattern -- Urban mobility model; Ubiquitous computing -- Periodicals; Mobile computing -- Periodicals; Computer network protocols -- Periodicals; Computer network architectures -- Periodicals; Application software -- Development -- Periodicals; 004.6
• DOI: 10.1108/IJPCC-D-18-00009
• Languages: English
• ISSNs: 1742-7371

Abstract

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.