Travelers-Tracing and Mobility Profiling Using Machine Learning in Railway Systems

Asad, S., Dashtipour, K., Hussain, S. , Abbasi, Q. H. and Imran, M. A. (2020) Travelers-Tracing and Mobility Profiling Using Machine Learning in Railway Systems. In: 5th International Conference on the UK-China Emerging Technologies (UCET 2020), Glasgow, UK, 20-21 Aug 2020, ISBN 9781728194882 (doi: 10.1109/UCET51115.2020.9205456)

Abstract

With the advent of Coronavirus Disease 2019 (COVID-19) throughout the world, safe transportation becomes critical while maintaining reasonable social distancing that requires a strategy in the mobility of daily travelers. Crowded train carriages, stations, and platforms are highly susceptible to spreading the disease, especially when infected travelers intermix with healthy travelers. Travelers-profiling is one of the essential interventions that railway network professionals rely on managing the disease outbreak while providing safe commute to staff and the public. In this plethora, a Machine Learning (ML) driven intelligent approach is proposed to manage daily train travelers that are in the age-group 16-59 years and over 60 years (vulnerable age-group) with the recommendations of certain times and routes of traveling, designated train carriages, stations, platforms, and special services using the London Underground and Overground (LUO) Network. LUO dataset has been compared with various ML algorithms to classify different agegroup travelers where Support Vector Machine (SVM) mobility prediction classification achieves up to 86.43% and 81.96% in age-group 16-59 years and over 60 years.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer and Imran, Professor Muhammad and Hussain, Dr Sajjad and Asad, Syed and Dashtipour, Dr Kia
• Authors: Asad, S., Dashtipour, K., Hussain, S., Abbasi, Q. H., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISBN: 9781728194882
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 International Conference on UK-China Emerging Technologies (UCET)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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