Mobility prediction-based optimisation and encryption of passenger traffic-flows using machine learning

Asad, S. M., Ahmad, J., Hussain, S. , Zoha, A. , Abbasi, Q. H. and Imran, M. A. (2020) Mobility prediction-based optimisation and encryption of passenger traffic-flows using machine learning. Sensors, 20(9), 2629. (doi: 10.3390/s20092629)

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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zoha, Dr Ahmed and Abbasi, Dr Qammer and Imran, Professor Muhammad and Hussain, Dr Sajjad and Asad, Syed
Creator Roles:
Asad, S. M.Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft
Hussain, S.Formal analysis, Project administration, Supervision, Writing – review and editing
Zoha, A.Conceptualization, Project administration, Writing – review and editing
Abbasi, Q. H.Funding acquisition, Project administration, Supervision, Validation, Visualization
Imran, M. A.Funding acquisition, Project administration, Supervision, Validation
Authors: Asad, S. M., Ahmad, J., Hussain, S., Zoha, A., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Sensors
ISSN (Online):1424-8220
Published Online:05 May 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Sensors 20(9): 2629
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300725Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy