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Article

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

1
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
2
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(9), 2629; https://doi.org/10.3390/s20092629
Received: 12 April 2020 / Revised: 30 April 2020 / Accepted: 3 May 2020 / Published: 5 May 2020
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. View Full-Text
Keywords: transportation; RFID sensors; artificial intelligence; mobility predictions; optimisation; encryption; smart city planning; passenger pathways; machine learning; 5G transportation; RFID sensors; artificial intelligence; mobility predictions; optimisation; encryption; smart city planning; passenger pathways; machine learning; 5G
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MDPI and ACS Style

Asad, S.M.; Ahmad, J.; Hussain, S.; Zoha, A.; Abbasi, Q.H.; Imran, M.A. Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning. Sensors 2020, 20, 2629. https://doi.org/10.3390/s20092629

AMA Style

Asad SM, Ahmad J, Hussain S, Zoha A, Abbasi QH, Imran MA. Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning. Sensors. 2020; 20(9):2629. https://doi.org/10.3390/s20092629

Chicago/Turabian Style

Asad, Syed M., Jawad Ahmad, Sajjad Hussain, Ahmed Zoha, Qammer H. Abbasi, and Muhammad A. Imran 2020. "Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning" Sensors 20, no. 9: 2629. https://doi.org/10.3390/s20092629

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