Cargando…

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

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 In...

Descripción completa

Detalles Bibliográficos
Autores principales: Asad, Syed Muhammad, Ahmad, Jawad, Hussain, Sajjad, Zoha, Ahmed, Abbasi, Qammer Hussain, Imran, Muhammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248743/
https://www.ncbi.nlm.nih.gov/pubmed/32380656
http://dx.doi.org/10.3390/s20092629
_version_ 1783538441304145920
author Asad, Syed Muhammad
Ahmad, Jawad
Hussain, Sajjad
Zoha, Ahmed
Abbasi, Qammer Hussain
Imran, Muhammad Ali
author_facet Asad, Syed Muhammad
Ahmad, Jawad
Hussain, Sajjad
Zoha, Ahmed
Abbasi, Qammer Hussain
Imran, Muhammad Ali
author_sort Asad, Syed Muhammad
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7248743
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-72487432020-08-13 Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning Asad, Syed Muhammad Ahmad, Jawad Hussain, Sajjad Zoha, Ahmed Abbasi, Qammer Hussain Imran, Muhammad Ali Sensors (Basel) Article 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. MDPI 2020-05-05 /pmc/articles/PMC7248743/ /pubmed/32380656 http://dx.doi.org/10.3390/s20092629 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Asad, Syed Muhammad
Ahmad, Jawad
Hussain, Sajjad
Zoha, Ahmed
Abbasi, Qammer Hussain
Imran, Muhammad Ali
Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title_full Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title_fullStr Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title_full_unstemmed Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title_short Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning
title_sort mobility prediction-based optimisation and encryption of passenger traffic-flows using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248743/
https://www.ncbi.nlm.nih.gov/pubmed/32380656
http://dx.doi.org/10.3390/s20092629
work_keys_str_mv AT asadsyedmuhammad mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning
AT ahmadjawad mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning
AT hussainsajjad mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning
AT zohaahmed mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning
AT abbasiqammerhussain mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning
AT imranmuhammadali mobilitypredictionbasedoptimisationandencryptionofpassengertrafficflowsusingmachinelearning