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A novel markov model-based traffic density estimation Technique for intelligent transportation system

An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent tr...

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Main Authors: Hira Beenish, Tariq Javid, Muhammad Fahad, Adnan Ahmed Siddiqui, Ghufran Ahmed, Hassan Jamil Syed
Format: Article
Language:English
English
Published: Molecular Diversity Preservation International (MDPI) 2023
Subjects:
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:https://eprints.ums.edu.my/id/eprint/36084/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/36084/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/36084/
https://doi.org/10.3390/s23020768
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spelling my.ums.eprints.360842023-07-20T01:39:26Z https://eprints.ums.edu.my/id/eprint/36084/ A novel markov model-based traffic density estimation Technique for intelligent transportation system Hira Beenish Tariq Javid Muhammad Fahad Adnan Ahmed Siddiqui Ghufran Ahmed Hassan Jamil Syed TK1-9971 Electrical engineering. Electronics. Nuclear engineering TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic. Molecular Diversity Preservation International (MDPI) 2023 Article NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/36084/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/36084/2/FULL%20TEXT.pdf Hira Beenish and Tariq Javid and Muhammad Fahad and Adnan Ahmed Siddiqui and Ghufran Ahmed and Hassan Jamil Syed (2023) A novel markov model-based traffic density estimation Technique for intelligent transportation system. Sensors, 23. pp. 1-24. https://doi.org/10.3390/s23020768
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Hira Beenish
Tariq Javid
Muhammad Fahad
Adnan Ahmed Siddiqui
Ghufran Ahmed
Hassan Jamil Syed
A novel markov model-based traffic density estimation Technique for intelligent transportation system
description An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.
format Article
author Hira Beenish
Tariq Javid
Muhammad Fahad
Adnan Ahmed Siddiqui
Ghufran Ahmed
Hassan Jamil Syed
author_facet Hira Beenish
Tariq Javid
Muhammad Fahad
Adnan Ahmed Siddiqui
Ghufran Ahmed
Hassan Jamil Syed
author_sort Hira Beenish
title A novel markov model-based traffic density estimation Technique for intelligent transportation system
title_short A novel markov model-based traffic density estimation Technique for intelligent transportation system
title_full A novel markov model-based traffic density estimation Technique for intelligent transportation system
title_fullStr A novel markov model-based traffic density estimation Technique for intelligent transportation system
title_full_unstemmed A novel markov model-based traffic density estimation Technique for intelligent transportation system
title_sort novel markov model-based traffic density estimation technique for intelligent transportation system
publisher Molecular Diversity Preservation International (MDPI)
publishDate 2023
url https://eprints.ums.edu.my/id/eprint/36084/1/ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/36084/2/FULL%20TEXT.pdf
https://eprints.ums.edu.my/id/eprint/36084/
https://doi.org/10.3390/s23020768
_version_ 1772812710819397632
score 13.211869

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