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Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City

Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and str...

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Autores principales: Jan, Tony, Azami, Pegah, Iranmanesh, Saeid, Ameri Sianaki, Omid, Hajiebrahimi, Shiva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219040/
https://www.ncbi.nlm.nih.gov/pubmed/32316356
http://dx.doi.org/10.3390/s20082276
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author Jan, Tony
Azami, Pegah
Iranmanesh, Saeid
Ameri Sianaki, Omid
Hajiebrahimi, Shiva
author_facet Jan, Tony
Azami, Pegah
Iranmanesh, Saeid
Ameri Sianaki, Omid
Hajiebrahimi, Shiva
author_sort Jan, Tony
collection PubMed
description Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods.
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spelling pubmed-72190402020-05-22 Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City Jan, Tony Azami, Pegah Iranmanesh, Saeid Ameri Sianaki, Omid Hajiebrahimi, Shiva Sensors (Basel) Article Traffic control is one of the most challenging issues in metropolitan cities with growing populations and increased travel demands. Poor traffic control can result in traffic congestion and air pollution that can lead to health issues such as respiratory problems, asthma, allergies, anxiety, and stress. The traffic congestion can also result in travel delays and potential obstruction of emergency services. One of the most well-known traffic control methods is to restrict and control the access of private vehicles in predetermined regions of the city. The aim is to control the traffic load in order to maximize the citizen satisfaction given limited resources. The selection of restricted traffic regions remains a challenge because a large restricted area can reduce traffic load but with reduced citizen satisfaction as their mobility will be limited. On the other hand, a small restricted area may improve citizen satisfaction but with a reduced impact on traffic congestion or air pollution. The optimization of the restricted zone is a dynamic multi-regression problem that may require an intelligent trade-off. This paper proposes Optimal Restricted Driving Zone (ORDZ) using the Genetic Algorithm to select appropriate restricted traffic zones that can optimally control the traffic congestion and air pollution that will result in improved citizen satisfaction. ORDZ uses an augmented genetic algorithm and determinant theory to randomly generate different foursquare zones. This fitness function considers a trade-off between traffic load and citizen satisfaction. Our simulation studies show that ORDZ outperforms the current well-known methods in terms of a combined metric that considers the least traffic load and the most enhanced citizen satisfaction with over 30.6% improvements to some of the comparable methods. MDPI 2020-04-16 /pmc/articles/PMC7219040/ /pubmed/32316356 http://dx.doi.org/10.3390/s20082276 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
Jan, Tony
Azami, Pegah
Iranmanesh, Saeid
Ameri Sianaki, Omid
Hajiebrahimi, Shiva
Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title_full Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title_fullStr Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title_full_unstemmed Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title_short Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City
title_sort determining the optimal restricted driving zone using genetic algorithm in a smart city
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7219040/
https://www.ncbi.nlm.nih.gov/pubmed/32316356
http://dx.doi.org/10.3390/s20082276
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