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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7219040 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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