Keyword search (4,163 papers available)

"genetic algorithm" Keyword-tagged Publications:

Title Authors PubMed ID
1 On-site workshop investment problem: A novel mathematical approach and solution procedure Moradi N; Kayvanfar V; Baldacci R; 38125448
ENCS
2 A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand Alamatsaz K; Ahmadi A; Mirzapour Al-E-Hashem SMJ; 34415526
ENCS
3 Identification of point source emission in river pollution incidents based on Bayesian inference and genetic algorithm: Inverse modeling, sensitivity, and uncertainty analysis Zhu Y; Chen Z; Asif Z; 34380214
ENCS
4 Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City. Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S 32316356
ENCS

 

Title:Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City.
Authors:Azami P,  Jan T,  Iranmanesh S,  Ameri Sianaki O,  Hajiebrahimi S
Link:https://www.ncbi.nlm.nih.gov/pubmed/32316356?dopt=Abstract
DOI:10.3390/s20082276
Publication:Sensors (Basel, Switzerland)
Keywords:air pollution;  genetic algorithm;  restricted driving zone;  smart city;  traffic management; 
PMID:32316356 Category:Sensors (Basel) Date Added:2020-04-23
Dept Affiliation: ENCS
1 Computer Science, Laurentian University, Sudbury, ON P3E 2C6, Canada.
2 School of IT and Engineering, Melbourne Institute of Technology, Sydney, NSW 2000, Australia.
3 Business School, Victoria University, Melbourne, VIC 3000, Australia.
4 Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC H3G 1M8, Canada.

Description:

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

Sensors (Basel). 2020 Apr 16;20(8):

Authors: Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Abstract

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.

PMID: 32316356 [PubMed - in process]




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