Optimization of vehicular flow through the use of genetic algorithms in a stretch of San Salvador

Authors

DOI:

https://doi.org/10.5377/ryr.v1i58.17401

Keywords:

genetic algorithms, simulation, vehicle flow

Abstract

Unplanned urbanization and population growth have increased mobility problems in the city of San Salvador, El Salvador. After COVID-19, the number of vehicles in the metropolitan area increased up to 150 % and with it the problems related to road congestion. In the search for innovative solutions to address the challenges posed by these problems, genetic algorithms emerge as a revolutionary tool to explore new solutions. In this research we carried out an application of genetic algorithms together with a simulation of a traffic pattern for a segment of the road network to find the optimal sequence of states in each traffic light in the study network with the objective of optimizing the vehicular flow and decreasing the waiting time to transit through the roads.

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Author Biographies

James Edward Humberstone Morales, Universidad Francisco Gavidia

Ingeniería en Ciencias de la Computación, por la Universidad Francisco Gavidia, El Salvador.
Maestría en Informática Aplicada a Redes, por la Universidad Francisco Gavidia, El Salvador.
Docente de la Facultad de Ingeniería y Sistemas e Investigador del Centro de Modelaje Matemático «Carlos Castillo-Chávez», en el Instituto de Ciencia, Tecnología e Innovación (ICTI), en la Universidad Francisco Gavidia, El Salvador.

Franklin Iván Argueta Bermúdez, Universidad Francisco Gavidia

Licenciado en Matemáticas, por la Universidad de El Salvador, El Salvador.
Investigador del Centro de Modelaje Matemático «Carlos Castillo-Chávez», en el Instituto de Ciencia, Tecnología e Innovación (ICTI), en la Universidad Francisco Gavidia, El Salvador.

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Published

2023-12-19

How to Cite

Humberstone Morales, J. E. ., & Argueta Bermúdez, F. I. . (2023). Optimization of vehicular flow through the use of genetic algorithms in a stretch of San Salvador. Reality and Reflection, 1(58), 131–145. https://doi.org/10.5377/ryr.v1i58.17401

Issue

Vol. 1 No. 58 (2023)

Section

Articles

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