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An improved evolutionary algorithm to optimize traffic flow at signalized intersections

Author(s)
SIDINA BOUDAAKAT SSDIA Laboratory, ENSET Mohammedia, Hassan II University, Morocco
MOHAMED AMINE BASMASSI ISO Laboratory, Faculty of Sciences, Ibn Tofail University, Morocco
AHMED REBBANI SSDIA Laboratory, ENSET Mohammedia, Hassan II University, Morocco
OMAR BOUATTANE SSDIA Laboratory, ENSET Mohammedia, Hassan II University, Morocco
JIHANE ALAMI CHENTOUFI ISO Laboratory, Faculty of Sciences, Ibn Tofail University, Morocco
LAMIA BENAMEUR LIROSA Laboratory, Faculty of Sciences, Abdelmalik Essaadi University, Morocco
Abstract
Traffic congestion remains a hard phenomenon problem to solve. Due to the difficulty and high cost of solutions, many studies are trying to find the most inexpensive solutions. However, with the exponential growth of technology, new opportunities and challenges are opening up to reduce congestion problems. This paper focuses on the minimization of the waiting vehicles at an isolated four arms intersection. The proposed model is divided into two main stages. First, modeling the intersection using a fuzzy system by generating a generalized fuzzy graph based on the intersection situation. Second, an improved greedy genetic algorithm solution is applied to determine the traffic cycle length to reach the maximum performance at the intersection. The results of the experiments used in the simulation of the intersection modeling generalized fuzzy graph and the novel greedy genetic algorithm (IMGFG-NGGA) performed very well compared with traditional fixed-time traffic signal under different traffic demands.
Publication Details
Page(s) 3259-3269
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 10, Year: 2022

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Keywords
Genetic Algorithm, Traffic Signal Control, Modeling Intersection, Fuzzy Graph Coloring, Signalized Intersection, Traffic management
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