Modern Control Systems for industrial processes: State of the art for cases of traffic congestion, through resolution of genetic and control algorithms.
DOI:
https://doi.org/10.59282/reincisol.V3(5)1296-1321Keywords:
Control, traffic, neural networks, controller.Abstract
The steady increase of vehicles on the roads has recently made road congestion a crucial challenge. To cope with current traffic conditions and meet the growing demand for transportation, effective solutions are needed in urban transportation systems. However, introducing changes in urban infrastructures often involves significant constraints in time and feasibility. Therefore, optimizing traffic signal timing (TST) emerges as one of the fastest and most cost-effective methods to reduce congestion at intersections and improve traffic flow in urban networks. Researchers have been exploring various approaches, along with technological advances, to improve TST. This article aims to review recent literature from January 2015 to January 2020, focusing on computational intelligence (CI)-based simulation approaches and CI-based strategies for optimizing TST and traffic signal control (TSC) systems. Through this analysis, we seek to provide insight into existing research, identify gaps, and suggest possible directions for future research in this field.
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Copyright (c) 2024 Jonathan Geovanny Velásquez Mora , Carlos Patricio Arroyo Vilela
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