Modelling and Simulation of Traffic Light Control

Vatchova, Boriana; Boneva, Yordanka; Gegov, Alexander

doi:10.2478/cait-2023-0032

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Another issue resulting from traffic and vehicles is noise pollution High fuel consumption has adverse effects on both individual and national budgets Paragraphs following the first paragraph should begin with the paragraph indentation [1].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

İnağ,

Arıkan

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

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Section: Introductionmentioning

confidence: 99%