Intelligent Urban Traffic Flow Control: A Case Study on Fuzzy Logic Application

Lamas, Wendell de Queiróz

doi:10.11648/j.ijtet.20170303.11

Cited by 1 publication

(1 citation statement)

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“…Existing traffic was estimated and a preassigned speed was proposed to synchronize the traffic signal [10]. Fuzzy logic was developed to solve a linear programming traffic model to efficiently control the intelligent traffic lights [11]. Deep reinforcement learning is used to analyze the traffic flow.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and Optimization of Traffic Congestion at Single Intersection Using Matlab and Arena Simulation

Roy¹,

Ghosh²,

Zoha³

et al. 2020

ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)

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Traffic jam is one of the most widespread problems commonly seen all over the world that causes loss of billions of dollars and useful hours per annum. Traffic lights are used at the intersections to manage the traffic flow, but one problem of those traffic lights is that the color changes at constant intervals irrespective of the traffic density or time of the day. As a result, the measure fails to keep the optimal traffic flow throughout the day at every convergence. This study describes a possible and effective method to systemize traffic lights by considering the traffic density for a specific time of the day. The range of idle time or waiting time within the queue is identified as a function of cycle time, effective green time and traffic arrival rate. After plotting the values within the graph, the trend is observed for idle time with reference to these variables. The lower and upper bound for these independent variables like cycle time, effective green time and traffic arrival rate are decided from observed data sets. As the idle time comes in the form of a range, an estimation of traffic congestion at a particular time is yielded from the method. This traffic flow is analyzed using Arena. The result of idle time using Arena is analogous with the previously analyzed model using MATLAB. Also, at the end of the study, a simulation video is generated that gives practical visual experience.

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Section: Literature Reviewmentioning

confidence: 99%