Traffic Light Control for Multi Intersection Model in Istanbul/Altunizade

Tunc, Ilhan; Koroglu, Ali Osman; Edem, Ali Emre; Akmese, Serra Nur; Elmas, Ozge; Söylemez, Mehmet Turan

doi:10.1109/med51440.2021.9480194

Cited by 1 publication

(1 citation statement)

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“…Fuzzy systems have been used as a lighting control strategy [11], so they fit very well in traffic light control schemes, for example, for a large number of intersections [12], seeking to maximize the duration time of the green light [13], among others [14]- [18]. Some of these cases have been strengthened with the integration of fuzzy systems to neural networks [19], where nowadays one of the main neural architectures in pattern recognition are convolutional networks [20].…”

Section: Introductionmentioning

confidence: 99%

Ambulance detection for smart traffic light applications with fuzzy controller

Moreno

Baquero²,

Umaña³

2022

IJECE

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<p>In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.</p>

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