Regional flow control in peak travel period based on fuzzy algorithm

Jiang, Chunhong; Ren, Guanghao

doi:10.1016/j.future.2021.08.016

Cited by 1 publication

(1 citation statement)

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“…This allows to identify and localize the ambulance within the scene. To control the time duration of each traffic light state, when an ambulance is detected, a fuzzy inference algorithm is used, which, as stated in the state of the art in the introduction, is one of the traffic control algorithms that stands out for its efficiency and versatility [27], [28]. Both parts of the algorithm are presented below.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%