Cooperative Control for Urban Vehicle Traffic

Leţia, Tiberiu; Cuibus, Octavian

doi:10.3182/20120403-3-de-3010.00054

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Once the special vehicles pass through, the next phase is selected based on the normal priority. Moreover, Letia and Cuibus [17] developed a fuzzy control system to improve the control performance by getting the optimal control decisions and by extending or terminating the phase green time for fixed order intersection phases and fixed off sets between intersections in the network. Whereas, Ma et al (2012) [18] proposed a flexible multi-phase fuzzy control system in coordination with a genetic algorithm for a simple intersection to optimize the fuzzy membership functions based on the traffic condition at different times to be used in various traffic states.…”

Section: Fuzzy Logic Traffic Control Systemmentioning

confidence: 99%

A Survey of Intelligent Transportation Systems

Alothman

2018

Kafrelsheikh Journal of Information Sciences

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The problem of traffic delay and congestion is a severe problem in the world due to the population growth and difficulties in changing the infrastructure. This issue is being studied by researchers and international traffic centers because the traffic problem affected by the delay in services and products and the negative economic that traffic problem causes. It is difficult to improve the traffic system performance by using the traditional control methods. Many studies had been conducted using the fuzzy logic system and neural network to control the road intersections. In this article, the artificial intelligence traffic control principles and approaches which applied in the traffic signal control are reviewed. A comparison between the artificial intelligent systems and some points of view about future research in this area are proposed. The review shows that the traffic performance of the fuzzy controller has better performance than traditional traffic signal controls, especially during heavy and uneven traffic volume conditions.

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Section: Fuzzy Logic Traffic Control Systemmentioning

confidence: 99%