Traffic Signal Control Based on Adaptive Neuro-Fuzzy Inference

Wannige, C.T.; Sonnadara, D. U. J.

doi:10.1109/iciafs.2008.4783976

Cited by 7 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ANFIS traffic control model can also be tested using the graphical user interface in the MATLAB software package, which was done in (Abiodun et al, 2014). The model proposed in (Wannige & Sonnadara, 2008) has two inputs representing the number of vehicles entering the intersection in both directions. The model training was performed for the given input values and for calculating an optimal time of the green light interval based on them.…”

Section: Traffic Control At Intersections With Light Signalingmentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

View full text Add to dashboard Cite

The paper's focus is on researching the application of the ANFIS (Adaptive Neuro Fuzzy Inference System) model in traffic and transport through a review of relevant papers. The ANFIS, as an element of artificial intelligence, is widely used in intelligent transport systems. All collected papers are divided into 7 sub-areas, namely: 1) vehicle routing, 2) traffic control at intersections with light signaling, 3) vehicle steering and control, 4) safety, 5) modeling of fuel consumption, engine performance and exhaust emissions, 6) traffic congestion prediction, and 7) other applications. For each sub-area, the analysis of the proposed models is performed with a tabular overview of respective input and output variables, while in the third section the discussion of the results is given. It is found that the steering and control of vehicles represent a sub-area with the highest percentage in the total number of examined papers, while the security applications take second place.

show abstract

Section: Traffic Control At Intersections With Light Signalingmentioning

confidence: 99%

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Stojčić

2019

Oper. Res. Eng. Sci. Theor. Appl.

View full text Add to dashboard Cite

show abstract

“…ANN system consists of inputs, which are multiplied by weights, and then manipulated by a mathematical function. The ANN system determines the activation of the neuron beside computing the output of the artificial neuron which is sometimes independent of a certain threshold, as shown in Fig.2 [2].…”

Section: B Artificial Neural Network Control Systemmentioning

confidence: 99%

Intelligent Traffic Control System for Over-Saturated Signalized Intersections in Kuwait..

Alothman

2018

Kafrelsheikh Journal of Information Sciences

View full text Add to dashboard Cite

This paper proposes a new intelligent traffic control (ITC) system which is more efficient than the traffic control system currently used in the state of Kuwait. The proposed ITC system is designed as a dynamic system by using a fuzzy expert system; the fuzzy rules are applied in the visual basic and computer-based program (Excel) to run the validation process. The developed control system applied on five intersections in the grid network at four periods. The results show that the number of vehicles passing the intersection phases is increased in most phases by an average of 12.9% at the first period, 23.3% at the second period, 10.4% at the third period and by 21.2% at the fourth period. For the same periods, the phases green time is increased by an average of 9.1%, 5.8%, 9.9% and 6.3%. And the number of intersection cycles remains constant at the most time which means that the developed control system distributes the phases green time dynamically based on the traffic situation.

show abstract

“…In 2008, Wannige, and Sonnadara [27] developed an adaptive neuro-fuzzy system to reduce the vehicle waiting time and the vehicle delay by specifying the traffic green time using several inputs in the calculation such as a gap between two vehicles, delay at intersections, vehicle density, flow rate and queue length.…”

Section: Artificial Neural Network Control Systemmentioning

confidence: 99%

A Survey of Intelligent Transportation Systems

Alothman

2018

Kafrelsheikh Journal of Information Sciences

View full text Add to dashboard Cite

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.

show abstract

Traffic Signal Control Based on Adaptive Neuro-Fuzzy Inference

Cited by 7 publications

References 5 publications

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

Intelligent Traffic Control System for Over-Saturated Signalized Intersections in Kuwait..

A Survey of Intelligent Transportation Systems

Contact Info

Product

Resources

About