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Traffic Signal Optimization Control in Five-road Intersection Based on Artificial Fish Swarm Algorithm

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DOI: 10.23977/eleid.2019.11001 | Downloads: 8 | Views: 2196

Author(s)

Wang Peng 1

Affiliation(s)

1 School of Economics and Management, Dalian University, No.10, Xuefu Avenue, Economic & Technical Development Zone, Dalian, Liaoning,The People's Republic of China(PRC)

Corresponding Author

Wang Peng

ABSTRACT

The traffic signal control system plays a key role in the road network, and its control performance directly affects the traffic safety and delay time in the intersection. Traditional control in five-road intersection does not have the ability to adjust itself, which wastes green time. This paper adopts a method that use artificial fish swarm algorithm (AFSA) to optimize dynamic-fuzzy neural network (D-FNN) to achieve multi-phase and variable phase sequence intelligent control in five-road intersection. Taking the reciprocal of average vehicle delay as the food concentration of AFSA, and the weights and thresholds of the dynamic-fuzzy neural network which need to be modified are used as the individual state of artificial fish. A set of optimal dynamic-fuzzy neural network parameters are obtained through iterating and updating. After doing simulation analyses in the case of different rates of vehicles arrival, the result shows that this method is better than the traditional control in automatically adjusting the signal cycle, and it reduces the average delay of vehicles for about 11%.

KEYWORDS

Traffic signal control; five-road intersection; artificial fish swarm algorithm; dynamic-fuzzy neural network; average delay of vehicles

CITE THIS PAPER

Wang Peng, Traffic Signal Optimization Control in Five-road Intersection Based on Artificial Fish Swarm Algorithm. Electrical Insulation and Dielectrics(2019) Vol. 1: 1-5. DOI: http://dx.doi.org/10.23977/eleid.2019.11001.

REFERENCES

[1] Araghi S,. (2015) Intelligent Cuckoo Search Optimized Traffic Signal Controllers for Multi-intersection Network. Expert Systems with Applications, 9,4422-4431
[2] Li S S. (2014) BP Simulation Model and SensitivityAnalysis of Right-turn Vehicles’ Crossing Decisions at SignalizedIntersection Journal of Transportation Systems 2, 91-106.
[3] Pappis C P. (1977) A Fuzzy Logic Controller for a TrafficJunction IEEE Transactions on Systems Man and Cybernetics, 10, 707-717.

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