Intelligent Traffic Light Based On Genetic Algorithm

This paper proposes an intersection control algorithm which aims to determine an efficient vehicle-passing sequence that allows the emergency vehicle to cross an intersection as soon as possible while the travel times of other vehicles are minimally affected. When there are no emergency vehicles within the intersection area, the vehicles are controlled by the DICA that we proposed in our earlier work. When there are emergency vehicles entering the communication range, we prioritize emergency vehicles through optimal ordering of vehicles. Since the number of possible vehicle-passing sequences increases rapidly with the number of vehicles, finding an efficient sequence of vehicles in a short time is the main challenge of the study. A genetic algorithm is proposed to solve the optimization problem which finds the optimal vehicle sequence that gives the emergency vehicles the highest priority. The efficiency of the proposed approach for expedited crossing of emergency vehicles is validated through comparisons with DICA and a reactive traffic light algorithm through extensive simulations. The results show that the proposed genetic algorithm is able to decrease the travel times of emergency vehicles significantly in light and medium traffic volumes without causing any noticeable performance degradation of normal vehicles.

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Cooperative traffic light controlling based on machine learning and a genetic algorithm

2017 23rd Asia-Pacific Conference on Communications (APCC) ◽

10.23919/apcc.2017.8303995 ◽

2017 ◽

Cited By ~ 4

Author(s):

Huan Wang ◽

Jiang Liu ◽

Zhenni Pan ◽

Koshimizu Takashi ◽

Shigeru Shimamoto

Keyword(s):

Machine Learning ◽

Genetic Algorithm ◽

Traffic Light

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Traffic Light Optimization Based on Modified Webster Function

Journal of Advanced Transportation ◽

10.1155/2021/3328202 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Huizhen Zhang ◽

Hongtao Yuan ◽

Youqing Chen ◽

Wenlong Yu ◽

Cheng Wang ◽

...

Keyword(s):

Genetic Algorithm ◽

Road Traffic ◽

Optimal Solution ◽

Normal Operation ◽

Signal Timing ◽

Improved Genetic Algorithm ◽

Traffic Light ◽

Delay Function ◽

Traffic Lights ◽

Traffic Signal Timing

Intersection traffic lights are a basic means of ensuring the normal operation of road traffic. A good signal timing scheme is essential for improving traffic congestion. To obtain the signal timing scheme of the designated intersection, the method proposed in this article is based on a modified Webster function. The method uses the signal cycle and proportion of green light duration as independent variables to establish the corresponding intersection vehicle delay function. This function is converted from a multiobjective optimization to a single-objective optimization formulation; a modified genetic algorithm is then used to find the optimal solution to this function. The experimental results show that the timing scheme optimized by the improved genetic algorithm can reduce the intersection delay by nearly 15.64%. The proposed traffic signal timing based on the modified Webster function will be of value as an important reference for the optimization of traffic lights at urban intersections.

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Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v8i1.pp246-253 ◽

2018 ◽

Vol 8 (1) ◽

pp. 246 ◽

Cited By ~ 1

Author(s):

I Gede Pasek Suta Wijaya ◽

Keeichi Uchimura ◽

Gou Koutaki

Keyword(s):

Genetic Algorithm ◽

Traffic Congestion ◽

Road Network ◽

Hash Table ◽

Optimization Methods ◽

Light Signal ◽

Parameters Optimization ◽

Traffic Light ◽

Swarm Optimization ◽

Signal Parameters

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.

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