Genetic Algorithms Based Traffic Signal Optimization at a Congested Intersection

2012 ◽  
Vol 209-211 ◽  
pp. 814-817 ◽  
Author(s):  
Ping Wang ◽  
Qun Yang
Keyword(s):  
Genetic Algorithms ◽  
Objective Function ◽  
Traffic Signal ◽  
Signal Timing ◽  
Signal Optimization ◽  
Average Delay ◽  
Traffic Signal Timing ◽  
Optimal Signal ◽  
Isolated Intersection

The objective of this paper is to investigate genetic algorithms (GA) on traffic signal timing at a congested isolated intersection. The objective function for GA modeling was established on the strategy of minimizing average delay and GA was applied to search for the optimal signal timing. Then microsimulation is used to compare the optimized timings produced by the GA with those obtained for the same intersection using Synchro. Results indicated that applying GA results in lower values of average delay and average number of stops in congested condition than applying Synchro.

scholarly journals Multiple Objective Genetic Algorithms for Solving Traffic Signal Optimization Issue at a Complex Intersection: A Case Study in Taichung City, Taiwan

2020 ◽  
Vol 14 (1) ◽  
pp. 126-140
Author(s):  
. Do Van Manh ◽  
Liang- Tay Lin ◽  
Pei Liu ◽  
Dinh Tuan Hai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Traffic Signal ◽  
Multiple Objective ◽  
Signal Timing ◽  
Suggested Model ◽  
Hypothetical Model ◽  
Traffic Signal Timing ◽  
Single Objective ◽  
Isolated Intersection

Background: In optimal traffic signal timing, some researchers proposed a single objective genetic algorithm to optimize the timing plan at an isolated intersection. However, the genetic algorithm belongs to a natural selection procession. It means that a suggested model might have a local, optimal result instead of global optimization. A few researchers have tried to avoid local optimization values by making many assumptions for the suggested model, these estimations lacked comprehensive theoretical bases in the transportation field. Objective: The objective of this study is to contribute a comprehensive optimization solution, by applying multiple objective genetic algorithms, to minimize the effective green time and cycle length at a complex urban intersection. Methods: First, the fitness function was established by the minimum issues of average control delay and queue length at the complex isolated intersection. Secondly, constraint functions were identified based on a scientific basis to provide a comprehensive hypothetical model. After running the hypothetical model with single and multiple objective genetic algorithms and real traffic flow data, the results were compared between the use of multiple genetic algorithms and the use of a single-objective genetic algorithm, between an existing traffic signal timing plan and a suggested traffic signal timing plan. Then, the traffic simulation model for the complex intersection was generated to validate the effectiveness of the suggested method. Results: After comparison, the suggested model was found to be more efficient than the existing traffic signal timing at the complex intersection. Conclusion: This study demonstrated multiple objective genetic algorithms that overwhelmed the single objective genetic algorithm in optimal traffic signal timing. The multiple objective genetic algorithms could be effectively used to handle traffic optimization at a complex large-scale intersection. Furthermore, a comprehensive solution of applying multiple genetic algorithms to deal with traffic signal optimization has been generated in this research.

scholarly journals How To Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based On Swarm Intelligence Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2631
Author(s):  
Xiancheng Fu ◽  
Hengqiang Gao ◽  
Hongjuan Cai ◽  
Zhihao Wang ◽  
Weiming Chen
Keyword(s):  
Traffic Flow ◽  
Optimization Model ◽  
Traffic Congestion ◽  
Delay Time ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Average Delay ◽  
Traffic Capacity ◽  
Traffic Signal Timing

Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

scholarly journals Multi-Objective Optimization of Traffic Signal Timing for Oversaturated Intersection

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yan Li ◽  
Lijie Yu ◽  
Siran Tao ◽  
Kuanmin Chen
Keyword(s):  
Simulation Software ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Simulation Environment ◽  
Multi Objective Optimization ◽  
Good Efficiency ◽  
Multi Objective ◽  
Traffic Signal Timing

For the purpose of improving the efficiency of traffic signal control for isolate intersection under oversaturated conditions, a multi-objective optimization algorithm for traffic signal control is proposed. Throughput maximum and average queue ratio minimum are selected as the optimization objectives of the traffic signal control under oversaturated condition. A simulation environment using VISSIM SCAPI was utilized to evaluate the convergence and the optimization results under various settings and traffic conditions. It is written by C++/CRL to connect the simulation software VISSIM and the proposed algorithm. The simulation results indicated that the signal timing plan generated by the proposed algorithm has good efficiency in managing the traffic flow at oversaturated intersection than the commonly utilized signal timing optimization software Synchro. The update frequency applied in the simulation environment was 120 s, and it can meet the requirements of signal timing plan update in real filed. Thus, the proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain under both normal condition and over-saturated condition.

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