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.

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 Signal Timing Optimization for Corridors with Multiple Highway-Rail Grade Crossings Using Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Yifeng Chen ◽  
Laurence R. Rilett
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Traffic Signal ◽  
Signal Timing ◽  
Optimization Approach ◽  
Underlying Assumption ◽  
Simulation Based ◽  
Critical Issues ◽  
Grade Crossings ◽  
Traffic Signal Timing

Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs) and their nearby intersections. Standard traffic signal optimization programs are not designed to work on roadway networks that contain multiple HRGCs, because their underlying assumption is that the roadway traffic is in a steady-state. During a train event, steady-state conditions do not occur. This is particularly true for corridors that experience high train traffic (e.g., over 2 trains per hour). In this situation, the non-steady-state conditions predominate. This paper develops a simulation-based methodology for optimizing traffic signal timing plan on corridors of this kind. The primary goal is to maximize safety, and the secondary goal is to minimize delay. A Genetic Algorithm (GA) was used as the optimization approach in the proposed methodology. A new transition preemption strategy for dual tracks (TPS_DT) and a train arrival prediction model were integrated in the proposed methodology. An urban road network with multiple HRGCs in Lincoln, NE, was used as the study network. The microsimulation model VISSIM was used for evaluation purposes and was calibrated to local traffic conditions. A sensitivity analysis with different train traffic scenarios was conducted. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with HRGCs.

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