The Combination of Genetic Programming and Genetic Algorithm in Optimization of Signalized series Intersections
In urban road networks, traffic signals have been used to control vehicle movements in order to reduce congestion, improve safety, and enable specific straregies such as minimizing delays and improving environmental pollution. The optimization of signal control is at the heart of urban traffic. Using the optimization techniques in determining signal timing has been discused greatly for decades. Due to the fact that finding effective green times in signalised intersection cause complex differential functions, it is not possible to use derivative rules for analysis of this kind of problems. Due to complexity of the area control problems, new methods and approaches are needed to improve efficiency of signal control in a signalized road network such as meta-heuristic methods. In this paper the combination of genetic programming and genetic algorithm are applied to optimize signal timing. The proposed method uses existing traffic data to construct genetic network. The duty of genetic programming is to predict intersections delay for different timing and phasing of signals. All this process is done in MATLAB software. The comparisons show acceptable result of the proposed method. The major advantages of this method is the ability of genetic programming in producing a proper formula.
-
Traffic Signal Timing in Saturated Mode Using Reinforcement Learning
*, Mahmoud Ahmadinejad, Alireza Movahedi, Hamid Bigdel Rad
Journal of Transportation Research, Summer 2025 -
Evaluation of Road Diet Strategy as One of the Methods of Demand Management for Non-Motorized Vehicles and Pedestrians in the Cities Center
*, Shahriar Ourmazdi Khoramshahi, Hamid Bigdeli Rad
Road journal,