Abstract
Most of the existing traffic optimization control methods are based on accurate mathematical models. As an uncertain and complex system, the urban traffic system faces difficulty in accurately calibrating the model parameters. Therefore, the existing methods become very difficult in the actual application process. Based on the massive data contained in the urban traffic system and the repetitive characteristics of traffic flow, this paper proposes a hierarchical traffic signal control method for urban road network based on iterative learning control. The simulation results show that the algorithm can achieve better control effect and can solve the problem of urban traffic congestion more effectively than traditional traffic control methods.
Highlights
Traffic signal control is an effective method to change urban road traffic conditions and improve road capacity
E current control of traffic signals at intersections is to divide the day into several time periods, and the cycle length is fixed in each time period
If the green signal ratio of each phase can be controlled to respond to changes in traffic flow in real time, the waiting time of vehicles will be minimized
Summary
Traffic signal control is an effective method to change urban road traffic conditions and improve road capacity. Due to the strong randomness of the traffic flow of the road section, the phased traffic congestion is unavoidable, and it cannot be based on specific conditions It will make real-time adjustments [3]. Iterative learning is applied between the main and auxiliary roads to achieve equilibrium control in local sections, so that the equilibrium control error converges, but the disadvantage is that the model ignores the impact of changing lanes on traffic conditions and cannot be applied to traffic intersections in daily life. Is article first uses the iterative learning control method to determine the best effective green light time for each phase and verifies the green signal ratio of a certain road section through simulation.
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