Abstract
In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.
Highlights
In the past decade, world population has increased rapidly
Use past historical noted that the new parameters estimation method of LSTARIMA uses a time variant weight matrix data with Modified Q-learning to solve traffic congestion problems
Artificial intelligence (AI) refers to the wisdom expressed by the machines which have been made by people
Summary
World population has increased rapidly. Cities with high population density are inevitably facing problems of traffic congestion, inclusive of reducing commuting efficiency, worsening vehicle emissions, and increasing traffic accidents. Peng Qin et al [6] used image analysis to solve traffic problems It is generally not model possible obtain a top view of intersections in real time, so we purposes. Use past historical noted that the new parameters estimation method of LSTARIMA uses a time variant weight matrix data with Modified Q-learning to solve traffic congestion problems. Peng Qin et al [6] used image analysis to solve traffic taken from the Department of Transportation, Taipei City Government We look at reinforcement learning and the way traffic data are being collected show that the model can effectively recommend a better traffic light cycle configuration.
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