A dynamic optimization method for traffic signals at urban intersections under the environment of Internet of Vehicles

Abstract

The invention provides a method for dynamically optimizing a traffic signal of an urban intersection under an environment of vehicle networking. According to the method, firstly, a ratio of an averagequeuing time to a traffic capacity at the intersection is determined as an optimization objective function, and the effects of a minimum average queuing time and a maximum traffic capacity are achieved at the same time, and secondly, the influence of an actual vehicle networking environment is considered, with a communication delay, a phase cycle and an effective green light time as constraints,a signal dynamic optimization model under the environment of vehicle networking is established. Through the method, the signal control effect of the urban intersections with medium and high saturationcan be effectively optimized, at the same time, the traffic environment of the urban intersection can be effectively improved, and the road capacity is improved.

Description

technical field [0001] The invention belongs to the application field of traffic signal optimization / Internet of Vehicles technology, and in particular relates to a novel urban intersection signal optimization method based on the Internet of Vehicles technology. Background technique [0002] With the rapid development of China's economy and the acceleration of urbanization, compared with the rapid growth of motor vehicle ownership, the problem of traffic congestion is becoming more and more serious. As an important part of the urban traffic network, urban road signal intersections are one of the "areas" with the most serious traffic congestion problems. At present, there are many ways to optimize traffic signals at urban road intersections. The most basic traffic signal control method is the single-point fixed signal timing method. Its theoretical basis is the famous Webster-Cobb theory. The principle and the formula of the shortest cycle length to make the intersection sat...

AI Technical Summary

Problems solved by technology

[0009] 1. Most of the current signal control optimization models are based on traditional intersection evaluation indicators, such as capacity, delay, number of stops, travel time, etc. The optimization objective is relatively single, and it fails to comprehensively consider both travelers and managers

[0010] 2. During the simulation verification, most of the data sources are based on traditional detectors or manual collection methods. On the one hand, it is impossible to obtain comprehensive and real-time traffic information, and on the other hand, the reliability of the data needs to be improved.

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