Traffic light optimization time distribution method based on improved fuzzy control

Abstract

The invention discloses a traffic light optimization time distribution method based on improved fuzzy control. Firstly, based on the dynamic performance of traffic flow of a single intersection, the average vehicle delay time at the intersection is calculated according to the number of vehicles waiting at all intersections and serves as an evaluation index of performance optimization, wherein the vehicles are detected by intersection sensors; secondly, a fuzzy controller is used for conducting optimization control over a time distribution scheme of traffic lights, and the particle swarm optimization is used for optimizing the fuzzy rule. By means of the traffic light optimization time distribution method, the subjectivity and blindness caused by the fuzzy rule which is manually set can be effectively avoided, and the optimal performance of the algorithm is improved.

Description

technical field [0001] The invention belongs to the field of intelligent traffic, and relates to an improved fuzzy control-based traffic light optimization timing method. Background technique [0002] The traffic signal control system is the main way of urban traffic control and an effective guarantee to relieve the traffic pressure at the intersection. It maximizes the traffic capacity of the intersection by controlling the display of traffic lights according to the distribution of waiting vehicles at the intersection. [0003] For the information of vehicles waiting at intersections collected by sensors, the traditional traffic light control strategy is based on the mathematical modeling method of traffic flow, which describes the current traffic dynamics by establishing a mathematical model, but these calculations are quite complicated and difficult to meet real-time requirements requirements. However, mathematical modeling requires strict assumptions, which reduces the...

AI Technical Summary

Problems solved by technology

[0003] For the information of vehicles waiting at intersections collected by sensors, the traditional traffic light control strategy is based on the mathematical modeling method of traffic flow, which describes the current traffic dynamics by establishing a mathematical model, but these calculations are quite complicated and difficult to meet real-time requirements requirements

However, mathematical modeling requires strict assumptions, which reduces the generality of the control algorithm.

Recently, some scholars have introduced artificial intelligence algorithms, such as fuzzy control, neural network, particle swarm algorithm, etc. Due to the limitations of these algorithms, a single artificial intelligence algorithm cannot solve the problem of optimal timing of traffic lights.

Images