Intersection self-adaptation control method based on car networking environment

Abstract

The invention discloses an intersection self-adaptation control method based on a car networking environment and belongs to the technical field of car networking. According to the method, the advantages of the car networking are made full use of, the real-time state information of cars is provided for a road side unit, firstly, abstract modeling is performed on a whole traffic network, and the dynamic priority of each traffic flow is calculated; then, an optimal phase position and phase sequence model is built according to specific characteristics of an intersection so that the optimal phase position sequence of the current intersection can be obtained, high-priority traffic flows can pass through the intersection preferentially, and meanwhile, it is guaranteed that the flow of the cars allowed to pass each time is maximum. According to the method, lots of buried sensors are not needed, and not only is city control system construction cost reduced, but also maintenance upgrading of a traffic control system is facilitated. The traffic flows and the states of the cars are accurately mastered in real time, and the situation that in the prior art, a traffic control system lags behind seriously, and obtained information is little and even wrong is greatly changed.

Description

technical field [0001] The invention relates to an adaptive control method for an intersection based on the Internet of Vehicles environment, which belongs to the technical field of the Internet of Vehicles. Background technique [0002] With the rapid development of the national economy, the number of urban motor vehicles continues to increase. Energy consumption, urban congestion, automobile traffic safety and other issues have become bottlenecks restricting the sustainable development of my country's transportation industry. The intelligence, informatization and integration of urban traffic have become the most critical technology to solve this problem. Intelligent transportation technology can reduce traffic congestion by about 60%, increase short-distance transportation efficiency by nearly 70%, and increase the traffic capacity of the existing road network by 2-3 times. However, there are many problems in the traditional traffic control methods, the main problems are...

AI Technical Summary

Problems solved by technology

The traditional traffic control method relies on the static data collected by a single sensor, which cannot regulate the traffic flow in real time, and is completely unsuitable for the working environment of the Internet of Vehicles

3) The traffic control method works better when the traffic is not congested, but the control effect is poor when the traffic is about to be congested or congested.

It is difficult for the signal system to deeply analyze the causes of traffic congestion, and it is even more difficult to decide which section of traffic flow to let go in the case of congestion is most beneficial to the evacuation of traffic congestion

4) Most of the traditional traffic signal control systems assume that the phase scheme of the signal has been determined, and optimize the signal timing algorithm, but ignore the optimization of the signal phase itself

Not being able to combine the real-time status of traffic flow to dynamically adjust the phase sequence, the control lags, and the efficiency is low

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