Intersection signal optical control method in environment of Internet of vehicles

Abstract

The invention discloses an intersection signal optical control method in an environment of Internet of vehicles. A signal timing plan is optimized by combining vehicle state information acquired at a signal intersection in real time, so that the delay time that a vehicle passes through the intersection is shortened. The method disclosed by the invention realizes an actual application of the technology of Internet of vehicles at the signal intersection, thereby favorably improving the traffic efficiency at the signal intersection.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles, in particular to an intersection signal optimization control method under the Internet of Vehicles environment. Background technique [0002] With the rapid development of my country's national economy, people's living standards are steadily improving, and the number of urban motor vehicles is also increasing year by year. In comparison, the construction of transportation infrastructure is slow, and it is difficult to meet the growing demand for transportation. As a result, serious traffic congestion problems have appeared in many areas of our country, especially in some large cities. Traffic congestion not only increases people's travel time and brings inconvenience to people's lives, but also generates a series of traffic accidents, environmental pollution, excessive energy consumption, etc., which seriously affects the efficient and orderly operation of the urban transportatio...

AI Technical Summary

Problems solved by technology

However, most of the traffic signal control systems that have been adopted in our country use fixed detectors on the road or on the side of the road to collect traffic flow information. large impact, and the installation and maintenance of fixed detectors also require a lot of cost

In addition, the detector data relied on by the traditional signal control method has a large granularity in time and space, and it is difficult to reflect the real-time changes of traffic flow, so it cannot meet the actual traffic travel needs in different time periods.

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