Depth learning-based regional traffic signal lamp control system and method

Abstract

The invention relates to a depth learning-based regional traffic signal lamp control system and method. The depth learning-based regional traffic signal lamp control system comprises an information acquisition unit, a storage unit, a 5G communication unit, a cloud end data processing and database unit, a regional road network model building unit, a Synchro-based simulation calculation unit, a depth learning-based traffic forecast unit a traffic signal control unit, wherein data such as traffic flow is acquired by the acquisition unit, 5G communication is used for transmission, data informationsuch as each intersection traffic flow is integrated at a cloud server, forecast is performed according to depth learning, an optimal control scheme is obtained by simulation of the forecasted traffic flow data according to Synchro and is finally transmitted to each intersection traffic light for execution, the traffic light management and control in the region can be effectively optimized, and apractical method is provided for traffic congestion reduction.

Description

technical field [0001] The invention relates to a regionalized traffic signal light control system and method based on deep learning, which belongs to the technical field of intelligent transportation. Background technique [0002] Along with the economic development, the scale of the city is gradually expanding, and the traffic volume is increasing. In order to meet people's growing travel needs, solve the problem of people and vehicles competing for roads, improve the quality of travel, and alleviate traffic congestion, it is necessary to add more traffic lights to improve The above phenomenon. However, the changing cycle of the currently used traffic lights is fixed, which cannot adapt to real-time road conditions at different intersections, and cannot solve the problem of uneven distribution of driving directions during peak traffic flows. [0003] The initial application of the existing intelligent traffic light control system has made up for some of the problems, but ...

AI Technical Summary

Problems solved by technology

There is no real-time collection of the current situation of different corresponding intersections such as weather and special events

[0005] 2. There is a lag in data

It takes a long time from collecting data to calculating the control plan, which makes there is a large time difference between information transmission and command execution, which seriously reduces the beneficial effect brought by the implementation of the control plan.

[0006] 3. Optimal control target is single

The control s...

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