Intelligent traffic management system and method based on cloud computing

Abstract

The invention provides an intelligent traffic management system and method based on cloud computing. The system comprises a plurality of image acquisition units, a cloud computing server and traffic lights, each image acquisition unit comprises a camera, an image analysis module and a data storage module, the camera is used for shooting an image at a crossing, the image analysis module is used for recognizing vehicles in the image and counting the vehicles to obtain traffic flow data, the data storage module stores image data and the traffic flow data and uploads the traffic flow data to the cloud computing server, and the cloud computing server sets the traffic lights according to the traffic flow data. The method includes following steps: obtaining the traffic flow data of each crossing and uploading the data to the cloud computing server; setting a reference passing time T; and adjusting and setting the passing time of each crossing. According to the system and the method, real-time onsite images are acquired and statistics of the traffic flow data is performed so that the time of the traffic lights and information notification of vehicle GPSs are set, the road traffic management efficiency is improved, and the problem of road congestion is solved.

Description

technical field [0001] The present invention mainly relates to the field of public services, in particular to an intelligent traffic management system and method based on cloud computing. Background technique [0002] With the increasing number of private cars in the city, road traffic conditions have become a difficult problem for urban management. At present, most road traffic management still relies on timed traffic lights. However, this rigid management mode can easily lead to Some roads are congested, while some roads are idle at the same time. Therefore, this kind of management mode that relies on traffic lights for regular release cannot adapt to the current urban traffic conditions. [0003] With the improvement of urban intelligent management, especially the rise of cloud computing technology, many inventors have proposed the method of using cloud computing to realize the control of traffic lights. However, the methods currently proposed are mainly divided into two ...

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Problems solved by technology

[0002] With the increasing number of private cars in the city, road traffic conditions have become a difficult problem for urban management. At present, most road traffic management still relies on timed traffic lights. However, this rigid management mode can easily lead to Some roads are congested, while some roads are idle at the same time. Therefore, this management mode of fading out and relying on traffic lights for regular release can no longer adapt to the current urban traffic conditions.

[0003] With the improvement of urban intelligent management, especially the rise of cloud computing technology, many inventors have proposed the method of using cloud computing to realize the control of traffic lights. However, the methods currently proposed are mainly divided into two categories, one is Install sensors on vehicles so that the number and status of vehicles on the road can be monitored in real time, but this mode has many difficulties in practical applications. For example, sensors are not inherent parts of vehicles, so it is necessary to consider where to install sensors and consider Who will pay for the increased cost? In fact, no matter where the cost increases, consumers will pay the bill in the end. Obviously, consumers will not be willing to pay this cost, so effective promotion will not be achieved, and the entire system will not be able to Effective operation; the other is to access the city's traffic management system, and use the big data of the traffic management system to predict or judge the traffic flow in real time. This mode also has some problems, such as the relationship between the results of data analysis and the real-time status. There will be some delays, and there will be some deviations in the data in the traffic management system, which will lead to deviations in the intelligent control of the entire traffic system, and cannot meet the needs of fast and accurate traffic control

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