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Traffic flow statistics method, device and equipment and storage medium

A statistical method and technology of traffic flow, applied in the field of transportation, can solve problems such as high algorithm complexity, and achieve the effect of low complexity, little influence of light and weather, and small amount of data

Active Publication Date: 2020-07-03
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the complexity of the algorithms adopted by these techniques is high

Method used

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  • Traffic flow statistics method, device and equipment and storage medium
  • Traffic flow statistics method, device and equipment and storage medium
  • Traffic flow statistics method, device and equipment and storage medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0037] like figure 1 It is a flow chart of the traffic flow statistics method in Embodiment 1 of the present invention, and this embodiment can be applied to making portable mobile traffic flow monitoring equipment.

[0038] Embodiment 1 steps are as follows:

[0039] S110. Obtain training data, and train the designed convolutional neural network according to the training data to obtain an instantaneous vehicle number estimation model.

[0040] The convolutional neural network designed in this embodiment is used to complete a three-classification function, which can determine the instantaneous number of vehicles in the detection area according to the detection data of the radar in the detection area. Completing this function through the convolutional neural network is compared to The calculation speed of the traditional algorithm is faster and the requirements for the equipment are not high. In order to improve the accuracy of determining the instantaneous vehicle number thro...

Embodiment 2

[0050] Embodiment 2 of the present invention further supplements part of the content on the basis of Embodiment 1, specifically as follows:

[0051] like image 3 As shown, in step S110, the convolutional neural network designed according to the training data training to obtain the instantaneous vehicle number estimation model specifically includes:

[0052] S111. Input the input data in the training data into the designed convolutional neural network to obtain the number of vehicles at the moment of training.

[0053] S112. Comparing the output data in the training data with the training instantaneous vehicle number to obtain an error, and feedback and adjust the convolutional neural network.

[0054] S113. After iteratively performing the above steps for a predetermined number of times, the adjusted convolutional neural network is obtained as the instantaneous vehicle number estimation model.

[0055] Steps S111-113 are the training process of the convolutional neural netw...

Embodiment 3

[0064] Figure 5 A vehicle flow counting device 300 provided in Embodiment 3 of the present invention specifically includes the following modules:

[0065] The model training module 310 is used to acquire training data, and train a designed convolutional neural network according to the training data to obtain an instantaneous vehicle number estimation model.

[0066] The model application module 320 is used to obtain radar monitoring data, and input the radar monitoring data into the instantaneous vehicle number estimation model to obtain the instantaneous vehicle number and determine the driving state of the lane.

[0067] The traffic flow statistics module 330 is configured to judge lane traffic state change information according to the instantaneous vehicle number and lane traffic state, and count traffic flow according to the lane traffic state change information.

[0068] More specifically, the model training module 310 includes:

[0069] a data acquisition unit, config...

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Abstract

The embodiment of the invention discloses a traffic flow statistics method, device and equipment and a storage medium. The method specifically comprises steps of obtaining training data, and traininga designed convolutional neural network according to the training data to obtain an instantaneous vehicle number estimation model; acquiring radar monitoring data, inputting the radar monitoring datainto the instantaneous vehicle number estimation model to obtain the instantaneous vehicle number, and determining a lane driving state; and judging lane driving state change information according tothe instantaneous vehicle number and the lane driving state, and counting the traffic flow according to the lane driving state change information. The method is based on radar for monitoring. The method has advantages of simple operation, low influence of light and weather, no need of destroying a road surface, low requirement on operational capability, quicker operation, low complexity of an algorithm method, easiness in realization on embedded equipment and capability of being manufactured into a portable traffic flow monitor as the data volume acquired by the radar is small and the convolutional neural network does not need to be designed very complexly.

Description

technical field [0001] The present invention relates to the traffic field, in particular to a traffic flow statistics transmission method, device, equipment and storage medium. Background technique [0002] Traffic flow information is the most important information in an intelligent transportation system. Grasping the traffic flow information at key intersections plays a key role in rationally allocating traffic resources. Common traffic flow statistics methods can be divided into geomagnetic detection technology, video detection technology and radar-based detection technology used in this paper. [0003] The geomagnetic detection technology buries the sensor device underground, and the electric signal changes when the vehicle passes through the sensor detection area. The electric signal is used as the input signal of the detection system, and the statistics of the traffic flow can be completed by using the peripheral circuit and algorithm. The detection accuracy is high, bu...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/065G06N3/08G06N3/04G01S13/88
CPCG08G1/0125G08G1/065G06N3/08G01S13/88G06N3/045
Inventor 阳召成曾鹏
Owner SHENZHEN UNIV