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Traffic flow prediction method and system, and terminal device

A prediction method and technology of traffic flow, applied in traffic flow detection, traffic control system, traffic control system of road vehicles, etc. effect, the effect of improving the accuracy

Inactive Publication Date: 2020-04-07
SHENZHEN INST OF ADVANCED TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of this, the embodiment of the present application provides a traffic flow forecasting method, system, and terminal equipment to solve the problem that the existing traffic forecasting method ignores the spatial dependence of traffic, thus making the forecast inaccurate

Method used

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  • Traffic flow prediction method and system, and terminal device
  • Traffic flow prediction method and system, and terminal device
  • Traffic flow prediction method and system, and terminal device

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] figure 1It is a schematic flow chart of the traffic flow prediction method provided in Embodiment 1 of the present application, and the method may include the following steps:

[0050] Step S11: Collect traffic data in the specified time period of the target area, the specified time period includes the current N hours, N hours of the same time period of the previous day, and N hours of the same time period of the corresponding day of the previous week.

[0051] Step S12: Perform graph convolutional network training on the traffic data to capture the topology of the urban road network to obtain spatial features.

[0052] Step S13: Input the time series of the spatial features into the gated recurrent unit model, and obtain dynamic changes through the information transfer between units to capture the temporal features.

[0053] Step S14: Establish a prediction model according to the spatial characteristics and the time characteristics, so as to predict the traffic flow i...

Embodiment 2

[0063] Figure 6 It is a schematic structural diagram of the traffic flow forecasting system provided in Embodiment 2 of the present application. For the convenience of description, only the parts related to the embodiment of the present application are shown.

[0064] The forecasting system includes:

[0065] Acquisition module 61, is used for collecting the traffic data in the specified time period of target area, and described specified time period comprises current N hours, the N hours of the same time period of the previous day and the N hours of the same time period of the corresponding day of the previous week;

[0066] Spatial feature acquisition module 62, for carrying out graph convolutional network training to described traffic data, captures the topological structure of urban road network to obtain spatial feature;

[0067] The temporal feature acquisition module 63 is used to input the time series of the spatial features into the gated recurrent unit model, and o...

Embodiment 3

[0077] Figure 7 It is a schematic structural diagram of a terminal device provided in Embodiment 3 of the present application. Such as Figure 7 As shown, the terminal device 7 of this embodiment includes: a processor 70 , a memory 71 and a computer program 72 stored in the memory 71 and operable on the processor 70 . When the processor 70 executes the computer program 72, the steps in the first method embodiment above are implemented, for example figure 1 Steps S11 to S14 are shown. When the processor 70 executes the computer program 72, it realizes the functions of each module / unit in the above-mentioned device embodiments, for example Figure 6 Function of units 61 to 64 shown.

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Abstract

The method is suitable for the technical field of traffic prediction, and provides a traffic flow prediction method and system, and a terminal device. The method comprises the following steps of: acquiring traffic data in a specified time period of a target area, performing graph convolution network training on the traffic data, capturing a topological structure of an urban road network to obtainspatial features, inputting the time sequence of the spatial features into a gating cycle unit model, and obtaining dynamic changes through information transmission between units to capture time features; and establishing a prediction model according to the spatial features and the time features so as to predict the traffic flow of the target area according to the prediction model. According to the invention, a graph convolutional neural network based on an attention mechanism is applied to the prediction problem in the traffic field, high-dimensional features in the road traffic network can be better mined in a complex road structure, the accuracy of traffic flow prediction is improved in cooperation with the improved recurrent neural network, and the improvement effect is obvious especially in medium-term and long-term prediction.

Description

technical field [0001] The present application relates to the technical field of traffic forecasting, and in particular to a traffic flow forecasting method, system, terminal equipment, and computer-readable storage medium. Background technique [0002] Accurate real-time forecasts of traffic conditions are of critical importance to road users, the private sector and governments. Widely used transportation services, such as flow control, route planning, and navigation, also rely heavily on high-quality traffic situation assessments. Generally speaking, multi-scale traffic prediction is the premise and foundation of urban traffic control and guidance, and it is also one of the main functions of intelligent transportation system (ITS). In traffic research, the basic variables of traffic flow, namely speed, density and occupancy rate, are usually selected as indicators for monitoring the current state of traffic conditions and predicting the future. According to the length of...

Claims

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Application Information

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IPC IPC(8): G08G1/01G06N3/04G06N3/08
CPCG08G1/0125G08G1/0145G06N3/08G06N3/045
Inventor 叶可江田科烺须成忠
Owner SHENZHEN INST OF ADVANCED TECH
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