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Traffic flow prediction system and method and model training method

A technology for traffic flow and forecasting systems, applied in the field of data processing, to solve problems such as lack of

Pending Publication Date: 2020-01-10
北京顺智信科技有限公司
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Problems solved by technology

[0003] Therefore, the technical problem to be solved by the present invention is to overcome the defect that the prior art lacks a method that can truly realize the combination of spatial features and temporal features with relatively complex topological structures, so as to provide a traffic flow forecasting system, method and model training method

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  • Traffic flow prediction system and method and model training method
  • Traffic flow prediction system and method and model training method

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Embodiment 1

[0044] Existing traffic forecasting methods are mainly divided into two categories: one is based on time-dependent relationships; the other is based on time- and spatial-dependent relationships. For the time-dependent relationship model, it mainly includes ARIMA, Kalman filter model, support vector machine, k-nearest neighbor model, Bayesian model and local neural network model. However, such models only consider the dynamic changes of traffic conditions, ignoring the spatial dependence of traffic conditions. Therefore, changes in traffic conditions are limited by the road network, and traffic data cannot be accurately predicted. In order to make better use of spatial features, some studies introduce convolutional neural networks for spatial modeling. But convolutional neural networks are often used for Euclidean data, such as image data, normative networks, etc. These models cannot be used in urban road network data with complex topological structures, and essentially these...

Embodiment 2

[0085] An embodiment of the present invention provides a traffic flow prediction model training method, such as Figure 7 shown, including:

[0086] Step S110: Obtain historical traffic flow data within a preset time period, and perform preprocessing on the historical traffic flow data to construct training graph-structured traffic data. For a detailed description, see the description of graph-structured data in Embodiment 1 above.

[0087] Step S120: Input the traffic data of the training map structure into the neural network system, train the neural network system, and obtain the traffic flow forecasting model, the neural network system is the traffic flow forecasting system provided in the above-mentioned embodiment 1, and see the above-mentioned embodiment 1 for detailed description A description of the traffic flow forecasting system in .

[0088] In the traffic flow prediction model training method provided by the embodiment of the present invention, when training the t...

Embodiment 3

[0096] An embodiment of the present invention provides a traffic flow prediction model training device, such as Figure 8 shown, including:

[0097] The training data acquisition module 110 is used to acquire historical traffic flow data within a preset time period, and preprocess the historical traffic flow data to construct training graph-structured traffic data. For detailed description, see the description of graph-structured data in Embodiment 1 above.

[0098] The traffic flow prediction model training module 120 is used for inputting the training map structure traffic data into the neural network system, and training the neural network system to obtain the traffic flow prediction model. The neural network system is the traffic flow prediction system provided in the above-mentioned embodiment 1, For a detailed description, see the description of the traffic flow forecasting system in Embodiment 1 above.

[0099] The traffic flow prediction model training device provided...

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Abstract

The invention provides a traffic flow prediction system and method, and a model training method. The traffic flow prediction system comprises a first space-time convolution network, a second space-time convolution network, and an output layer, wherein the first space-time convolution network is used for generating a first space-time feature vector according to the graph structure data; the secondspace-time convolution network is used for generating a second space-time feature vector according to the graph structure data and the first space-time feature vector; and the output layer is used forgenerating a prediction value according to the second spatial-temporal feature vector. In the present invention, the traffic flow prediction system processes the graph structure data; and on the premise of ensuring the time attribute of the traffic data, the graph structure data gives the spatial attribute to the traffic data, and the spatial-temporal dependence relationship is obtained by superposing the two spatial-temporal convolutional networks, so that the traffic flow prediction system provided by the invention truly realizes prediction of the traffic flow by combining the spatial feature and the temporal feature of the traffic data.

Description

technical field [0001] The invention relates to the field of data processing, in particular to a traffic flow prediction system, method and model training method. Background technique [0002] A major problem in the development of modern cities is traffic congestion. To completely solve this chronic problem in urban development, real-time and efficient short-term traffic flow prediction is the key technology to realize advanced traffic control and guidance in intelligent transportation systems. , it can realize the rational use of the carrying capacity of the urban road network to the maximum extent. In the past few decades, the research on short-term traffic flow forecasting by scholars at home and abroad has been continuously developed, and many forecasting methods have been derived. Existing traffic forecasting methods are mainly divided into two categories: one is based on time-dependent relationships; the other is based on time- and spatial-dependent relationships. For...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G08G1/01
CPCG06Q10/04G08G1/0125G06N3/045G06Q50/40
Inventor 张立文程东坡陈忠然张艺於今王东朱峻涛
Owner 北京顺智信科技有限公司
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