CN112668797B - Long-short-period traffic prediction method - Google Patents

Abstract

The application discloses a long-term and short-term traffic prediction method, which comprises the steps of obtaining first historical traffic data of nodes in a constructed traffic road network diagram, and carrying out convolution processing on the first historical traffic data through a first convolution layer in a preset traffic prediction model; traffic prediction is carried out on the convolved first historical traffic data through a first iteration RNN operator in the model, a traffic prediction result of a first time step is output, and the traffic prediction result of the first time step is input to a next iteration RNN operator for traffic prediction until the T-th traffic prediction is carried out _p T of the iterative RNN operator output _p Traffic prediction results of the time steps are obtained through a splicing module in the model _p And the traffic prediction results of the time steps are spliced and then input into a second convolution layer for convolution processing, and the final traffic prediction result is output. The traffic prediction method solves the technical problems that the existing traffic prediction method has high prediction error accumulation and cannot simultaneously consider the long-term prediction precision and the short-term prediction precision.

Description

A Long-term and Short-term Traffic Forecasting Method

technical field

The present application relates to the technical field of traffic forecasting, in particular to a long-term and short-term traffic forecasting method.

Background technique

Traffic forecasting is a classic spatiotemporal forecasting problem, which is widely used in real life, such as smart city road network planning, intelligent travel route planning, urban public transportation systems, etc. Due to the highly nonlinear and complex nature of traffic data, existing technologies mainly use deep learning to solve traffic prediction problems. However, the existing traffic forecasting methods have the problems of high accumulation of forecast errors and the inability to take into account both long-term and short-term forecast accuracy.

Contents of the invention

The present application provides a long-term and short-term traffic forecasting method, which is used to solve the technical problems that the existing traffic forecasting methods have a high accumulation of forecast errors and cannot take into account both long-term and short-term forecasting accuracy.

In view of this, the first aspect of the present application provides a long-term and short-term traffic forecasting method, including:

Construct the traffic road network into a graph structure to obtain a traffic road network graph;

Acquiring first historical traffic data of nodes in the traffic road network graph;

The first historical traffic data is input to the preset traffic prediction model comprising the first convolutional layer, T _p iterative RNN operators, splicing modules and the second convolutional layer, so that the first convolutional layer is The first historical traffic data is subjected to convolution processing, and the first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, outputs the traffic prediction result of the first time step, and converts the first time step The traffic prediction result of one time step is input to the next iterative RNN operator for traffic prediction, until the T _pth iterative RNN operator outputs the traffic prediction result of the T _pth time step, and the splicing module performs the traffic prediction for the T _p time step The traffic prediction results of the first step are spliced, and the second convolutional layer performs convolution processing on the spliced traffic prediction results to output the final traffic prediction result.

Optionally, the iterative RNN operator includes a gated linear unit, a diffuse convolution layer and a fully connected layer;

The first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, and outputs the traffic prediction result of the first time step, including:

The gated linear unit extracts the time dependence of the first historical traffic data after convolution processing, and outputs the first feature;

The diffusion convolution layer extracts the spatial dependence of the first feature, and outputs the second feature;

The fully connected layer performs traffic prediction on the second feature, and outputs the traffic prediction result of the first time step.

Optionally, the gated linear unit extracts the temporal dependence of the first historical traffic data after convolution processing, and outputs the first feature, including:

The gated linear unit performs one-dimensional convolution processing on the first historical traffic data after convolution processing to obtain a first convolution feature and a second convolution feature,

The gated linear unit activates the second convolution feature through an activation function, and calculates the Hadamard product of the first convolution feature and the activated second convolution feature;

The gated linear unit performs residual connection between the first historical traffic data after convolution processing and the Hadamard product, and outputs a first feature.

Optionally, the diffusion convolution layer extracts the spatial dependence of the first feature, and outputs a second feature, including:

The diffusion convolution layer performs diffusion convolution feature extraction with an adaptive matrix on the first feature, and outputs a second feature.

Optionally, the second feature is:

为自适应矩阵，E₁、E₂分别为源节点嵌入、目标节点嵌入。Among them, Z is the second feature, P _f =A/rowsum(A) is the forward transfer matrix, A is the weight adjacency matrix; P _b = ^AT /rowsum(A ^T ) is the backward transfer matrix; X is the first Features, W is the parameter matrix of the diffusion convolution layer, and K is a constant;

is an adaptive matrix, and E ₁ and E ₂ are source node embedding and target node embedding respectively.

Optionally, the method also includes:

The weight adjacency matrix is calculated based on the traffic road network diagram, and the calculation formula of the weight in the weight adjacency matrix is:

Among them, a _ij is the weight of the edge between neighbor nodes i and j in the traffic road network graph, d _ij is the distance between neighbor nodes i and j, δ is the threshold parameter, and ε is the hyperparameter.

Optionally, the input of the first historical traffic data to a preset traffic prediction model comprising the first convolutional layer, T _p iterative RNN operators, splicing modules and the second convolutional layer, also includes:

Perform preprocessing on the first historical traffic data.

Optionally, the configuration process of the preset traffic prediction model is:

Acquiring second historical traffic data of nodes in the traffic road network graph;

The traffic prediction network is trained by the second historical traffic data to obtain the preset traffic prediction model.

Optionally, the loss function of the traffic prediction network is:

为预测的T_p个时间步的交通数据，/>

为未来T_p个时间步的真实交通数据，W_θ为交通预测网络的训练参数。in,

is the predicted traffic data of T _p time steps, />

is the real traffic data of T _p time steps in the future, and W _θ is the training parameter of the traffic prediction network.

As can be seen from the above technical solutions, the present application has the following advantages:

This application provides a long-term and short-term traffic forecasting method, including: constructing the traffic road network as a graph structure to obtain the traffic road network graph; obtaining the first historical traffic data of the nodes in the traffic road network graph; Input to the preset traffic prediction model including the first convolutional layer, T _p iterative RNN operators, splicing module and the second convolutional layer, so that the first convolutional layer performs convolution processing on the first historical traffic data, and the second An iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, outputs the traffic prediction result of the first time step, and inputs the traffic prediction result of the first time step to the next iterative RNN operator Carry out traffic prediction until the _T pth iterative RNN operator outputs the traffic prediction result of the T _p time step, the splicing module splices the traffic prediction results of the T _p time step, and the second convolutional layer The prediction results are processed by convolution, and the final traffic prediction results are output.

In the traffic forecasting method in this application, the acquired first historical traffic data is input into a preset traffic forecasting model, and a time step forecast is performed through an iterative RNN operator, and each traffic forecast result is input into the next iterative RNN operator Predict the next time step, and at the same time, as part of the final output, through each iterative RNN operator to predict a time step, you can extract effective long-term and short-term traffic spatio-temporal information, reduce the phenomenon of error accumulation, and improve the short-term Accuracy of traffic prediction; the traffic prediction results of all time steps are spliced through the splicing module, and then the spliced traffic prediction results are convoluted to output the final traffic prediction results, and the long-term and short-term traffic prediction results are obtained, taking into account the long-term and short-term traffic Forecasting accuracy, thus solving the technical problems that the existing traffic forecasting methods have a high accumulation of forecasting errors and cannot take into account the long-term and short-term forecasting accuracy at the same time.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without any creative effort.

Fig. 1 is a schematic flow chart of a long-term and short-term traffic forecasting method provided by the embodiment of the present application;

Fig. 2 is a schematic diagram of a traffic road network sensor distribution provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of an RNN structure provided in an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a gated linear unit provided in an embodiment of the present application;

FIG. 5 is a schematic structural diagram of a preset traffic prediction network model provided by the embodiment of the present application;

FIG. 6 is a schematic flow chart of traffic prediction performed by the first iterative RNN operator provided in the embodiment of the present application.

Detailed ways

The present application provides a long-term and short-term traffic forecasting method, which is used to solve the technical problems that the existing traffic forecasting methods have a high accumulation of forecast errors and cannot take into account both long-term and short-term forecasting accuracy.

In order to enable those skilled in the art to better understand the solution of the application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the drawings in the embodiment of the application. Obviously, the described embodiment is only It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

For ease of understanding, please refer to Figure 1, an embodiment of a long-term and short-term traffic forecasting method provided by the present application, including:

Step 101. Construct the traffic road network into a graph structure to obtain a traffic road network graph.

In the embodiment of the present application, the traffic road network can be constructed as a directed graph structure, and the traffic road network graph G=(V, E, A) can be obtained, and V is a node set, that is, a collection of sensors on the traffic road network. 2. Each sensor will record historical traffic data, such as speed, traffic flow, etc. E is the edge set between nodes, and A∈R ^N×N is the weighted adjacency matrix of the traffic road network graph G.

Further, the calculation formula of the weight in the weight adjacency matrix is:

Among them, a _ij is the weight of the edge between neighbor nodes i and j in the traffic road network graph, d _ij is the distance between neighbor nodes i and j, δ is the threshold parameter, which can be set to 0.1, ε is the hyperparameter, A specific value can be set according to the actual situation. The weight adjacency matrix A is composed of the weights of the edges between all neighbor nodes i and j in the traffic road network graph.

Step 102. Obtain first historical traffic data of nodes in the traffic road network graph.

为在第t个时间步的第i个节点的交通数据，x_t∈R^N为第t个时间步的所有节点的交通数据。本申请实施例中，通过获取历史的τ个交通数据，以预测未来T_p个时间步的交通数据。因此，本申请实施例中获取的第一历史交通数据可以表示为：After obtaining the traffic road network graph G, the first historical traffic data can be obtained through the nodes in it.

is the traffic data of the i-th node in the t-th time step, and x _t ∈ R ^N is the traffic data of all nodes in the t-th time step. In the embodiment of the present application, the traffic data of T _p time steps in the future are predicted by acquiring historical τ traffic data. Therefore, the first historical traffic data obtained in the embodiment of the present application can be expressed as:

X=(x ₁ , x ₂ ,..., x _τ )∈R ^N×τ ;

The traffic data that needs to be predicted in the future can be expressed as:

Step 103, input the first historical traffic data to the preset traffic prediction model comprising the first convolutional layer, T _p iterative RNN operators, splicing modules and the second convolutional layer, so that the first convolutional layer is relatively accurate to the first The historical traffic data is processed by convolution, and the first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, outputs the traffic prediction result of the first time step, and converts the traffic prediction result of the first time step The result is input to the next iterative RNN operator for traffic prediction, until the _T pth iterative RNN operator outputs the traffic prediction result of the T pth time step, the splicing module splices the traffic prediction results of the T _p time step, and the traffic prediction result of the T _pth time step is The second convolutional layer performs convolution processing on the concatenated traffic prediction results, and outputs the final traffic prediction results.

RNN is a type of neural network used to process sequence data. The difference from other basic neural networks that only establish connections between layers is that RNN also establishes weighted connections between neurons between layers. , because the data at the back of the sequence data is also closely related to the data at the front, as shown in Figure 3.

The embodiment of this application is based on the idea of RNN, an iterative prediction process is used as an iterative RNN operator, and the output prediction result is part of the final output result, and the single-step prediction result is input to the next iterative RNN operator at the same time. The preset traffic prediction model in the embodiment includes T _p iterative RNN operators for predicting traffic data of T _p time steps in the future. The iterative RNN operator in the embodiment of this application is different from the traditional RNN structure. Each RNN operator in the traditional RNN structure shares parameters, while the iterative RNN operator in the embodiment of the application includes the time and space of a prediction Modules (ie, linear gating unit and diffusion convolution layer), etc., are used to extract temporal dependencies and spatial dependencies, respectively, to complete single-step prediction, and the parameters of different iterative RNN operators are different.

The preset traffic prediction model in the embodiment of the present application includes a first convolutional layer, T _p iterative RNN operators, a splicing module and a second convolutional layer, as shown in FIG. 5 . By inputting the obtained first historical traffic data into the preset traffic prediction model to predict the traffic data of T _p time steps in the future, specifically, the first convolutional layer is used to perform convolution processing on the first historical traffic data, and the second An iterative RNN operator performs traffic prediction on the first convoluted historical traffic data, outputs the traffic prediction result of the first time step, and inputs the traffic prediction result of the first time step to the next iterative RNN operator for further calculation. Traffic prediction until the _T pth iterative RNN operator outputs the traffic prediction result of the T _p time step, the traffic prediction results of the T _p time step are spliced through the splicing module, and the spliced traffic prediction results are spliced by the second convolutional layer The traffic prediction results are convoluted to output the final traffic prediction results.

Further, before inputting the first historical traffic data to the preset traffic prediction model comprising the first convolutional layer, T _p iterative RNN operators, splicing modules and the second convolutional layer, it also includes: performing the first historical traffic data preprocessing. Specifically, a linear difference method may be used to fill in missing null values in the first historical traffic data, and a Z-Score method may be used to remove outliers.

Further, the iterative RNN operator in the embodiment of the present application includes a gated linear unit, a diffuse convolution layer and a fully connected layer. Please refer to Figure 6, the first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, and the specific steps for outputting the traffic prediction result of the first time step include:

S1031. The gated linear unit extracts the temporal dependence of the first historical traffic data after convolution processing, and outputs the first feature.

In the embodiment of the present application, the gated linear unit performs one-dimensional convolution processing on the first historical traffic data after convolution processing to obtain the first convolution feature and the second convolution feature; The second convolutional feature is activated, and the Hadamard product of the first convolutional feature and the activated second convolutional feature is calculated; the gated linear unit performs the convolutional first historical traffic data and the Hadamard product. Residual connection, output first feature.

C_i为卷积处理后的第一历史交通数据的维度，本申请实施例中因果卷积层的卷积核的大小为

门控线性单元对卷积处理后的第一历史交通数据进行一维卷积处理后，得到的卷积结果为/>

将卷积结果分为前后两部分，得到第一卷积特征P和第二卷积特征Q，第一卷积特征P和第二卷积特征Q的特征数和卷积前的输入特征数是一致的。The gated linear unit in the embodiment of the present application uses a convolution structure to capture the dynamic information of the first historical traffic data in the time dimension, as shown in FIG. 4 . Specifically, one-dimensional convolution processing is performed through a one-dimensional causal convolution layer. The width of the convolution kernel of the causal convolution layer is K _t . Under the action of the convolution kernel, the input data after convolution processing time The sequence length is shortened by K _t -1. For the first historical traffic data after input convolution processing

_Ci is the dimension of the first historical traffic data after convolution processing, and the size of the convolution kernel of the causal convolution layer in the embodiment of the present application is

After the gated linear unit performs one-dimensional convolution processing on the first historical traffic data after convolution processing, the obtained convolution result is

The convolution result is divided into two parts before and after, and the first convolution feature P and the second convolution feature Q are obtained. The feature numbers of the first convolution feature P and the second convolution feature Q and the input feature numbers before convolution are consistent.

In the embodiment of the present application, it is preferable to use the sigmoid activation function to activate the second convolutional feature Q for gating the first convolutional feature P, that is, to calculate the first convolutional feature and the activated second convolutional feature The Hadamard product P⊙σ(Q), ⊙ is the Hadamard product. The gated linear unit further performs residual connection between the first historical traffic data after convolution processing and the calculated Hadamard product, and outputs the first feature.

S1032. The diffusion convolution layer extracts the spatial dependence of the first feature, and outputs the second feature.

The diffusion convolution layer in the embodiment of the present application is a diffusion convolution layer with an adaptive matrix, and the diffusion convolution layer performs feature extraction with a diffusion convolution with an adaptive matrix on the first feature, and outputs the second feature.

Given the node structure information, K finite steps can be used to simulate the diffusion process of the signal, extract node features, and the diffusion convolution features obtained by diffusion convolution processing are:

Among them, P ^k is the power series of the transfer matrix, and the transfer matrix is calculated from the weighted adjacency matrix A. In the embodiment of this application, since the traffic road network structure is a directed graph, the diffusion process has two directions, that is, the forward transfer matrix P _f =A/rowsum(A), and the backward transfer matrix P _b = ^AT /rowsum (A ^T ), so the diffusion convolution feature can be expressed as:

In the formula, X is the first feature of the input, and W is the parameter matrix of the diffusion convolution layer.

Given the node structure information, the hidden spatial information can be obtained through the adaptive matrix, and the adaptive matrix is:

包括两个随机初始化可学习参数E₁、E₂∈R^N×c，具体的，E₁为源节点嵌入，E₂为目标节点嵌入，通过E₁、E₂相乘，得到源节点和目标节点之间的空间依赖权重，再通过ReLU激活函数来消除该权重的弱连接，通过SoftMax函数对ReLU激活函数处理后的权重进行归一化处理，自适应矩阵可以看作是隐扩散过程的转移矩阵。In the formula, the adaptive matrix

Including two randomly initialized learnable parameters E ₁ , E ₂ ∈ ^{R N×c} , specifically, E ₁ is source node embedding, E ₂ is target node embedding, by multiplying E ₁ and E ₂ , the source node and target The space between nodes depends on the weight, and then the weak connection of the weight is eliminated through the ReLU activation function, and the weight processed by the ReLU activation function is normalized through the SoftMax function. The adaptive matrix can be regarded as the transfer of the implicit diffusion process matrix.

In the embodiment of the present application, the explicit graph structure correlation is captured by the diffusion convolution, and the implicit graph structure correlation is captured by the adaptive matrix, and the spatial dependency is captured through the combination of the two, that is, the graph structure information of a given node, and finally The second feature output is:

S1033. The fully connected layer performs traffic prediction on the second feature, and outputs the traffic prediction result of the first time step.

The fully connected layer performs traffic prediction on the input second feature, and outputs the traffic prediction result y ₁ of the first time step.

The specific processing process of other iterative RNN operators is similar to the processing process of the first iterative RNN operator above, the difference is that the input data and output data of different iterative RNN operators are different.

Further, the configuration process of the preset traffic prediction model in the embodiment of the present application is: obtain the second historical traffic data of the nodes in the traffic road network graph; train the traffic prediction network through the second historical traffic data to obtain the preset traffic predictive model.

After obtaining the second historical traffic data, the time interval can be set to 5 minutes, that is, there will be 12 historical traffic data in one hour, and there are 288 historical traffic data in one day. It is also possible to preprocess the second historical traffic data, specifically, use a linear interpolation method to fill in missing null values in the second historical traffic data, and use a Z-Score method to remove outliers.

Wherein, the time period between the second historical traffic data and the current time is longer than the time period between the first historical traffic data and the current time. For example, if the sensors in a traffic network record 62 days of historical traffic data, the historical traffic data of the first 50 days can be used as a training set to obtain the second historical traffic data; the historical traffic data of the 51st to 56th days can be The data is used as a verification set to obtain the third historical traffic data; the historical traffic data of the last 6 days can be used as a test set to obtain the first historical traffic data. Of course, those skilled in the art can make flexible divisions according to actual conditions, which are not specifically limited here.

The traffic prediction network is trained through the second historical traffic data until the preset number of iterations is reached, and the training is stopped to obtain a preset traffic prediction model. During the training process, the third historical traffic data can be used for model verification.

During training, the loss value can be calculated through the loss function, and the parameters of each layer of the traffic prediction network can be updated through the backpropagation of the loss value. The loss function of the traffic prediction network can be:

为预测的T_p个时间步的交通数据，/>

为未来T_p个时间步的真实交通数据，W_θ为交通预测网络的训练参数。in,

is the predicted traffic data of T _p time steps, />

is the real traffic data of T _p time steps in the future, and W _θ is the training parameter of the traffic prediction network.

The long-term and short-term traffic forecasting method in the embodiment of the present application combines the advantages of iterative forecasting and one-time forecasting to ensure the accuracy of the long-term and short-term traffic forecasting results; and the preset traffic forecasting model in the embodiment of the present application can At the same time, it takes into account the accuracy of long-term and short-term predictions, and can reduce the accumulation of iterative prediction errors, because during training, each step of the prediction will adjust the error of the prediction results according to the real value.

In the traffic forecasting method in the embodiment of the present application, the acquired first historical traffic data is input into the preset traffic forecasting model, and a time-step forecast is performed through an iterative RNN operator, and each traffic forecasting result is input into the next iterative RNN The operator predicts the next time step, and as a part of the final output result, through each iteration of the RNN operator to predict a time step, effective long-term and short-term traffic spatio-temporal information can be extracted, reducing the phenomenon of error accumulation and improving The short-term traffic prediction accuracy is improved; the traffic prediction results of all time steps are spliced through the splicing module, and then the spliced traffic prediction results are convoluted to output the final traffic prediction results, and the long-term and short-term traffic prediction results are obtained, taking into account the long-term The accuracy of short-term traffic forecasting solves the technical problems that the existing traffic forecasting methods have a high cumulative forecast error and cannot take into account both long-term and short-term forecasting accuracy.

In the several embodiments provided in this application, it should be understood that the disclosed devices and methods may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or part of the contribution to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions for executing all or part of the steps of the methods described in the various embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (English full name: Read-OnlyMemory, English abbreviation: ROM), random access memory (English full name: Random Access Memory, English abbreviation: RAM), disk Or various media such as CDs that can store program codes.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still understand the foregoing The technical solutions described in each embodiment are modified, or some of the technical features are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the application.

Claims (8)

1. A long-term and short-term traffic forecasting method, characterized in that, comprising: Construct the traffic road network into a graph structure to obtain a traffic road network graph; Acquiring first historical traffic data of nodes in the traffic road network graph; The first historical traffic data is input to the preset traffic prediction model comprising the first convolutional layer, T _p iterative RNN operators, splicing modules and the second convolutional layer, so that the first convolutional layer is The first historical traffic data is subjected to convolution processing, and the first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, outputs the traffic prediction result of the first time step, and converts the first time step The traffic prediction result of one time step is input to the next iterative RNN operator for traffic prediction, until the T _pth iterative RNN operator outputs the traffic prediction result of the T _pth time step, and the splicing module performs the traffic prediction for the T _p time step The traffic prediction results of the first step are spliced, and the second convolution layer performs convolution processing on the spliced traffic prediction results, and outputs the final traffic prediction results; The iterative RNN operator includes a gated linear unit, a diffusion convolution layer and a fully connected layer; The first iterative RNN operator performs traffic prediction on the first historical traffic data after convolution processing, and outputs the traffic prediction result of the first time step, including: The gated linear unit extracts the time dependence of the first historical traffic data after convolution processing, and outputs the first feature; The diffusion convolution layer extracts the spatial dependence of the first feature, and outputs the second feature; The fully connected layer performs traffic prediction on the second feature, and outputs the traffic prediction result of the first time step. 2. The long-term and short-term traffic prediction method according to claim 1, characterized in that, said gated linear unit extracts the temporal dependence of said first historical traffic data after convolution processing, and outputs the first feature, comprising: The gated linear unit performs one-dimensional convolution processing on the first historical traffic data after convolution processing to obtain a first convolution feature and a second convolution feature, The gated linear unit activates the second convolution feature through an activation function, and calculates the Hadamard product of the first convolution feature and the activated second convolution feature; The gated linear unit performs residual connection between the first historical traffic data after convolution processing and the Hadamard product, and outputs a first feature. 3. The long-term and short-term traffic prediction method according to claim 2, wherein the diffusion convolution layer extracts the spatial dependence of the first feature, and outputs the second feature, including: The diffusion convolution layer performs diffusion convolution feature extraction with an adaptive matrix on the first feature, and outputs a second feature. 4. The long-term and short-term traffic prediction method according to claim 3, characterized in that, the second feature is:

Among them, Z is the second feature, P _f =A/rowsum(A) is the forward transfer matrix, A is the weight adjacency matrix; P _b = ^AT /rowsum(A ^T ) is the backward transfer matrix, and A ^T is the weight The transpose matrix of the adjacency matrix; X is the first feature, W is the parameter matrix of the diffusion convolution layer, and K is a constant;

is an adaptive matrix, E ₁ and E ₂ are source node embedding and target node embedding respectively, W _k1 is the parameter matrix of the forward transition matrix of the diffusion convolution layer at the kth finite step, W _k2 is the diffusion convolution layer at The parameter matrix of the backward transition matrix of the k-th finite step, W _k3 is the parameter matrix of the adaptive matrix of the k-th finite step of the diffusion convolution layer. 5. The long-term and short-term traffic prediction method according to claim 4, characterized in that, the method further comprises: The weight adjacency matrix is calculated based on the traffic road network diagram, and the calculation formula of the weight in the weight adjacency matrix is:

Among them, a _ij is the weight of the edge between neighbor nodes i and j in the traffic road network graph, d _ij is the distance between neighbor nodes i and j, δ is the threshold parameter, and ε is the hyperparameter. 6. the long-term and short-term traffic prediction method according to claim 1, is characterized in that, described first historical traffic data is input to comprise the first convolutional layer, _T iterative RNN operator, stitching module and the first The preset traffic prediction model of the second convolutional layer also includes: Perform preprocessing on the first historical traffic data. 7. The long-term and short-term traffic forecasting method according to any one of claims 1-6, wherein the configuration process of the preset traffic forecasting model is: Acquiring second historical traffic data of nodes in the traffic road network graph; The traffic prediction network is trained by the second historical traffic data to obtain the preset traffic prediction model. 8. The long-term and short-term traffic prediction method according to claim 7, wherein the loss function of the traffic prediction network is:

in,

is the predicted traffic data of T _p time steps, />

is the real traffic data of T _p time steps in the future, and W _θ is the training parameter of the traffic prediction network.

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