CN116363874B - Urban traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation - Google Patents

Urban traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation Download PDF

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CN116363874B
CN116363874B CN202310268121.3A CN202310268121A CN116363874B CN 116363874 B CN116363874 B CN 116363874B CN 202310268121 A CN202310268121 A CN 202310268121A CN 116363874 B CN116363874 B CN 116363874B
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hypergraph
traffic
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traffic state
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CN116363874A (en
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唐坤
徐添
郭唐仪
何流
徐永能
刘英舜
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Nanjing University of Science and Technology
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Abstract

The invention discloses an urban traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation, which comprises the following steps: extracting static space topology data and dynamic traffic state data among all traffic areas, and constructing a traffic area adjacency matrix, a recent traffic state feature matrix and a historical traffic state feature matrix; establishing a space topology hypergraph, a recent semantic hypergraph and a historical semantic hypergraph according to the extracted data; splicing the three hypergraphs to obtain a new fusion hypergraph, constructing a depth hypergraph convolution network on the basis, and performing hypergraph convolution layer by layer; and training the model by using the training set to obtain optimal model parameters, and predicting the traffic condition at the next moment by using the optimal model. According to the invention, the hypergraph data structure is introduced into the complex correlation modeling of the traffic condition, so that the second-order spatial adjacency of the traffic area is considered, the multi-mode high-order semantic correlation of the traffic state is considered, and the accuracy of road network traffic state prediction is improved.

Description

Urban traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation
Technical Field
The invention belongs to urban traffic prediction technology, and particularly relates to an urban traffic hypergraph convolution prediction method integrating multi-mode high-order semantic correlation.
Background
Providing active traffic condition information in urban networks is significant and challenging in traffic management and operation. Due to the nature and intuitiveness of traffic networks, the potential of graph roll-up network (GCN) based models in traffic prediction has been at a brand-new angle, with increasing attention. Typical GCN models use a pair-wise connection to capture only the second-order correlation between vertices. However, the potential dependencies hidden in the actual application data may go beyond the range of pairs, and they appear more highly correlated and even more complex in the face of multi-source data.
Disclosure of Invention
In order to solve the technical defects in the prior art, the invention provides an urban traffic hypergraph convolution prediction method integrating multi-mode high-order semantic correlation.
The technical scheme for realizing the purpose of the invention is as follows: a city traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation comprises the following steps:
Step 1: extracting static space topological structure data and dynamic traffic state data among all traffic areas in a target area, and constructing a traffic area adjacent matrix, a recent traffic state feature matrix taking a time period as an interval and a historical traffic state feature matrix taking a day as an interval;
step 2: establishing a topological hypergraph based on second-order spatial correlation, a semantic hypergraph based on continuous time period traffic state characteristics and a semantic hypergraph based on daily traffic state characteristics according to the extracted data;
Step 3: the method comprises the steps of constructing a depth hypergraph convolution network by taking a time period traffic state feature matrix, a daily traffic state feature matrix and a new hypergraph incidence matrix as inputs, and performing hypergraph convolution layer by layer;
Step 4: training the model by using the training set to obtain optimal model parameters, inputting traffic state characteristics at the next moment into the trained optimal model, and predicting the traffic state at the next moment.
Preferably, the specific steps of extracting static space topological structure data and dynamic traffic state data of each traffic area in the target area are as follows:
Step 11: the space topological structure among all traffic areas of the target area is extracted and is marked as an adjacent matrix A g, and the adjacent matrix A g data are expressed as:
Wherein a g (i, j) is the i-th row of the matrix A g, the element of the j-th column, v i is the i-th node in the graph, and the corresponding i-th traffic area;
Step 12: according to the traffic state data of each traffic area in different time periods, constructing a recent traffic state feature matrix X r of the current time t with the time period as an interval, which is expressed as follows:
Wherein, The traffic conditions of N areas at the time t are represented, N represents the number of traffic areas, and p represents a time period; each row vector/>, in feature matrix X r I=1, 2, l, n, representing the recent traffic feature vector of the i-th region, column vector/>J=1, 2, l, p denote traffic state information of all areas at different time periods;
step 13: historical data of the time period t+1 to be predicted in the previous q days are acquired to form a historical traffic state characteristic matrix X d with the days as intervals, and the historical traffic state characteristic matrix is expressed as:
Wherein the method comprises the steps of The traffic condition of N areas in the next time period t+1 is represented, T d represents the number of time periods included per day, and q represents the number of days considered.
Preferably, the specific methods for establishing the topological hypergraph based on the second-order spatial correlation, the recent semantic hypergraph based on the continuous time period traffic state characteristics and the historical semantic hypergraph based on the daily traffic state characteristics according to the extracted data are as follows:
step 21: according to the feature matrix X r, constructing a superside by a KNN nearest neighbor algorithm, specifically:
For each node v i, i=1, 2, l, n, k-1 nearest neighbor nodes are calculated in the row vector of matrix X r using KNN algorithm; node v i and k-1 nearest neighbor nodes are combined together to form a superedge Obtaining N superflimit;
splice all supersides into one Is denoted as an association matrix H r, expressed as:
Where H r (i, j) is the element of row i, column j of matrix H r;
Constructing semantic hypergraphs of recent traffic conditions according to hyperedges Wherein/>Representing a collection of traffic areas,/>Representing a hyperedge set in a recent semantic hypergraph;
Step 22: according to the feature matrix X d, constructing an overtravel by a KNN nearest neighbor algorithm, splicing all the overtravel to form an association matrix H d, and representing as
Wherein the method comprises the steps ofIs the j th superside of matrix H d;
semantic hypergraph for constructing historical traffic states according to hyperedges Wherein the method comprises the steps ofRepresenting a hyperedge set in a history semantic hypergraph;
Step 23: constructing a topological hypergraph based on second-order spatial correlation according to the extracted adjacency matrix A g Second order spatial correlation for reflecting traffic conditions;
For each edge of adjacency matrix A g, it is considered as a superedge containing only two nodes, thereby constructing a space topology supergraph The hypergraph is composed of one/>Is described by the association matrix H g;
Wherein the method comprises the steps of Is the j th superside of H g, represents the second order correlation between different traffic areas, and ε g represents the set of supersides in the spatial topology supergraph.
Preferably, the new hypergraph obtained after the three hypergraphs are spliced is used as the input of the depth network to carry out hypergraph convolution, and the specific method is as follows:
Step 31: splicing the incidence matrixes based on the spatial topological hypergraph and corresponding to the recent traffic state semantic hypergraph and the historical traffic state semantic hypergraph obtained in the step 3 to obtain a comprehensive hypergraph integrating the multi-mode high-order semantic correlation Comprehensive hypergraph/>By one dimension/>Is described by an association matrix H; splicing the recent traffic state feature matrix established in the step 1 with the historical traffic state feature matrix to form a comprehensive traffic state feature matrix
Step 32: taking the incidence matrix H and the feature matrix X obtained in the step 31 as deep network input, and performing hypergraph convolution;
the depth network is formed by stacking a plurality of hypergraph convolution layers;
The calculation formula of each hypergraph convolution layer is as follows:
Wherein the method comprises the steps of Is the input signal of the first layer in the hypergraph,/>Is a parameter matrix of the first layer, sigma is a nonlinear activation function, and D v,De and W are a node degree matrix, a superside degree matrix and a superside weight matrix respectively.
Preferably, the training set is used for training the prediction model, the error between the predicted value and the true value is minimized, and the specific method for calculating the optimal parameters of the prediction model is as follows:
Constructing a training set according to traffic state observed values of N traffic areas in T time periods Wherein/>Is a feature matrix,/>For predictive labels, p is the number of features;
inputting the training set into a prediction model for training, wherein the model training objective function is as follows:
Wherein the first term is a predicted mean square error empirical loss term, the second term is a model parameter regularization term, Representing hypergraph/>The upper parameter is a predictive model of Θ,/>As a loss function, m=t-p is the number of samples.
After model training, the feature matrix at the current moment is obtainedInputting the value to the optimal model obtained after training, and obtaining the prediction of the traffic condition at the next moment as follows:
Wherein the method comprises the steps of For the characteristic matrix at the current moment,/>And the parameters are the learned optimal model parameters.
Compared with the prior art, the invention has the remarkable advantages that:
(1) According to the invention, a new data structure, namely a hypergraph data structure, is introduced into complex correlation modeling of traffic conditions, so that high-order semantic correlation can be modeled, multi-mode correlation is incorporated, and the interpretability and the accuracy of a prediction model are improved by comprehensively considering related information;
(2) Based on the hypergraph learning theory, the invention provides a new convolution operator to learn the characteristic representation on the hypergraph, and the information transmission between the vertexes is more universal by fully utilizing the correlation between the high order and the multiple modes, and a basic component similar to the graph convolution is provided for the construction of the depth hypergraph network, so that the accuracy of the prediction result is improved;
(3) The traffic condition framework based on hypergraph is used for cooperatively predicting the traffic condition of the urban road network, and the geographic space association and the multimode high-order semantic association of the traffic condition are fused in a unified framework.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a flow chart of the urban traffic hypergraph convolution prediction method which is provided by the invention and is fused with the multimode high-order semantic correlation.
Fig. 2 is an input schematic of the present invention.
Fig. 3 is a hypergraph convolution schematic of the present invention.
Detailed Description
It is easy to understand that various embodiments of the present application can be envisioned by those of ordinary skill in the art without altering the true spirit of the present application in light of the present teachings. Accordingly, the following detailed description and drawings are merely illustrative of the application and are not intended to be exhaustive or to limit or restrict the application. Rather, these embodiments are provided so that this disclosure will be thorough and complete by those skilled in the art. Preferred embodiments of the present application are described in detail below with reference to the attached drawing figures, which form a part of the present application and are used in conjunction with the embodiments of the present application to illustrate the innovative concepts of the present application.
The invention relates to an end-to-end method, which consists of four main parts: data input, hypergraph construction, hypergraph convolution and traffic prediction. Based on the static topography data and the dynamic traffic data of the multimode, two semantic hypergraphs and one geospatial graph are constructed and integrated to describe the high-order semantic association and the second-order geospatial association of the fine granularity and the coarse granularity of the traffic condition. To learn the feature representation on hypergraphs, a new hypergraph convolution operator is derived from graph convolution and hypergraph learning theory. By using the proposed hypergraph convolution as a deep network of building blocks, a predicted advanced feature representation is learned and then traffic conditions are predicted. The invention captures the second-order geospatial correlation and the multi-mode high-order semantic correlation of traffic conditions together in a unified framework, and is quite attractive.
The invention will be further described with reference to the accompanying drawings and examples.
Referring to fig. 1, an urban traffic hypergraph convolution prediction method integrating multimode high-order semantic correlation includes the following steps:
Step 1: extracting static space topological structure data and dynamic traffic state data among all traffic areas in a target area, and constructing a traffic area adjacent matrix, a recent traffic state feature matrix taking a time period as an interval and a historical traffic state feature matrix taking a day as an interval;
step 2: establishing a topological hypergraph based on second-order spatial correlation, a semantic hypergraph based on continuous time period traffic state characteristics and a semantic hypergraph based on daily traffic state characteristics according to the extracted data;
Step 3: the method comprises the steps of constructing a depth hypergraph convolution network by taking a time period traffic state feature matrix, a daily traffic state feature matrix and a new hypergraph incidence matrix as inputs, and performing hypergraph convolution layer by layer;
Step 4: training the model by using the training set to obtain optimal model parameters, inputting traffic state characteristics at the next moment into the trained optimal model, and predicting the traffic state at the next moment.
In a further embodiment, as shown in the input part of fig. 1, step 1 extracts static space topology structure data and dynamic traffic state data between each traffic area in the target area, and the specific method for constructing the traffic area adjacency matrix, the recent traffic state feature matrix with time slots as intervals, and the historical traffic state feature matrix with days as intervals is as follows:
Step 11: the space topological structure among all traffic areas of the target area is extracted and is marked as an adjacent matrix A g, and the adjacent matrix A g data are expressed as:
Wherein a g (i, j) is the i-th row of the matrix A g, the element of the j-th column, v i is the i-th node in the graph, and the corresponding i-th traffic area;
Step 12: as shown in fig. 2, according to traffic state data of each traffic area in different time periods, a recent traffic state feature matrix X r of the current time t with the time period as an interval is constructed, and is expressed as follows:
Wherein, The traffic conditions of N areas at the time t are represented, N represents the number of traffic areas, and p represents a time period; each row vector/>, in feature matrix X r I=1, 2, l, n, representing the recent traffic feature vector of the i-th region, column vector/>J=1, 2, l, p denote traffic state information of all areas at different time periods;
Step 13: as shown in fig. 2, historical data of the period t+1 to be predicted for the previous q days is acquired, and a historical traffic state feature matrix X d with the days as intervals is formed, which is expressed as follows:
Wherein the method comprises the steps of The traffic condition of N areas in the next time period t+1 is represented, T d represents the number of time periods included per day, and q represents the number of days considered.
In a further embodiment, as shown in the hypergraph building block of FIG. 1, step 2 is specifically
Step 21: according to the feature matrix X r, constructing a superside by a KNN nearest neighbor algorithm, specifically:
For each node v i, i=1, 2, l, n, k-1 nearest neighbor nodes are calculated in the row vector of matrix X r using KNN algorithm; node v i and k-1 nearest neighbor nodes are combined together to form a superedge Obtaining N superflimit;
splice all supersides into one Is denoted as an association matrix H r, expressed as:
Where H r (i, j) is the element of row i, column j of matrix H r;
Constructing semantic hypergraphs of recent traffic conditions according to hyperedges Wherein/>Representing a collection of traffic areas,/>Representing a hyperedge set in a recent semantic hypergraph;
Step 22: according to the feature matrix X d, constructing an overtravel by a KNN nearest neighbor algorithm, splicing all the overtravel to form an association matrix H d, and representing as
Wherein the method comprises the steps ofIs the j th superside of matrix H d;
semantic hypergraph for constructing historical traffic states according to hyperedges Wherein the method comprises the steps ofRepresenting a hyperedge set in a history semantic hypergraph;
Step 23: constructing a topological hypergraph based on second-order spatial correlation according to the extracted adjacency matrix A g Second order spatial correlation for reflecting traffic conditions;
For each edge of adjacency matrix A g, it is considered as a superedge containing only two nodes, thereby constructing a space topology supergraph The hypergraph is composed of one/>Is described by the association matrix H g;
Wherein the method comprises the steps of Is the j th superside of H g, represents the second order correlation between different traffic areas, and ε g represents the set of supersides in the spatial topology supergraph.
In a further embodiment, as shown in the hypergraph convolution part in fig. 1, the specific method for performing hypergraph convolution by using the new hypergraph obtained by splicing three hypergraphs as the input of the depth network is as follows:
Step 31: splicing the incidence matrixes based on the spatial topological hypergraph and corresponding to the recent traffic state semantic hypergraph and the historical traffic state semantic hypergraph obtained in the step 3 to obtain a comprehensive hypergraph integrating the multi-mode high-order semantic correlation Comprehensive hypergraph/>By one dimension/>Is described by an association matrix H; splicing the recent traffic state feature matrix established in the step 1 with the historical traffic state feature matrix to form a comprehensive traffic state feature matrix
Step 32: taking the incidence matrix H and the feature matrix X obtained in the step 31 as deep network input, and performing hypergraph convolution;
Hypergraph convolution consists of two parts, hypergraph fusion and hypergraph convolution. Considering the relevance of multiple modes, the hypergraph is synthesized By/>Three sub hypergraphs. Hypergraph/>Is a set of ε g,εr,εd whose dimension is/>
To learn the feature representation of the composite hypergraph, a hypergraph convolution is performed with the feature matrices of the correlation matrices H, X r, and X d as inputs. Hypergraph convolution is a novel feature learning method specially designed for hypergraphs. Inspired by classical graph convolution and derived from hypergraph learning theory.
Comprehensive hypergraphLaplacian delta expression of (A) is as follows
Wherein D v,De and W are diagonal matrices of vertex degree, edge degree and edge weight, respectively, and H is hypergraphIs used for the correlation matrix of the (a). Which is an N x N semi-positive definite matrix.
Through characteristic decomposition, obtain
Where Φ= (Φ 1,φ2,L,φN) is a matrix composed of normal eigenvectors, Λ=diag (λ 1,λ2,L,λN) is a diagonal matrix composed of eigenvalues.
Since the eigenvectors in Φ form the orthogonal basis of space, the eigenvectors alone in spaceFourier transform of (a) into
Then with convolution kernelFrequency domain convolution of x of (2) is
Wherein the method comprises the steps ofIs an inverse fourier transform, e is a hadamard product, where the eigenvalue function g θ (Λ) is defined as
The time complexity of the Fourier transform and the inverse Fourier transform are both
G θ (Λ) can be approximated using chebyshev's K-th order expansion, and the frequency domain convolution in step 35 can be reduced to
Where theta k is the chebyshev coefficient,Is a laplace scaling operator, T k (x) chebyshev polynomial. T k (x) can be calculated from T k(x)＝2xTk-1(x)-Tk-2(x),T0(x)＝1,T1 (x) =x.
Since the higher order associations between vertices can be well represented by the laplacian in hypergraphs, the first order ChebNet is employed to further increase the computational efficiency, i.e., k=1, λ max =2. The convolution process may be simplified as follows:
Wherein both coefficients θ 0 and θ 1 can be reduced to using one coefficient
So the hypergraph convolution is finally
Based on the hypergraph convolution operator, a hypergraph convolution neural network can be constructed, and the characteristic expression of the hypergraph can be learned layer by layer. By stacking multiple layers of hypergraph convolutions to build a deep network, the convolution layers on the network can be calculated as follows:
the exploded view of each part of the formula is shown in FIG. 3
Wherein the method comprises the steps ofIs the input signal of the first layer in the hypergraph,/>Is the parameter matrix of the first layer, and is a nonlinear activation function. The next layer/>, by convolution with the kernel Θ (l) Mid-vertex feature representations are also available.
In a further embodiment, as shown in the prediction part in fig. 1, the training set is used to train the prediction model, the error between the predicted value and the true value is minimized, the optimal parameter of the prediction model is calculated, the feature representation learned by the last layer of the depth network is brought into the optimal parameter prediction model, and the specific method for predicting the traffic condition at the next moment is as follows:
Constructing a training set according to traffic state observed values of N traffic areas in T time periods Wherein/>Is a feature matrix,/>For predictive labels, p is the number of features;
the learning of the whole prediction model is to minimize the error between the predicted value and the true value on the training set, the training set is input into the prediction model for training, and the objective function of model training is as follows:
Wherein the first term is a predicted mean square error empirical loss term, the second term is a model parameter regularization term, Representing hypergraph/>The upper parameter is a predictive model of Θ,/>As a loss function, m=t-p is the number of samples.
Based on the learning model f, the feature matrix of the current moment is obtainedInputting the value to the optimal model obtained after training, and obtaining the prediction of the traffic condition at the next moment as follows:
Wherein the method comprises the steps of For the characteristic matrix at the current moment,/>And the parameters are the learned optimal model parameters.
The invention introduces a new data representation method, namely hypergraph, to simulate complex relations in traffic data.
The above description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto,
Any changes or substitutions that would be easily recognized by those skilled in the art within the technical scope of the present disclosure are intended to be covered by the present invention.
It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes described in the context of a single embodiment or with reference to a single figure in order to streamline the invention and aid those skilled in the art in understanding the various aspects of the invention. The present invention should not be construed as including the features of the exemplary embodiments that are essential to the patent claims.
It should be understood that modules, units, components, etc. included in the apparatus of one embodiment of the present invention may be adaptively changed to arrange them in an apparatus different from the embodiment. The different modules, units or components comprised by the apparatus of the embodiments may be combined into one module, unit or component or they may be divided into a plurality of sub-modules, sub-units or sub-components.

Claims (1)

1. The urban traffic hypergraph convolution prediction method integrating the multimode high-order semantic correlation is characterized by comprising the following steps of:
Step 1: the method comprises the following specific steps of extracting static space topology data and dynamic traffic state data among all traffic areas in a target area, and constructing a traffic area adjacent matrix, a recent traffic state feature matrix taking a time period as an interval and a historical traffic state feature matrix taking a day as an interval:
Step 11: the space topological structure among all traffic areas of the target area is extracted and is marked as an adjacent matrix A g, and the adjacent matrix A g data are expressed as:
Wherein a g (i, j) is the i-th row of the matrix A g, the element of the j-th column, v i is the i-th node in the graph, and the corresponding i-th traffic area;
Step 12: according to the traffic state data of each traffic area in different time periods, constructing a recent traffic state feature matrix X r of the current time t with the time period as an interval, which is expressed as follows:
Wherein, The traffic conditions of N areas at the time t are represented, N represents the number of traffic areas, and p represents a time period; each row vector/>, in feature matrix X r I=1, 2, …, N, representing the recent traffic feature vector of the i-th region, column vector/>J=1, 2, …, p, representing traffic status information of all areas at different time periods;
step 13: historical data of the time period t+1 to be predicted in the previous q days are acquired to form a historical traffic state characteristic matrix X d with the days as intervals, and the historical traffic state characteristic matrix is expressed as:
Wherein the method comprises the steps of Representing the traffic condition of N areas in the next time period t+1, T d representing the number of time periods contained each day, q representing the number of days considered;
Step 2: according to the extracted data, a space topology hypergraph based on second-order space adjacency, a recent semantic hypergraph based on continuous time period traffic state characteristics and a historical semantic hypergraph based on daily traffic state characteristics are established, and the specific method comprises the following steps:
Step 21: according to the feature matrix X r, utilizing a KNN nearest neighbor algorithm to construct a superside, specifically:
For each node v i, i=1, 2, …, N, k-1 nearest neighbors are calculated in the row vector of matrix X r using KNN algorithm; node v i and k-1 nearest neighbor nodes are combined together to form a superedge Obtaining N superflimit;
splice all supersides into one Is denoted as an association matrix H r, expressed as:
Where H r (i, j) is the element of row i, column j of matrix H r;
Constructing semantic hypergraphs of recent traffic conditions according to hyperedges Wherein/>Representing a collection of traffic areas,/>Representing a hyperedge set in a recent semantic hypergraph;
step 22: according to the feature matrix X d, utilizing KNN nearest neighbor algorithm to construct supersides, splicing all the supersides to form an association matrix H d, and representing as
Wherein the method comprises the steps ofIs the j th superside of matrix H d;
constructing semantic hypergraph of historical traffic state according to hyperedge Wherein/>Representing a hyperedge set in a history semantic hypergraph;
Step 23: constructing a space topology hypergraph based on second-order space adjacency according to the extracted adjacency matrix A g Second order spatial correlation for reflecting traffic conditions;
For each edge of adjacency matrix A g, it is considered as a superedge containing only two nodes, thereby constructing a space topology supergraph The hypergraph is composed of one/>Is described by the association matrix H g;
Wherein the method comprises the steps of The j th superside of H g represents the second-order correlation among different traffic areas, and epsilon g represents the superside set in the space topology supergraph;
Step 3: the three hypergraphs are spliced to obtain a new fusion hypergraph, a depth hypergraph convolution network is built by taking a time period traffic state feature matrix, a daily traffic state feature matrix and an incidence matrix of the fusion hypergraph as inputs, and the hypergraph convolution is carried out layer by layer, and the specific method comprises the following steps:
step 31: splicing the space topology hypergraph obtained in the step 2 and the incidence matrix corresponding to the recent traffic state semantic hypergraph and the historical traffic state semantic hypergraph to obtain a fusion hypergraph fusing the multimode high-order semantic correlation The fusion hypergraph/>By one dimension/>Is described by an association matrix H; splicing the recent traffic state feature matrix established in the step 1 with the historical traffic state feature matrix to form a comprehensive traffic state feature matrix/>
Step 32: taking the incidence matrix H and the feature matrix X obtained in the step 31 as deep network input, and performing hypergraph convolution;
the depth network is formed by stacking a plurality of hypergraph convolution layers;
The calculation formula of each hypergraph convolution layer is as follows:
Wherein the method comprises the steps of Is the input signal of the first layer in the hypergraph,/>Is a parameter matrix of a first layer, sigma is a nonlinear activation function, and D v,De and W are a node degree matrix, a superside degree matrix and a superside weight matrix respectively;
step 4: training the model by using a training set to obtain optimal model parameters, inputting traffic state characteristics of the next moment into the trained optimal model, and predicting the traffic state of the next moment, wherein the specific method comprises the following steps:
Constructing a training set according to traffic state observed values of N traffic areas in T time periods Wherein/>Is a feature matrix,/>For predictive labels, p is the number of features;
inputting the training set into a prediction model for training, wherein the model training objective function is as follows:
Wherein the first term is a predicted mean square error empirical loss term, the second term is a model parameter regularization term, Representing hypergraph/>The upper parameter is a predictive model of Θ,/>As a loss function, m=t-p is the number of samples;
After model training, the feature matrix at the current moment is obtained Inputting the traffic condition prediction model into an optimal model obtained after training, and obtaining the prediction of the traffic condition at the next moment as follows:
Wherein the method comprises the steps of For the characteristic matrix at the current moment,/>And the parameters are the learned optimal model parameters.
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