CN111091712A - A Traffic Flow Prediction Method Based on Recurrent Attention Dual Graph Convolutional Networks - Google Patents

A Traffic Flow Prediction Method Based on Recurrent Attention Dual Graph Convolutional Networks Download PDF

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CN111091712A
CN111091712A CN201911355366.XA CN201911355366A CN111091712A CN 111091712 A CN111091712 A CN 111091712A CN 201911355366 A CN201911355366 A CN 201911355366A CN 111091712 A CN111091712 A CN 111091712A
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陈岭
陈纬奇
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Zhejiang University ZJU
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Abstract

The invention discloses a traffic flow prediction method based on a convolution network of a circular attention dual graph, which comprises the following steps: 1) constructing a traffic flow time sequence according to traffic flow data collected by road coil sensors on an urban traffic network, and preprocessing the traffic flow time sequence; 2) constructing a dual graph to represent a spatial dependency relationship, wherein the dual graph comprises a node graph and an edge graph, wherein a single sensor is regarded as a node, the node graph is constructed according to the road network distance between the sensors, edges in the node graph represent the relationship between the sensors, the edges in the node graph are regarded as the nodes to construct the edge graph, and the edges in the edge graph represent the mutual influence of the relationship between the sensors; 3) inputting the preprocessed traffic flow time sequence into a convolution network of the circular attention dual graph, and predicting the traffic flow of a future traffic network. The traffic flow prediction method can realize the prediction of the traffic flow of the traffic network, and has wide application prospect in the fields of travel planning, traffic management and the like.

Description

Traffic flow prediction method based on cyclic attention dual graph convolution network
Technical Field
The invention relates to the field of intelligent traffic systems, in particular to a traffic flow prediction method based on a convolution network of a circular attention dual map.
Background
With the continuous promotion of urbanization and industrialization, the number of automobiles is continuously increased, and urban traffic gradually becomes congested. The urban disease affects daily trips of people, and brings great challenges to urban road planning and traffic management of relevant departments. The method can effectively guide people to plan a travel route by accurately predicting the future traffic flow of the traffic network, and can also provide powerful data support for traffic management, so that the traffic flow prediction becomes an extremely valuable research direction in an intelligent traffic system.
Early traffic flow prediction methods mostly use traditional linear sequence models to predict the traffic flow of a single node, such as autoregressive moving average model (ARMIA), Kalman Filtering (Kalman Filtering), and the like. However, such methods ignore non-linear relationships in traffic flow data, and do not take into account spatial dependencies between multiple nodes.
In order to model complex space-time dependency, researchers have proposed deep learning based traffic flow prediction methods. Some methods model temporal dependencies using Recurrent Neural Networks (RNNs) while modeling spatial dependencies using Convolutional Neural Networks (CNNs). Such a traffic flow prediction method based on CNN can only represent the spatial dependency relationship in a regular grid structure, but the spatial dependency relationship between nodes is often represented as a non-european relationship subject to the constraint of an irregular traffic network. Therefore, the latest deep learning-based method uses a Graph structure to represent the spatial dependency relationship among nodes, edges in a Graph represent the connection relationship of the nodes in a traffic Network, and a Graph Convolutional Network (GCNs) is introduced to aggregate information of a certain node and its neighbor nodes, so as to model the non-european dependency relationship, and the prediction accuracy is higher compared with the CNN-based method.
However, most of the recent traffic flow prediction methods based on the GCN use an unweighted graph or a weighted graph with fixed weight to represent the relationship between nodes, thereby excessively simplifying the complex spatial dependency relationship in the actual traffic network. Additionally, these methods aggregate information within a given neighborhood range (e.g., nodes within a k-hop range), however, different neighborhood ranges tend to exhibit different traffic characteristics, e.g., neighbors within a small range can represent local spatial dependencies and neighbors within a large range tend to reflect overall traffic patterns over a relatively large area. The existing method ignores the influence of different neighbor ranges and cannot model a multi-range spatial dependency relationship.
Disclosure of Invention
The invention aims to solve the technical problem of how to model the complex space-time dependency relationship in traffic flow data, provides a traffic flow prediction method based on a convolution network of a circular attention dual graph, and aims to solve the defects of the prior art in the background technology.
In order to solve the above problems, the present invention provides a traffic flow prediction method based on a convolutional network of a cyclic attention dual graph, which comprises the following steps:
step 1, constructing a traffic flow time sequence according to traffic flow data collected by road coil sensors on an urban traffic network, and preprocessing the traffic flow time sequence;
step 2, calculating the road network distance between the coil sensors, and constructing a node map according to the road network distance
Figure BDA0002335757840000021
Wherein, VnIs a set of nodes, EnIs a set of edges, AnIs a contiguous matrix;
step 3, node map
Figure BDA0002335757840000022
Edge E innDefining two edge influence modes of upstream and downstream connection relation and competition relation as the nodes of the edge graph, constructing the edges of the edge graph according to the two edge influence modes, and then constructing an edge graph Ge＝(Ve,Ee,Ae) Wherein V iseIs a set of nodes, EeIs a set of edges, AeIs a contiguous matrix;
step 4, according to the node map
Figure BDA0002335757840000034
And edge graph GeConstructing a convolution network of a dual graph, and dividing the time tTraffic flow XtInputting k layers of the convolutional network of the dual graph, and expressing the node of each layer output as
Figure BDA0002335757840000031
Figure BDA0002335757840000032
Step 5, the output nodes of each layer of the convolution network of the dual graph represent input multi-range attention network for fusion, and the output fusion represents Ut；
Step 6, the past T is processed′Fused representation of individual moments U(s-T′+1):sThe coding and decoding structure long-time memory network shared among the input nodes outputs traffic flow predicted values at T moments in the future
Figure BDA0002335757840000033
According to the method, not only the dependency relationship among the nodes is considered when the spatial dependency relationship is modeled, but also the mutual influence of the relationships among the nodes is considered, and meanwhile, an attention mechanism is introduced to model the multi-range spatial dependency relationship. Compared with the prior method, the method has the advantages that:
1) and constructing a node graph according to the road network distance between the sensors, constructing an edge graph according to the edge influence mode, and modeling the relationship between the nodes and the relationship between the edges by using a dual graph convolution network display mode, thereby modeling a more complex spatial dependence relationship.
2) The multi-range attention mechanism can aggregate information of a plurality of neighbor ranges, and simultaneously learn a self-adaptive weight for different neighbor ranges, so that the expression capability of the model is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is an overall flowchart of a traffic flow prediction method based on a convolutional network of a cyclic attention dual graph according to an embodiment of the present invention;
fig. 2 is an edge influence mode provided by an embodiment of the present invention, in which (a) is an upstream and downstream connection relationship edge influence mode, and (b) is a competition relationship edge influence mode;
FIG. 3 is a block diagram of a convolutional trellis diagram for a dual graph according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a fusion of a multi-range attention network according to an embodiment of the present invention;
fig. 5 is a coding and decoding LSTM network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the detailed description and specific examples, while indicating the scope of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Fig. 1 is an overall flowchart of a traffic flow prediction method based on a convolutional network of a cyclic attention dual graph according to an embodiment of the present invention. The traffic flow prediction method is intended to realize the following tasks: let the current time be s, and according to the past T of N nodes on the traffic network′Traffic flow of every moment
Figure BDA0002335757840000041
Predicting traffic flow at T moments in the future
Figure BDA0002335757840000042
Figure BDA0002335757840000043
Referring to fig. 1, the traffic flow prediction method includes the steps of:
step 1, constructing a traffic flow time sequence according to traffic flow data collected by road coil sensors on an urban traffic network, and preprocessing the traffic flow time sequence.
Road coil sensors are widely used for road vehicle detection, vehicle type identification. Counting the number of vehicles detected by the coil sensor in a time window, and obtaining the traffic flow passing through the road node where the coil sensor is located in the time window. The number of coil sensors on the road network is N,
Figure BDA0002335757840000044
is represented by the formula (T ═ s-T)′+1,s-T′+2, …, s) time, and processing missing values and abnormal values for the traffic flow by using a linear interpolation method. Let the current time be s, count the past T′The traffic flow of each moment and the time sequence of the traffic flow is constructed
Figure BDA0002335757840000045
This is taken as input data.
Step 2, calculating the road network distance between the coil sensors, and constructing a node map according to the road network distance
Figure BDA0002335757840000059
Wherein, VnIs a set of nodes, EnIs a set of edges, AnIs a contiguous matrix;
wherein a node map is constructed
Figure BDA0002335757840000052
The method comprises the following specific steps:
(a) building a node graph
Figure BDA0002335757840000053
Node set V ofn＝{v1,v2,…,vNIn which, | VnN, node set VnOne element in (1) corresponds to one road node;
(b) calculating the road network distance between any two road nodes, dist (v)i,vj) The shortest road network distance from road node i to road node j is shown, and attention is paid to the fact that the road nodes areThe way tends to be directional, dist (v)i,vj)≠dist(vj,vi)；
(c) Calculation based on road network distance between nodes
Figure BDA0002335757840000054
Adjacent matrix A ofnWherein the adjacent matrix AnThe calculation formula of (2) is as follows:
Figure BDA0002335757840000051
wherein σ2D is a threshold value set manually, and when the shortest road network distance from a road node i to a road node j is greater than d, the graph is ignored
Figure BDA0002335757840000055
The edge from the middle road node i to the road node j, the guarantee graph
Figure BDA0002335757840000056
Sparsity of (3) to prevent over-fitting of the model.
(d) Building a node graph
Figure BDA0002335757840000057
Edge set E ofn＝{(i→j)|0≤i,j≤N,Ai,j>0, where (i → j) represents an edge with i as the head node and j as the tail node.
Step 3, node map
Figure BDA0002335757840000058
Edge E innDefining two edge influence modes of upstream and downstream connection relation and competition relation as the nodes of the edge graph, constructing the edges of the edge graph according to the two edge influence modes, and then constructing an edge graph Ge＝(Ve,Ee,Ae) Wherein V iseIs a set of nodes, EeIs a set of edges, AeIs a contiguous matrix;
wherein, an edge graph G is constructedeComprises the specific steps of：
(a) Constructing a boundary graph GeNode set V ofeEdge graph G ═ EeThe node in (1) corresponds to the node map
Figure BDA00023357578400000510
An edge of (1);
(b) for the path: defining the connection relationship between the upstream and the downstream as follows: (i → j) is the upstream side of (j → k), and (j → k) is the downstream side of (i → j), and defines AeThe weight of the upstream and downstream connection relationship between (i → j) and (j → k);
considering that a road segment (represented as an edge in a node map) in a traffic network is influenced by the road segments upstream and downstream, the upstream and downstream connection relation is introduced when the edge map is constructed;
taking fig. 2(a) as an example, when the degree of the connecting node j between (i → j) and (j → k) is larger, the influence relationship between (i → j) and (j → k) is weaker because the influence relationship is susceptible to influence of other neighbors, and in view of the above characteristics, a is definedeThe weight of the upstream and downstream connection relationship between (i → j) and (j → k) is as follows:
Figure BDA0002335757840000061
wherein deg is-(. and deg)+(. to) denote the in-degree and out-degree of the node, respectively2Representing the variance of the node degrees.
(c) For the path: a road node i to a road node k denoted as (i → k), a road node j to a road node k denoted as (j → k), i.e., (i → k) and (j → k) share the same end node, are defined as competing relationships, and define AeThe weight of the competitive relationship between (i → k) and (j → k);
considering that road segments connected to the same node in a traffic network compete for downstream traffic resources, a competitive relationship is introduced when constructing the edge graph. Taking fig. 2(b) as an example, when the out-degree of the upstream nodes i and j is large, the vehicle will have multiple routes to select when passing through the nodes i and j, so that the vehicle will have multiple routes to select when passing through the nodes i and jThe competition relationship is weak. In view of the above characteristics, define AeThe weight of the competitive relationship between (i → k) and (j → k) is as follows:
Figure BDA0002335757840000062
(d) constructing a boundary graph GeEdge set E ofeWherein E iseWherein the element is GeThe edge of (2).
Step 4, according to the node map
Figure BDA00023357578400000711
And edge graph GeConstructing a convolution network of a dual graph and calculating the traffic flow X at the time ttInputting k layers of the convolutional network of the dual graph, and expressing the node of each layer output as
Figure BDA0002335757840000071
Figure BDA0002335757840000072
The graph convolutional network is a deep neural network for processing graph structure data, can model message transmission among nodes, and is widely applied to application scenarios such as social network analysis and chemical molecular modeling. Let graph G be (V, E, a), a typical graph convolution network is calculated as follows:
Figure BDA0002335757840000073
wherein,
Figure BDA0002335757840000074
for the input node characteristics, N is the number of nodes, P is the characteristic dimension of each node,
Figure BDA0002335757840000075
for the parameters of the graph convolution network,
Figure BDA0002335757840000076
to take into account self-connected adjacency matrices, INIs an identity matrix of size N x N,
Figure BDA0002335757840000077
is composed of
Figure BDA0002335757840000078
P (-) is a non-linear activation function. The one-layer graph convolution network can aggregate the messages of the 1-hop neighbors for each node, and the range of the message-passing neighbors can be enlarged by stacking the multi-layer graph convolution networks.
As shown in FIG. 3, the invention designs a dual graph convolution network with graph convolution network as a component, which comprises k layers of node graph convolution network and k-1 layers of edge graph convolution network, can simultaneously model the message transmission of nodes and edges, and uses the dual graph convolution network to process the traffic flow at t moment
Figure BDA0002335757840000079
The specific steps (for simplicity of notation, the variables of steps (a) - (d) omit the subscript t without causing ambiguity) are:
(a) constructing node-edge mapping matrices
Figure BDA00023357578400000710
To represent the correspondence between road nodes and edges, where each row of M represents a road node, each column represents an edge, and M is defined as: mi,(i→j)＝M j,(i→j)1, and the other positions are 0;
(b) m pairs of node maps according to node-edge mapping matrix
Figure BDA00023357578400000712
Input X of(0)Linear transformation is performed on X and mapped to edge graph GeInput Z of(0)：
Z(0)＝MTX(0)Wb(5)
Wherein, WbIs a learnable mapping matrix;
(c) side map GeInput Z of(0)Inputting k-1 layers of edge graph convolution networks, and outputting edge representations of each layer:
Figure BDA0002335757840000081
wherein ★ G represents a graph convolution operation,
Figure BDA0002335757840000082
parameters for convolution of the l +1 th layer edge map, Z(l)And Z(l+1)Edge representations respectively representing the output of the l < th > layer and l +1 < th > layer edge graph convolutional networks;
(d) mixing X(0)Inputting a k-layer node graph convolution network, and outputting node representations of all layers, wherein the first-layer node graph convolution network does not consider edge representation, and a back k-1-layer network considers edge representation:
Figure BDA0002335757840000083
Figure BDA0002335757840000084
wherein,
Figure BDA0002335757840000085
is the parameter of the l +1 level node graph convolution [, ]]Indicating a splicing operation, X(l)And X(l+1)Node representations representing the output of the l-th layer and l + 1-th layer node graph convolution network respectively;
(e) taking the output of the node graph convolution network as the output of the dual graph convolution network, and outputting node representation of k layers of the dual graph convolution network at t time
Figure BDA0002335757840000086
The output of each layer represents information for a different neighbor range, where F is the dimension of the output node representation.
Step 5, the nodes output by each layer of the convolution network of the dual graph represent input multi-range attention networks to be fused, and output fusion is carried outCombined expression Ut。
The multi-range attention network is shown in fig. 4, and the specific steps of fusing node representations output by k layers of the convolutional network of the dual graph by using the attention network (for simplifying the labeling, the variable from the step (a) to the step (c) omits a subscript t without causing ambiguity) are as follows:
(a) node representation X for each layer output of the convolutional network of the dual graph(l)A linear transformation is performed, mapping it to the metric space:
Q(l)＝X(l)Wa(9)
wherein,
Figure BDA0002335757840000087
in order for the mapping matrix to be learnable,
Figure BDA0002335757840000088
is a node representation after mapping to the metric space.
(b) Representation after mapping to metric space for node i of l-th layer
Figure BDA0002335757840000091
Figure BDA0002335757840000092
Is Q(l)Is computed with a learnable global neighbor-wide context representation
Figure BDA0002335757840000093
And performing SoftMax normalization between layers to obtain the normalized weight represented by each layer of nodes:
Figure BDA0002335757840000094
Figure BDA0002335757840000095
wherein,
Figure BDA0002335757840000096
and
Figure BDA0002335757840000097
expressed as non-normalized and normalized weights, respectively.
(c) Using normalized weights
Figure BDA0002335757840000098
Carrying out weighted summation on the single node representations of each layer and outputting a fused representation of the single nodes
Figure BDA0002335757840000099
Figure BDA00023357578400000910
Wherein,
Figure BDA00023357578400000911
is X(l)Represents the representation of the level i node.
(d) Splicing the fusion expression of N nodes at the t moment to finally obtain
Figure BDA00023357578400000912
Step 6, the past T is processed′Fused representation of individual moments U(s-T′+1):sInputting a coding and decoding structure Long-time Memory (LSTM) network shared among nodes, and outputting traffic flow predicted values at T moments in the future
Figure BDA00023357578400000913
LSTM networks are a class of recurrent neural networks that can be used to model temporal dependencies in time series data. To effectively extract the features of time series data, a multi-layer LSTM network is generally adopted to enhance the nonlinear capability of the model. In order to balance the fitting capability and complexity of the model, the invention adopts two layers of LSTM networks to model the traffic flow time sequence. As shown in fig. 5, the present invention is based onLSTM constructs a coding and decoding frame shared among nodes, wherein the step length of an encoder LSTM network is T′The step size of the decoder LSTM network is T. Will pass T′Fused representation of individual moments U(s-T′+1):sInputting the encoder and the decoder to output predicted traffic flow values at T time points in the future
Figure BDA00023357578400000914
The traffic flow prediction method uses the dual graph to display and represent the dependency relationship between the nodes and the mutual influence of the relationships between the nodes, and simultaneously introduces the attention mechanism to model the multi-range spatial dependency relationship, so as to model the complex spatial dependency relationship in the traffic flow data, thereby predicting the traffic flow of a traffic network, and having wide application prospects in the fields of trip planning, traffic management and the like.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.

Claims (6)

1.一种基于循环注意力对偶图卷积网络的交通流量预测方法，包括以下步骤：1. A traffic flow prediction method based on recurrent attention dual graph convolutional network, comprising the following steps: 步骤1，根据城市交通路网上道路线圈传感器所采集的车流数据构建交通流量时序，并进行预处理；Step 1, construct a traffic flow time series according to the traffic flow data collected by the road coil sensor on the urban traffic road network, and perform preprocessing; 步骤2，计算线圈传感器之间的路网距离，根据该路网距离构建节点图
Figure FDA0002335757830000015
其中，Vn为节点集，En为边集，An为邻接矩阵；Step 2: Calculate the road network distance between the coil sensors, and build a node graph according to the road network distance
Figure FDA0002335757830000015
Among them, V n is the node set, E n is the edge set, and An is the adjacency matrix; 步骤3，将节点图
Figure FDA0002335757830000016
中的边En视作边图的节点，定义上下游连接关系和竞争关系两种边影响模式，根据该两种边影响模式构建边图的边，继而构建边图Ge＝(Ve，Ee，Ae)，其中，Ve为节点集，Ee为边集，Ae为邻接矩阵；Step 3, the node graph
Figure FDA0002335757830000016
The edge En in is regarded as the node of the edge graph, defines two edge influence modes of upstream and downstream connection relationship and competition relationship, constructs the edge of the edge graph according to the two edge influence modes, and then constructs the edge graph Ge =(V e , E e , A e ), where V e is a node set, E e is an edge set, and A e is an adjacency matrix; 步骤4，根据节点图
Figure FDA0002335757830000017
和边图Ge构建对偶图卷积网络，将t时刻的交通流量Xt输入k层对偶图卷积网络，各层输出的节点表示为
Figure FDA0002335757830000011
Figure FDA0002335757830000012
Step 4, according to the node graph
Figure FDA0002335757830000017
Construct a dual graph convolutional network with the edge graph Ge, input the traffic flow X t at time t into the k-layer dual graph convolutional network, and the output nodes of each layer are expressed as
Figure FDA0002335757830000011
Figure FDA0002335757830000012
 步骤5，将对偶图卷积网络各层输出的节点表示输入多范围注意力网络进行融合，输出融合表示Ut；Step 5, fuse the node representations output by each layer of the paired graph convolutional network to the input multi-range attention network, and the output fusion represents U t ; 步骤6，将过去T′个时刻的融合表示U(s-T′+1)：s输入节点间共享的编解码结构长短时记忆网络，输出未来T个时刻的交通流量预测值
Figure FDA0002335757830000013
Step 6: The fusion representation of the past T' moments U (sT'+1): s The long-short-term memory network of the codec structure shared between the input nodes, and output the traffic flow forecast value of the next T moments
Figure FDA0002335757830000013
 2.如权利要求1所述的基于循环注意力对偶图卷积网络的交通流量预测方法，其特征在于，步骤1中，道路网络上的线圈传感器的数量为N，
Figure FDA0002335757830000014
表示在t时刻结束时间窗口内交通路网上N个节点的交通流量，使用线性插值的方法对交通流量处理缺失值和异常值。2. The traffic flow prediction method based on the cyclic attention dual graph convolutional network as claimed in claim 1, wherein in step 1, the number of coil sensors on the road network is N,
Figure FDA0002335757830000014
Represents the traffic flow of N nodes on the traffic road network within the end time window at time t, and uses linear interpolation to deal with missing values and outliers in the traffic flow. 3.如权利要求1所述的基于循环注意力对偶图卷积网络的交通流量预测方法，其特征在于，步骤2中，构建节点图
Figure FDA0002335757830000018
的具体步骤为：3. The traffic flow prediction method based on cyclic attention dual graph convolutional network as claimed in claim 1, it is characterized in that, in step 2, construct node graph
Figure FDA0002335757830000018
The specific steps are: (a)构建节点图
Figure FDA0002335757830000023
的节点集Vn＝{v1，v2，…，vN}，其中，|Vn|＝N，节点集Vn中的一个元素对应一个道路节点；(a) Build a node graph
Figure FDA0002335757830000023
The node set V n ={v 1 , v 2 ,...,v N }, where |V n |=N, an element in the node set V n corresponds to a road node; (b)计算任意两个道路节点间的路网距离，dist(vi，vj)表示道路节点i到道路节点j的最短路网距离；(b) Calculate the road network distance between any two road nodes, dist(v i , v j ) represents the shortest network distance from road node i to road node j; (c)根据节点间的路网距离计算
Figure FDA0002335757830000024
的邻接矩阵An，其中，邻接矩阵An的计算公式为：(c) Calculated according to the road network distance between nodes
Figure FDA0002335757830000024
The adjacency matrix A n of , where the calculation formula of the adjacency matrix A n is:
Figure FDA0002335757830000021
Figure FDA0002335757830000021
 其中，σ2为所有道路节点间路网距离的方差，d为人工设置的阈值；Among them, σ 2 is the variance of the road network distance between all road nodes, and d is the manually set threshold; (d)构建节点图
Figure FDA0002335757830000025
的边集En＝{(i→j)|0≤i，j≤N，Ai，j＞0}，其中，(i→j)表示以i为头节点，以j为尾节点的边。(d) Build a node graph
Figure FDA0002335757830000025
The edge set En = { (i→j)|0≤i, j≤N, A i, j > 0}, where (i→j) represents the edge with i as the head node and j as the tail node . 4.如权利要求1所述的基于循环注意力对偶图卷积网络的交通流量预测方法，其特征在于，步骤3中，构建边图Ge的具体步骤为：4. the traffic flow prediction method based on the dual graph convolutional network of circular attention as claimed in claim 1, is characterized in that, in step 3, the concrete steps of constructing edge graph Ge are: (a)构建边图Ge的节点集Ve＝E，边图Ge中的节点对应节点图
Figure FDA0002335757830000026
中的边；(a) Construct the node set Ve = E of the edge graph Ge , and the nodes in the edge graph Ge correspond to the node graph
Figure FDA0002335757830000026
edge in; (b)针对路径：表示为(i→j)的道路节点i到道路节点j，表示为(j→k)的道路节点j到道路节点k，定义上下游连接关系为：(i→j)为(j→k)的上游边，(j→k)为(i→j)的下游边，并定义Ae中(i→j)与(j→k)的上下游连接关系的权重如下：(b) For the path: the road node i expressed as (i→j) to the road node j, the road node j expressed as (j→k) to the road node k, the upstream and downstream connection relationship is defined as: (i→j) is the upstream edge of (j→k), (j→k) is the downstream edge of (i→j), and defines the weight of the upstream and downstream connection relationship between (i→j) and (j→k) in A e as follows:
Figure FDA0002335757830000022
Figure FDA0002335757830000022
 其中，deg-(·)和deg+(·)分别表示节点的入度和出度，ε2表示节点度的方差；Among them, deg - ( ) and deg + ( ) represent the in-degree and out-degree of the node, respectively, and ε 2 represents the variance of the node degree; (c)针对路径：表示为(i→k)的道路节点i到道路节点k，表示为(j→k)的道路节点j到道路节点k，即(i→k)和(j→k)共享同一尾节点，定义(i→k)和(j→k)为竞争关系，并定义Ae中(i→k)和(j→k)的竞争关系的权重如下：(c) For paths: road node i represented as (i→k) to road node k, road node j represented as (j→k) to road node k, ie (i→k) and (j→k) Share the same tail node, define (i→k) and (j→k) as the competition relationship, and define the weight of the competition relationship between (i→k) and (j→k) in A e as follows:
Figure FDA0002335757830000031
Figure FDA0002335757830000031
 (d)构建边图Ge的边集Ee，其中，Ee中的元素为Ge的边。(d) Construct the edge set E e of the edge graph Ge , wherein the elements in E e are the edges of Ge. 5.如权利要求1所述的基于循环注意力对偶图卷积网络的交通流量预测方法，其特征在于，步骤4中，对偶图卷积网络，包含k层节点图卷积网络和k-1层边图卷积网络，同时建模节点和边的消息传递，使用对偶图卷积网络处理t时刻的交通流量
Figure FDA0002335757830000032
具体步骤为：5. The traffic flow prediction method based on the cyclic attention dual graph convolutional network as claimed in claim 1, it is characterized in that, in step 4, the dual graph convolutional network comprises k-layer node graph convolutional network and k-1 Layer-edge graph convolutional network, modeling both node and edge message passing, using dual graph convolutional network to process traffic flow at time t
Figure FDA0002335757830000032
The specific steps are: (a)构建节点-边映射矩阵
Figure FDA0002335757830000033
来表示道路节点和边的对应关系，其中，M的每一行表示一个道路节点，每一列表示一条边，M定义为：Mi，(i→j)＝Mj，(i→j)＝1，其他位置为0；(a) Build a node-edge mapping matrix
Figure FDA0002335757830000033
to represent the correspondence between road nodes and edges, where each row of M represents a road node, each column represents an edge, and M is defined as: M i, (i→j) = M j, (i→j) = 1 , other positions are 0; (b)根据节点-边映射矩阵M对节点图
Figure FDA0002335757830000036
的输入X(0)＝X进行线性变换，映射为边图Ge的输入Z(0)：(b) Pair the node graph according to the node-edge mapping matrix M
Figure FDA0002335757830000036
The input X (0) = X is linearly transformed and mapped to the input Z (0) of the edge graph Ge : Z(0)＝MTX(0)Wb (4)Z (0) = M T X (0) W b (4) 其中，Wb为可学习的映射矩阵；Among them, W b is a learnable mapping matrix; (c)将边图Ge的输入Z(0)输入k-1层边图卷积网络，输出各层的边表示：(c) Input the input Z (0) of the edge graph Ge into the k-1 layer edge graph convolution network, and output the edge representation of each layer:
Figure FDA0002335757830000034
Figure FDA0002335757830000034
 其中，★G表示图卷积操作，
Figure FDA0002335757830000035
为第l+1层边图卷积的参数，Z(l)和Z(l+1)分别表示第l层和第l+1层边图卷积网络输出的边表示；Among them, ★G represents the graph convolution operation,
Figure FDA0002335757830000035
are the parameters of the l+1 layer edge graph convolution, Z (l) and Z (l+1) represent the edge representation of the lth layer and the l+1 layer edge graph convolution network output, respectively; (d)将X(0)输入k层节点图卷积网络，输出各层的节点表示，其中，第一层节点图卷积网络不考虑边表示，后k-1层网络考虑边表示：(d) Input X (0) into the k-layer node graph convolution network, and output the node representation of each layer. Among them, the first layer node graph convolution network does not consider the edge representation, and the latter k-1 layer network considers the edge representation:
Figure FDA0002335757830000041
Figure FDA0002335757830000041
Figure FDA0002335757830000042
Figure FDA0002335757830000042
 其中，
Figure FDA0002335757830000043
为第l+1层节点图卷积的参数，[·，·]表示拼接操作，X(l)和X(l+1)分别表示第l层和第l+1层节点图卷积网络输出的节点表示；in,
Figure FDA0002335757830000043
is the parameter of the node graph convolution of the l+1 layer, [ , ] represents the splicing operation, X (l) and X (l+1) represent the output of the node graph convolution network of the lth layer and the l+1th layer respectively node representation; (e)以节点图卷积网络的输出作为对偶图卷积网络的输出，输出t时刻k层对偶图卷积网络的节点表示
Figure FDA0002335757830000044
每一层的输出表示不同邻居范围的信息，其中F为输出节点表示的维度。(e) Take the output of the node graph convolutional network as the output of the dual graph convolutional network, and output the node representation of the k-layer dual graph convolutional network at time t
Figure FDA0002335757830000044
The output of each layer represents the information of different neighbor ranges, where F is the dimension represented by the output node. 6.如权利要求1所述的基于循环注意力对偶图卷积网络的交通流量预测方法，其特征在于，步骤5中，使用注意力网络将对偶图卷积网络k层输出的节点表示进行融合的具体步骤为：6. The traffic flow prediction method based on the dual graph convolutional network of circular attention as claimed in claim 1, it is characterized in that, in step 5, use the attention network to fuse the node representation output by the k layer of the dual graph convolutional network The specific steps are: (a)对对偶图卷积网络各层输出的节点表示X(l)进行线性变换，将其映射到度量空间：(a) Perform a linear transformation on the node representation X (l) output by each layer of the dual graph convolutional network, and map it to the metric space: Q(l)＝X(l)Wa (8)Q (l) = X (l) W a (8) 其中，
Figure FDA0002335757830000045
为可学习的映射矩阵，
Figure FDA0002335757830000046
为映射到度量空间后的节点表示；in,
Figure FDA0002335757830000045
is a learnable mapping matrix,
Figure FDA0002335757830000046
is the node representation after mapping to the metric space; (b)对于第l层的节点i映射到度量空间后的表示
Figure FDA0002335757830000047
Figure FDA0002335757830000048
为Q(l)的第i行向量，计算其与可学习的全局邻居范围情境表示
Figure FDA0002335757830000049
的内积，并在层间进行SoftMax归一化，得到各层节点表示的归一化权重：(b) The representation of the node i of the lth layer after mapping to the metric space
Figure FDA0002335757830000047
Figure FDA0002335757830000048
is the ith row vector of Q (l) , and computes its contextual representation with the learnable global neighbor range
Figure FDA0002335757830000049
The inner product of , and SoftMax normalization is performed between layers to obtain the normalized weight represented by each layer node:
Figure FDA00023357578300000410
Figure FDA00023357578300000410
Figure FDA00023357578300000411
Figure FDA00023357578300000411
 其中，
Figure FDA00023357578300000412
和
Figure FDA00023357578300000413
分别表示为未归一化和归一化权重；in,
Figure FDA00023357578300000412
and
Figure FDA00023357578300000413
are denoted as unnormalized and normalized weights, respectively; (c)使用归一化权重
Figure FDA00023357578300000414
对各层单个节点表示进行加权求和，输出单个节点的融合表示
Figure FDA0002335757830000051
(c) use normalized weights
Figure FDA00023357578300000414
Weighted summation of individual node representations at each layer to output a fused representation of a single node
Figure FDA0002335757830000051
Figure FDA0002335757830000052
Figure FDA0002335757830000052
 其中，
Figure FDA0002335757830000053
为X(l)的第i行向量，代表第l层节点i的表示；in,
Figure FDA0002335757830000053
is the i-th row vector of X (l) , representing the representation of the l-th layer node i; (d)拼接t时刻N个节点的融合表示，最终得到
Figure FDA0002335757830000054
(d) Concatenate the fusion representation of N nodes at time t, and finally get
Figure FDA0002335757830000054
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