CN115456238A - Urban trip demand prediction method based on dynamic multi-view coupling graph convolution - Google Patents
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Abstract
The invention discloses a city travel demand prediction method based on convolution of a dynamic multi-view coupling graph, which comprises the steps of obtaining geographic position data, POI data and road data of each area in a city, and constructing a geographic similar graph, a functional similar graph and a road similar graph by taking each area as a node; taking unit time as an interval, acquiring travel demand data of each area in a city within each unit time, and constructing a dynamic demand similarity graph; and then, by combining a dynamic multi-view and coupling graph convolution module, modeling can be performed on complex space-time relations in the travel demand data from multiple angles, extracting time-related features from the fusion space features corresponding to each unit time by adopting a gate control cycle unit, extracting attention scores between the time-related features and external features, and performing weighted summation on the time-related features corresponding to all unit times by taking the attention scores as weights to obtain a final prediction result. The method and the device improve the accuracy of urban travel demand prediction.
Description
Technical Field
The application belongs to the technical field of data prediction, and particularly relates to a city travel demand prediction method based on dynamic multi-view coupling graph convolution.
Background
With the rapid development of the intelligent traffic system, vehicles such as taxis, network taxi appointments, buses and subways become main tools for people to go out daily, and the problems of traffic jam prediction, traffic accident prediction, abnormal taxi path detection and the like become important challenges for the construction of intelligent cities. How to improve the utilization rate of the vehicle and reduce the waiting time of passengers is a very urgent challenge. The accurate urban travel demand prediction can reduce waiting time of passengers, improve travel efficiency, help vehicle operators to reasonably pre-schedule vehicles and help traffic departments to relieve traffic jam.
Urban travel demand prediction is an important problem in an intelligent transportation system, and has great influence on traffic management, urban planning and the like. For this reason, a large number of traffic demand prediction methods have been proposed, which mainly focus on how to effectively extract temporal and spatial correlations. Early traffic demand predictions mostly used machine learning and statistical analysis methods such as autoregressive moving average ARIMA model and its variants, least squares support vector machine (LS-SVM), K-nearest neighbor, etc., which mainly studied the temporal sequence variation while ignoring the effect of spatial correlation between different regions.
Some recent deep learning efforts have shown excellent performance in processing complex spatiotemporal data, and many scholars combine Convolutional Neural Networks (CNNs) with other networks such as (recurrent neural networks RNNs, long-short term memory neural networks LSTM, gated cyclic units GRU) to capture spatiotemporal correlations. But most of them model complex traffic networks as static graphs, neglecting the importance of building a map of the area dynamically over the entire time axis.
Disclosure of Invention
The urban travel demand prediction method based on the convolution of the dynamic multi-view coupling graph overcomes the defects of the prior art and improves the accuracy of urban travel demand prediction.
In order to achieve the purpose, the technical scheme of the application is as follows:
a city travel demand prediction method based on dynamic multi-view coupling graph convolution comprises the following steps:
acquiring geographic position data, POI data and road data of each area in a city, and constructing a geographic similar graph, a functional similar graph and a road similar graph by taking each area as a node;
taking unit time as an interval, acquiring travel demand data of each region in a city within each unit time, obtaining a characteristic matrix corresponding to each unit time, and constructing a dynamic demand similarity graph;
inputting the characteristic matrix and the adjacent matrixes of the dynamic demand similar graph, the geographical similar graph, the functional similar graph and the road similar graph into corresponding coupling graph convolution modules to obtain the spatial characteristics corresponding to the similar graphs, and fusing the spatial characteristics corresponding to the similar graphs to obtain fused spatial characteristics;
and respectively extracting time correlation characteristics from the fusion space characteristics corresponding to each unit time by adopting a gate control cycle unit, extracting attention scores between the time correlation characteristics and external characteristics through an attention layer, taking the attention scores as weights, and performing weighted summation on the time correlation characteristics corresponding to all the unit times to obtain a final prediction result.
Further, the geographical similarity is represented as
Where V represents the set of nodes in the graph, E g Representing sets of edges between nodes, A g Representation diagram
Of a contiguous matrix of g Each element in the group represents whether two nodes are adjacent or not;
A g any one of the elements A g (i, j) is expressed as:
the functional similarity is shown as
Where V represents the set of nodes in the graph, E p Representing a set of edges between nodes, A p Representation diagram
Of a neighboring matrix of p Each element in the set represents POI distribution similarity between two nodes;
A p any one of the elements A p (i, j) is expressed as:
A p (i,j)＝1-JS(P i ||P j )；
wherein, P i ,
POI distribution representing region i and region j, K represents number of POI types, P i (k),P j (k) Respectively representing the number of POI with the type k in the area i and the area j;
the road similarity is shown as
Where V represents the set of nodes in the graph, E r Representing sets of edges between nodes, A r Representation diagram
Of a contiguous matrix of r Each element in the set represents the similarity of road characteristics between two nodes;
A r any one of the elements A r (i, j) is expressed as:
A r (i,j)＝1-JS(R i ||R j )；
wherein R is i ,
Indicating the road distribution of area i and area j, L indicating the number of road features, R i (l),R j (l) Respectively representing the number of roads with the characteristic l in the area i and the area j;
the dynamic demand similarity is represented as
Where V represents the set of nodes in the graph, E d Representing a set of edges between nodes, A d Representation diagram
Of a contiguous matrix of d Each element in the tree represents the similarity of travel demands between two nodes;
A d any one of the elements A d (i, j) is expressed as:
A d (i,j)＝1-JS(X i ||X j )；
wherein X i (m),X j (M) denotes the mth features of the region i and the region j, respectively, and M denotes the number of required features.
Further, the feature matrix includes departure requirements and arrival requirements of each node in unit time.
Further, the extracting of the attention score between the time-related feature and the external feature through the attention layer is formulated as follows:
α＝soft max(Re L U(UW U +EW E +b α ))；
wherein U represents time-related features corresponding to all unit times, E represents external features, and W U 、W E And b α Denotes a weight parameter, and α denotes an attention score vector.
Further, the urban trip demand prediction method based on the convolution of the dynamic multi-view coupling diagram further includes:
and converting the prediction result into a grid form through a mapping matrix of the areas and the urban grid to obtain the prediction result of each area.
According to the urban travel demand prediction method based on the dynamic multi-view coupling graph convolution, the dynamic multi-view coupling graph convolution module and the coupling graph convolution module are combined, the complex space-time relation in travel demand data can be modeled from multiple angles, and the accuracy of urban travel demand prediction can be improved.
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FIG. 1 is a flow chart of a city travel demand prediction method based on dynamic multi-view coupling diagram convolution according to the application;
FIG. 2 is a schematic diagram of a city demand prediction model structure according to the present application;
fig. 3 is a schematic diagram of a multi-view coupling diagram convolutional network structure according to the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
According to the urban trip demand prediction method based on the convolution of the dynamic multi-view coupling graph, in order to capture deep spatiotemporal correlation, a demand similarity graph is dynamically constructed at each moment, and the weight of an edge is obtained from demand data. Spatio-temporal correlations are then captured in conjunction with the GRU by fusing predefined geographical, functional and road similarity maps using graph convolution. And finally, combining external environmental factors to obtain the output of urban travel demand prediction through the attention layer and the mapping matrix.
In one embodiment, as shown in fig. 1, a city travel demand prediction method based on convolution of dynamic multi-view coupling graph includes:
s1, acquiring geographic position data, POI data and road data of each area in a city, and constructing a geographic similar graph, a functional similar graph and a road similar graph by taking each area as a node.
In the embodiment, the city is divided into a plurality of areas according to the grid, the area with rare data is eliminated, and other areas after data cleaning are selected for research. The method is based on the electronic map of the city, and the geographical position data, the POI data and the road data of each area are easily acquired, so that a geographical similar graph, a functional similar graph and a road similar graph are constructed.
The geographical similarity is shown as
Indicating the geographic relevance of the areas, i.e. whether they are geographically adjacent. Wherein V represents a set of nodes, i.e., regions, in the graph; e g Representing a set of edges between nodes; a. The g Representation diagram
Of a neighboring matrix of g Each element in (1) indicates whether two nodes are adjacent to each other.
Specifically, travel demands of two geographically adjacent regions present a certain correlation, and a graph is constructed by connecting the two geographically adjacent regions, where an edge in the graph defines a formula as follows:
the functional similarity is shown as
Representing POI distribution similarity among areas, wherein V represents a set of nodes in the graph, and the nodes are areas; e p Representing a set of edges between nodes; a. The p Representation diagram
Of a contiguous matrix of p Each element in (a) represents the POI distribution similarity between two nodes.
The travel demand of passengers is closely related to the functional area distribution of cities, and two areas with similar functions have similar demand patterns even though they are geographically distant. Since POI data may reflect the functionality of an area, the present embodiment uses POI similarity to describe the functional similarity of areas. POIs are classified into 7 categories (residential, school, entertainment, social services, cultural, transportation, and business) with similarity calculated as follows:
A p (i,j)＝1-JS(P i ||P j )；
wherein, P i ,
The POI distributions of the area i and the area j are indicated, and K indicates the number of POI types. P i (k),P j (k) Indicating the number of POIs of type k in region i and region j, respectively.
The road similarity is shown as
Representing the similarity of road characteristics between areas, such as the number of roads, the type of roads, etc. Wherein V represents a collection of nodes in the graph, a nodeNamely, the area; e r Representing a set of edges between nodes; a. The r Representation diagram
Of a neighboring matrix of r Each element in (a) represents the similarity of road characteristics between two nodes.
Road characteristics (e.g., total length of road, road type, etc.) are also highly correlated to traffic conditions within the area, and similar to the POI similarity calculation, the present embodiment uses JS divergence to calculate the road similarity between areas, and the calculation formula is as follows:
A r (i,j)＝1-JS(R i ||R j )；
wherein R is i ,
Indicating the road distribution of the area i and the area j, and L indicating the number of road features. R i (l),R j (l) Indicating the number of roads with feature i in region i and region j, respectively.
And S2, taking unit time as an interval, acquiring travel demand data of each area in the city in each unit time, obtaining a characteristic matrix corresponding to each unit time, and constructing a dynamic demand similarity graph.
In a specific embodiment, the feature matrix includes departure demands and arrival demands of each node in a unit time, that is, the travel demand data includes departure demands and arrival demands. The method and the device for calculating the travel demand data are not limited to specific contents included in the travel demand data, and statistics can be carried out according to vehicles, such as taxies or buses.
Taking a taxi as an example, screening and counting original taxi demand data to obtain departure demand and arrival demand of each region in each unit time by taking 1 hour as a unit, and taking the departure demand and the arrival demand as trip demand data. For example, there are N regions in a city, that is, there are N nodes, and in each unit time interval, each node has 2 features, which are the departure demand and arrival demand of the node at the current unit time, respectively, to form a feature matrix.
The constructed dynamic demand similarity graph is represented as
Representing the similarity of travel needs between different regions. Wherein V represents a set of nodes, i.e., regions, in the graph; e d Representing a set of edges between nodes; a. The d Representation diagram
Of the adjacent matrix.
The demand similarity degree of the two regions is evaluated based on Jensen-Shannon (JS) divergence, the JS divergence is widely applied to measure the similarity of two probability distributions, the value range is [0,1], and the smaller the JS divergence is, the higher the similarity of the two is shown. The calculation formula is as follows:
A d (i,j)＝1-JS(X i ||X j )；
wherein X i (m),X j (M) represents the mth features of zone i and zone j, respectively, and M represents the number of required features, M being equal to 2 in this embodiment, being the departure requirement and the arrival requirement, respectively.
This example E G 、E p 、E R 、E D All represent a set of edges between nodes, and the similarity between nodes is taken as the weight of the edges, thereby forming a graph structure. Each element in the adjacency matrix represents the similarity between nodes.
It should be noted that, in order to avoid that the difference between the data and the neural network parameter is too large, so that the learning rate difference between different layers is obvious, the embodiment performs normalization processing on various types of data, where a normalization formula is as follows:
where min (x) is the minimum value in the history data and max (x) is the maximum value in the history data. The data can be distributed more evenly between [0,1] by linear normalization.
And S3, inputting the feature matrix and the adjacency matrixes of the dynamic demand similar graph, the geographical similar graph, the function similar graph and the road similar graph into corresponding coupling graph convolution modules to obtain the spatial features corresponding to the similar graphs, and fusing the spatial features corresponding to the similar graphs to obtain fused spatial features.
In the embodiment, the travel demand is predicted by constructing an urban demand prediction model, and the constructed urban demand prediction model is shown in fig. 2 and comprises a multi-view coupling graph convolution network, a gating cycle unit GRU and an attention layer.
In the step, a multi-view coupling graph convolution network is adopted, and the complex space-time dependency relationship in the travel demand data is extracted by combining the dynamic demand similarity graph of each unit time with the predefined geographic similarity graph, the predefined functional similarity graph and the predefined road similarity graph.
The multi-view coupling graph convolution network comprises coupling graph convolution modules corresponding to four similar graphs respectively, as shown in FIG. 3. The propagation law of the coupling graph convolution module can be expressed as:
wherein Z (l) Denotes the input of l +1, Z (l+1) The output of the l +1 layer and the input of the l +2 layer in the network are represented, K represents that a diffusion process model is built by K steps under an undirected graph structure, A represents an adjacency matrix, and W represents trainable parameters.
The multilevel map signal obtained by CGCN is represented as:
where M represents the total number of map convolution layers, and the attention mechanism score is calculated by linear variation:
wherein W α And b α Representing weights and deviations in linear transformation, Z (m) Is Z (m) In a flattened form. Alpha is alpha (m) Is Z (m) H is the final output of CGCN.
According to the difference of the similar graphs of the input, the output h of each coupling graph convolution module is h d ,h g ,h p ,h r The dynamic demand similarity graph, the geographic similarity graph, the function similarity graph and the road similarity graph are respectively and correspondingly input. The formula is as follows:
wherein A is d ,A g ,A p ,A r Representing adjacency matrix, X representing input feature matrix, h d ,h g ,h p ,h r Representing the spatial characteristics of the output.
In this embodiment, the adjacency matrix and the feature matrix are input into the multi-view coupling graph convolution network to perform convolution operation, so as to capture the spatial correlation between the regions, and then the output of the multiple views is fused to obtain the fusion spatial feature.
Fusing the spatial features, updating the feature vectors of corresponding nodes in each graph into new vectors with corresponding multiple sizes, and fusing the edge sets in each graph in a union mode to obtain:
H＝(h d ,h g ,h p ,h r )
G d ＝(A d ,h d )
G r ＝(A r ,h r )
where H represents the new feature matrix in the fused graph and A represents the new adjacency matrix.
And S4, extracting time correlation characteristics from the fusion space characteristics corresponding to each unit time by adopting a gate control cycle unit, extracting attention scores between the time correlation characteristics and external characteristics through an attention layer, taking the attention scores as weights, and performing weighted summation on the time correlation characteristics corresponding to all the unit times to obtain a final prediction result.
The embodiment uses a Gated Round Unit (GRU) to model the dynamic time dependence of the travel demand, then combines external factors (weather events), and finally obtains the output of the urban travel demand prediction through an attention layer and a mapping matrix.
The input of each GRU unit is information of the current moment and the hidden state of the previous moment, and the output is the hidden state of the current moment and is fused with external features. The attention layer is used to capture the degree of influence of the historical time step on the target time step. And predicting the departure flow and the arrival flow respectively, wherein X is a prediction result of the departure flow or a prediction result of the arrival flow.
When using gated round robin units to model dynamic temporal correlations of travel demands, the temporal correlations are extracted using GRUs:
wherein H t Represents the output of the multi-view coupling graph convolution network at unit time t,
representing a hidden state in unit time t, d H Representing the number of hidden units in the GRU, is typically set to 64 sizes.
All GRU units share the same parameters, and the output is the hidden state of all GRU units
Represents the time-related characteristics corresponding to all the unit times, wherein,
where N represents the number of nodes.
The weather condition has a great influence on the traffic demand, the embodiment divides the weather into five grades of sunny days, cloudy days, rainy days, snowy days and foggy days, the temperature is scaled to the range of [0,1] through the minimum and maximum linear normalization, and the data of all external factors are connected into tensors which are input into the model as external features.
Attention tier by computing U and appearance E = { E = { E } 1 ,e 2 ,…,e T Attention score between (a) }, wherein
d e Time dependency is captured dynamically with temporal attention, expressed as the number of extrinsic features:
α＝soft max(Re L U(UW U +EW E +b α ))；
wherein,
is a weight parameter that can be learned by the user,
is a temporal attention score vector representing the distribution of importance of different historical time intervals over the target interval. Re L U is an activation function, and softmax is a classification function, which are well-established technologies in the field and are not described herein.
All the time-related features and the corresponding attention scores are obtained, and all the time-related features are subjected to weighted summation, so that the final prediction result obtained by time-correlation extraction can be represented as:
in a specific embodiment, the urban travel demand prediction method based on the convolution of the dynamic multi-view coupling graph further includes:
and converting the prediction result into a grid form through a mapping matrix of the areas and the urban grid to obtain the prediction result of each area.
The mapping matrix of this embodiment represents the correspondence between the area nodes and the grid, the grid is a set of all areas of the whole city, and the output is converted into a grid form:
obtaining a prediction result:
according to the method and the system, the characteristic matrix, the dynamic demand similar graph, the geographical similar graph, the function similar graph and the road similar graph in historical unit time are input into the network, and the travel demand of the next unit time is predicted, so that the scheduling of vehicles and the command of traffic can be accurately guided, and great convenience is brought to urban travel.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. The urban trip demand prediction method based on the convolution of the dynamic multi-view coupling graph is characterized by comprising the following steps of:
acquiring geographic position data, POI data and road data of each area in a city, and constructing a geographic similarity graph, a functional similarity graph and a road similarity graph by taking each area as a node;
taking unit time as an interval, acquiring travel demand data of each region in a city within each unit time, obtaining a characteristic matrix corresponding to each unit time, and constructing a dynamic demand similarity graph;
inputting the characteristic matrix and the adjacent matrixes of the dynamic demand similar graph, the geographical similar graph, the functional similar graph and the road similar graph into corresponding coupling graph convolution modules to obtain the spatial characteristics corresponding to the similar graphs, and fusing the spatial characteristics corresponding to the similar graphs to obtain fused spatial characteristics;
and extracting time correlation characteristics from the fusion space characteristics corresponding to each unit time by adopting a gate control cycle unit, extracting attention scores between the time correlation characteristics and external characteristics through an attention layer, taking the attention scores as weights, and performing weighted summation on the time correlation characteristics corresponding to all the unit times to obtain a final prediction result.
2. The dynamic multi-view coupled graph convolution-based urban travel demand prediction method according to claim 1, wherein the geographical similarity graph is represented as
Where V represents the set of nodes in the graph, E g Representing sets of edges between nodes, A g Representation diagram
Of a neighboring matrix of g Each element in the group represents whether two nodes are adjacent or not;
A g any one of the elements A g (i, j) is expressed as:
the functional similarity is shown as
Where V represents the set of nodes in the graph, E p Representing sets of edges between nodes, A p Representation diagram
Of a neighboring matrix of p Each element in the set represents POI distribution similarity between two nodes;
A p any one of the elements A p (i, j) is expressed as:
A p (i,j)＝1-JS(P i ||P j )；
wherein,
POI distribution representing area i and area j, K representing number of POI types, P i (k),P j (k) Respectively representing the number of POIs with the type k in the area i and the area j;
the road similarity is shown as
Where V represents the set of nodes in the graph, E r Representing sets of edges between nodes, A r Representation diagram
Of a neighboring matrix of r Each element in the set represents the similarity of road characteristics between two nodes;
A r any one of the elements A r (i, j) is expressed as:
A r (i,j)＝1-JS(R i ||R j )；
wherein,
indicating the road distribution of area i and area j, L indicating the number of road features, R i (l),R j (l) Respectively representing the number of roads with the characteristic l in the area i and the area j;
the dynamic demand similarity is represented as
Where V represents the set of nodes in the graph, E d Representing sets of edges between nodes, A d Representation diagram
Of a neighboring matrix of d Each element in the tree represents the similarity of travel demands between two nodes;
A d any one of the elements A d (i, j) is expressed as:
A d (i,j)＝1-JS(X i ||X j )；
wherein X i (m),X j (M) represents the mth features of the region i and the region j, respectively, and M represents the number of required features.
3. The urban travel demand prediction method based on dynamic multi-view coupling graph convolution according to claim 1, characterized in that the feature matrix comprises departure demands and arrival demands of each node in unit time.
4. The method for forecasting urban travel demand based on convolution of dynamic multi-view coupling graph according to claim 1, characterized in that the attention score between the time-related feature and the external feature is extracted through the attention layer, and is expressed by the following formula:
α＝softmax(ReLU(UW U +EW E +b α ))；
wherein U represents time-related features corresponding to all unit times, E represents external features, and W U 、W E And b α Representing the weight parameter and alpha the attention score vector.
5. The urban travel demand prediction method based on dynamic multi-view coupling graph convolution according to claim 1, wherein the urban travel demand prediction method based on dynamic multi-view coupling graph convolution further comprises:
and converting the prediction result into a grid form through a mapping matrix of the region and the city grid to obtain the prediction result of each region.
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| CN117829375B (en) * | 2024-02-29 | 2024-05-28 | 华侨大学 | Method, device, equipment and medium for predicting multi-region demand of inter-city shuttle passenger transport |
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