CN110991713A - Irregular area flow prediction method based on multi-graph convolution sum GRU - Google Patents

Irregular area flow prediction method based on multi-graph convolution sum GRU Download PDF

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CN110991713A
CN110991713A CN201911148344.6A CN201911148344A CN110991713A CN 110991713 A CN110991713 A CN 110991713A CN 201911148344 A CN201911148344 A CN 201911148344A CN 110991713 A CN110991713 A CN 110991713A
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史晓颖
僧德文
吕凡顺
徐海涛
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Abstract

The invention discloses an irregular area flow prediction method based on multi-graph convolution and GRU, which comprises the following steps: dividing a region into N unconnected irregular regions; step two, performing space-time simplification on the historical track data, and calculating to obtain the inflow and outflow of all the regions at each time step; establishing a plurality of correlation diagrams among the regions, constructing a corresponding adjacent matrix, and expressing diversified spatial correlation among irregular regions; designing a multi-graph convolution neural network based on the correlation diagram among the regions, fusing diversified spatial correlation characteristics among the regions, and obtaining a multi-graph convolution fusion result; step five, based on the multi-graph convolution fusion result, capturing time correlation by adopting a GRU neural network; and sixthly, selecting a proper loss function, training to obtain a prediction model, and predicting through the prediction model to obtain the inflow and outflow of each region.

Description

Irregular area flow prediction method based on multi-graph convolution sum GRU
Technical Field
The invention relates to the field of traffic flow prediction, in particular to an irregular area flow prediction method based on multi-graph volume sum GRU.
Background
Traffic flow prediction is an important component of intelligent transportation systems. The purpose of regional flow prediction is to predict future flow values in urban areas based on given historical data, and accurate prediction can help traffic managers to control and manage flow in advance.
Regional flow prediction methods typically utilize spatial and temporal correlations between regions. Conventional regional flow prediction uses time series prediction methods such as autoregressive moving average model (ARIMA), time-varying poisson model, vector autoregressive model. The prediction accuracy is low only by considering the time-dependent correlation. With the rise of deep learning, researchers use deep learning models to predict flow. Compared with the traditional method, the model of the long Short-Term memory network LSTM (Long Short Term memory) and the gated cyclic unit GRU (gated Recurrent Unit) has better effect on the Short-Term time sequence prediction. However, they still focus on temporal associations only. To better capture the spatio-temporal correlation, researchers have proposed predicting regional traffic using Convolutional Neural Network (CNN) and residual neural network based methods, first dividing cities into grids and then predicting traffic at the grid level. However, these methods can only predict the traffic in regular areas.
Cities can be divided into meaningful areas based on road network information or administrative boundaries, which are often irregular and have complex topologies that carry more semantic information than regular grids. The grid-based prediction model cannot predict the traffic demand of the irregular area, and the usability of the prediction result is reduced.
Disclosure of Invention
In order to overcome the defects of the prior art and improve the accuracy of flow prediction in irregular areas, the invention adopts the following technical scheme:
the irregular area flow prediction method based on the multi-graph convolution sum GRU comprises the following steps:
dividing a region into N unconnected irregular regions;
step two, performing space-time simplification on the historical track data, and calculating to obtain the inflow and outflow of all the regions at each time step;
establishing a plurality of correlation diagrams among the regions, constructing a corresponding adjacent matrix, and expressing diversified spatial correlation among irregular regions;
designing a Multi-graph convolution neural Network (MGCN) based on the correlation diagram among the regions, and fusing diversified spatial correlation characteristics among the regions to obtain a Multi-graph convolution fusion result;
step five, based on the multi-graph convolution fusion result, capturing time correlation by adopting a GRU neural network;
selecting a proper loss function, training to obtain a prediction model, and predicting through the prediction model to obtain the inflow and the outflow of each region;
and step one, dividing the region into N unconnected irregular regions by adopting an irregular region division method based on the road network structure data of the region.
The second step is that the whole time period is divided into a plurality of time steps according to unit time, and then the original historical track data is mapped into the area according to the unit time based on the area division result, so as to obtain a simplified track:
TRSimp＝(startRegion，startDate，startHour，endRegion，endDate，endHour)
wherein startRegion is the ID of the departure area, startDate is the departure date, startHour is the departure unit time, endRegion is the ID of the arrival area, endDate is the arrival date, endHour is the arrival unit time, and then the entering amount of each area is obtained by aggregation simplified track calculation
Figure BDA0002282852750000021
And outflow volume
Figure BDA0002282852750000022
Wherein said
Figure BDA0002282852750000023
Represents the amount of entry of zone i at the t-th time step, said
Figure BDA0002282852750000024
Represents the outflow of zone i at the t-th time step, based finally on the
Figure BDA0002282852750000025
And said
Figure BDA0002282852750000026
Calculating to obtain the entering amount of all the areas under the time step t
Figure BDA0002282852750000027
And outflow volume
Figure BDA0002282852750000028
Establishing different association graphs to represent diversified spatial associations among irregular areas, wherein the spatial associations comprise a distance graph, a flow interaction graph and a flow association graph, the different association graphs are represented by G (V, E), and a node V is represented by G (V, E)iE.g. V represents an irregular area, an edge (V)i，vj) E encodes the degree of association between irregular regions, represented by the adjacency matrix A E RN×NRepresents;
the weight of an edge of the distance map is the distance between two regions, and the adjacent matrix element A in the distance mapdThe values of (i, j) are calculated as follows:
Figure BDA0002282852750000029
the distance between said dist (i,j) representing the distance between the center points of the regions i and j, and the adjacency matrix AdNormalized to [0, 1 ]]And based on a predefined distance threshold thresdThe A is addeddConvert to an 0/1 matrix if Ad(i，j)≤thresdIndicating that the distance between the regions i and j is very close, let A bed(i, j) ═ 1, otherwise the said Ad(i，j)＝0；
The flow interaction diagram indicates whether there is frequent bidirectional flow between the two areas by aiming at the TRSimpAggregating the data to obtain the flow value fn (i, j) from the area i to the area j and the flow value fn (j, i) from the area j to the area i in the whole analysis time period, wherein the flow interaction diagram is adjacent to the matrix element AinterThe values of (i, j) are calculated as follows:
Figure BDA00022828527500000210
the adjacency matrix AinterNormalized to [0, 1 ]]And based on a predefined traffic interaction threshold thresinterThe A is addedinterConvert to an 0/1 matrix if Ainter(i，j)≥thresinterIndicating that the interaction between the regions i and j is very strong, let A beinter(i, j) ═ 1, otherwise the said Ainter(i，j)＝0；
The flow correlation diagram indicates the time correlation of flow among the areas, the historical flow value of each area in each time step is obtained, the time sequence of the inlet flow and the outlet flow of each area in the time period is set according to the time period needing to be analyzed, and for the area i, the time sequence is expressed as:
Figure BDA00022828527500000211
the T represents the length of a time slot, correlation between the region i and the region j is calculated by adopting a Pearson correlation coefficient, and the adjacent matrix element A in the flow correlation diagramcorrThe values of (i, j) are calculated asThe following:
Figure BDA0002282852750000031
h isiAnd h is saidjSaid time sequence representing said regions i and j will listen to said adjacency matrix AcorrNormalized to [0, 1 ]]And based on a predefined flow-related threshold threscorrWill hear the statement AcorrConvert to an 0/1 matrix if Acorr(i，j)≥threscorrIndicating that the regions i and j have similar time usage patterns, let Acorr(i, j) is 1, otherwise said Acorr(i，j)＝0。
Based on a plurality of association graphs among the regions, the multi-graph convolution neural network is proposed to fully mine useful information hidden in different graphs and capture complex spatial dependence association, and the method comprises the following steps:
(1) using Graph Convolutional neural Network (GCN) model f (X)t′A) processing each graph at time step t':
Figure BDA0002282852750000032
Figure BDA0002282852750000033
Figure BDA0002282852750000034
said Xt′＝(It′，Ot′) Is the input of the model at the t' time step, representing the ingress and egress of the region,
Figure BDA0002282852750000035
is an adjacency matrix with self-flow, said INIs an identity matrix, the
Figure BDA0002282852750000036
Is a diagonal matrix in which
Figure BDA0002282852750000037
The W isd、Winter、WcorrIs a trainable weight matrix, said tanh (-) represents an activation function;
(2) adding additional attribute attr to each time stept′Coding the flow influence factors at each time step;
(3) combining a plurality of the association graphs and the additional attributes by adopting a Full Connected Layer (FCL) to fuse various spatial association characteristics among the areas;
FCL(Xt′，Ad，Ainter，Acorr，attrt′)＝Wfcl[fd(Xt′，Ad)，finter(Xt′，Ainter)，fcorr(Xt′，Acorr)，attrt′]
the W isfclFor the weight matrix, the FCL (-) is the multi-graph convolution fusion result.
The additional attribute comprises a date attribute
Figure BDA0002282852750000038
An hour attribute
Figure BDA0002282852750000039
Weather Properties
Figure BDA00022828527500000310
And temperature property
Figure BDA00022828527500000311
The above-mentioned
Figure BDA00022828527500000312
The dimension is 7 dimensions, and represents the week number of one week; the above-mentioned
Figure BDA00022828527500000313
The dimension is 24 dimensions, which represents the first hour of the day; the above-mentioned
Figure BDA00022828527500000314
Is divided into 8 categories including sunny, cloudy, light rain, medium rain, heavy rain, light snow, medium snow and heavy snow; the above-mentioned
Figure BDA00022828527500000315
Is divided into 8 levels from 10 DEG F to 90 DEG F, each 10 DEG F corresponds to one level, all the additional attributes use one-hot coding, and vectors are obtained by connection
Figure BDA00022828527500000316
Based on the multi-graph convolution fusion result FCL (·), capturing time sequence correlation in history by adopting a GRU neural network, wherein the GRU takes a hidden state at a time step t '-1 and the multi-graph convolution result as input to obtain a flow state at a time step t', and a calculation process is as follows:
ut′＝σ(Wu[FCL(·)，ht′-1]+bu)
rt′＝σ(Wr[FCL(·)，ht′-1]+br)
ct'＝tanh(Wc[FCL(·),(rt'⊙ht'-1)]+bc)
ht′＝ut′ht′-1+(1-ut′)ct′
h ist'-1Represents the hidden state at said time step t' -1, said ut'To update the gate, the amount of which memory of the last moment is saved to the current time step is defined, rt'To reset the gate, it is decided how to combine the new input information with the previous memory, ct'For the stored content at the t' time step, the ht'Is the output state at the t 'time step, the FCL (-) is at the t' time stepThe multi-graph convolution result, the Wu、Wr、WcIs a weight matrix, said bu、br、bcIs a deviation vector, the sigma is a sigmoid function, and the ⊙ is an element-by-element multiplication operation.
Step six, using Smooth L1 as the loss function, training the weight matrix and the deviation vector by adopting a back propagation and Adam optimization algorithm based on a training set to obtain the prediction model, wherein the target of the prediction model is based on input historical data [ (I)0，O0)，(I1，O1)，...，(It-1，Ot-1)]Learning a function f (-) and mapping the inlet amount and the outlet amount of each area in the historical data to obtain the inlet amount and the outlet amount of the next time step
Figure BDA0002282852750000041
So that
Figure BDA0002282852750000042
Said (I)t，Ot) And selecting a model with the minimum root mean square error as a final prediction model for the real flow value of the next time step t according to the verification set, predicting by adopting the final prediction model based on the test set, and carrying out reverse normalization on an output result to obtain a final prediction result.
The invention has the advantages and beneficial effects that:
the invention provides a novel deep learning model facing irregular area traffic prediction, which is suitable for predicting the inflow and outflow of irregular areas in cities, uses a plurality of association graphs to encode diversified spatial associations among the irregular areas, designs a multi-graph convolution neural network to fuse the associations among the irregular areas to capture spatial dependence, and then adopts a GRU neural network to capture dynamic time sequence association, thereby effectively capturing complex time-space association at the same time, improving the practicability of prediction results, and simultaneously improving the prediction accuracy of irregular area traffic flow, so that the prediction results better assist the management and control of pedestrian and vehicle flow in urban areas.
Drawings
FIG. 1 is a flow chart of irregular area traffic prediction in accordance with the present invention.
FIG. 2 is a diagram of an irregular area traffic prediction model based on multi-graph convolution and GRU in the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and the embodiments.
As shown in fig. 1, the method for predicting irregular area traffic based on multi-graph convolution and GRU of the present invention includes the following steps:
dividing a city into N unconnected irregular areas;
step two, performing space-time simplification on the historical track data, and calculating to obtain the inflow and outflow of all the regions at each time step;
establishing a plurality of correlation diagrams among the regions, constructing a corresponding adjacent matrix, and expressing diversified spatial correlation among irregular regions;
designing a multi-graph convolution neural network based on the correlation diagram among the regions, fusing diversified spatial correlation characteristics among the regions, and obtaining a multi-graph convolution fusion result;
step five, based on the multi-graph convolution fusion result, capturing time correlation by adopting a GRU neural network;
selecting a proper loss function, training to obtain a prediction model, and predicting through the prediction model to obtain the inflow and the outflow of each region;
and step one, dividing the city into N unconnected irregular areas by adopting an irregular area dividing method based on the road network structure data of the city.
Step two, firstly, dividing the whole time period into a plurality of time steps according to hours, then mapping the original historical track data into the region according to hours based on the region division result to obtain a simplified track:
TRSimp＝(startRegion，startDate，startHour，endRegion，endDate，endHour)
wherein startRegion is the ID of the departure area, startDate is the departure date, startHour is the departure hour, endRegion is the ID of the arrival area, endDate is the arrival date, endHour is the arrival hour, and then the entering amount of each area is obtained by aggregating simplified track calculation
Figure BDA0002282852750000051
And outflow volume
Figure BDA0002282852750000052
Wherein said
Figure BDA0002282852750000053
Represents the amount of entry of zone i at the t-th time step, said
Figure BDA0002282852750000054
Represents the outflow of zone i at the t-th time step, based finally on the
Figure BDA0002282852750000055
And said
Figure BDA0002282852750000056
Calculating to obtain the entering amount of all the areas under the time step t
Figure BDA0002282852750000057
And outflow volume
Figure BDA0002282852750000058
Establishing different association graphs to represent diversified spatial associations among irregular areas, wherein the spatial associations comprise a distance graph, a flow interaction graph and a flow association graph, the different association graphs are represented by G (V, E), and a node V is represented by G (V, E)iE.g. V represents an irregular area, an edge (V)i，vj) E encodes the degree of association between irregular regions, represented by the adjacency matrix A E RN×NRepresents;
the distance map is used to map the distance between the two objects,the weight of an edge is the distance between two regions, such that adjacent regions are connected by a higher-weight edge, said distance map being bounded by the elements A of the adjacency matrixdThe values of (i, j) are calculated as follows:
Figure BDA0002282852750000059
the dist (i, j) represents the distance between the center points of the regions i and j, and the adjacency matrix AdNormalized to [0, 1 ]]And based on a predefined distance threshold thresdThe A is addeddConvert to an 0/1 matrix if Ad(i，j)≤thresdIndicating that the distance between the regions i and j is very close, let A bed(i, j) ═ 1, otherwise the said Ad(i，j)＝0；
The flow interaction diagram indicates whether there is frequent bidirectional flow between the two areas by aiming at the TRSimpAggregating the data to obtain the flow value fn (i, j) from the area i to the area j and the flow value fn (j, i) from the area j to the area i in the whole analysis time period, wherein the flow interaction diagram is adjacent to the matrix element AinterThe values of (i, j) are calculated as follows:
Figure BDA00022828527500000510
the adjacency matrix AinterNormalized to [0, 1 ]]And based on a predefined traffic interaction threshold thresinterThe A is addedinterConvert to an 0/1 matrix if Ainter(i，j)≥thresinterIndicating that the interaction between the regions i and j is very strong, let A beinter(i, j) ═ 1, otherwise the said Ainter(i，j)＝0；
The flow correlation diagram indicates the time correlation of the flow among the zones, the historical flow value of each zone at each time step is obtained, and assuming that the analysis time period is 1 year, each zone has a time sequence with the length of 17520(365 by 24 by 2)The hourly ingress and egress for the year is recorded and for the region i, the time series is expressed as:
Figure BDA0002282852750000061
the T represents the time period length, the correlation between the area i and the area j is calculated by adopting a Pearson correlation coefficient, and the adjacent matrix element A in the flow correlation diagramcorrThe values of (i, j) are calculated as follows:
Figure BDA0002282852750000062
h isiAnd h is saidjRepresenting said time series of said regions i and j, said adjacency matrix AcorrNormalized to [0, 1 ]]And based on a predefined flow-related threshold threscorrWill hear the statement AcorrConvert to an 0/1 matrix if Acorr(i，j)≥threscorrIndicating that the regions i and j have similar time usage patterns, let Acorr(i, j) is 1, otherwise said Acorr(i，j)＝0。
As shown in fig. 2, the fourth step is to propose the multi-map convolutional neural network to fully mine useful information hidden in different maps and capture complex spatially dependent relationships based on a plurality of relationship maps among regions, and includes the following steps:
(1) using a graph convolution neural network model f (X)t′A) processing each graph at time step t':
Figure BDA0002282852750000063
Figure BDA0002282852750000064
Figure BDA0002282852750000065
said Xt′＝(It′，Ot′) Is the input of the model at the t' time step, representing the ingress and egress of the region,
Figure BDA0002282852750000066
is an adjacency matrix with self-flow, said INIs an identity matrix, the
Figure BDA0002282852750000067
Is a diagonal matrix in which
Figure BDA0002282852750000068
The W isd、Winter、WcorrIs a trainable weight matrix, said tanh (-) represents an activation function;
(2) adding additional attribute attr to each time stept′Coding the flow influence factors at each time step;
(3) combining a plurality of association graphs and the additional attributes by adopting a full connection layer to fuse various spatial association characteristics among the regions;
FCL(Xt′，Ad，Ainter，Acorr，attrt′)＝Wfcl[fd(Xt′，Ad)，finter(Xt′，Ainter)，fcorr(Xt′，Acorr)，attrt′]
the W isfclFor the weight matrix, the FCL (-) is the multi-graph convolution fusion result.
The additional attribute comprises a date attribute
Figure BDA0002282852750000069
An hour attribute
Figure BDA00022828527500000610
Weather Properties
Figure BDA00022828527500000611
And temperatureProperties
Figure BDA00022828527500000612
The above-mentioned
Figure BDA00022828527500000613
The dimension is 7 dimensions, and represents the week number of one week; the above-mentioned
Figure BDA00022828527500000614
The dimension is 24 dimensions, which represents the first hour of the day; the above-mentioned
Figure BDA00022828527500000615
Is divided into 8 categories including sunny, cloudy, light rain, medium rain, heavy rain, light snow, medium snow and heavy snow; the above-mentioned
Figure BDA00022828527500000616
Is divided into 8 levels from 10 DEG F to 90 DEG F, each 10 DEG F corresponds to one level, all the additional attributes use one-hot coding, and vectors are obtained by connection
Figure BDA00022828527500000617
The fifth step, based on the multi-graph convolution fusion result FCL (-), capturing the time sequence correlation in the traffic flow history by using a GRU neural network, taking the hidden state at the t '-1 time step and the multi-graph convolution result as input by the GRU, obtaining the flow state at the t' time step, and calculating the flow as follows:
ut′＝σ(Wu[FCL(·)，ht′-1]+bu)
rt′＝σ(Wr[FCL(·)，ht′-1]+br)
ct'＝tanh(Wc[FCL(·),(rt'⊙ht'-1)]+bc)
ht′＝ut′ht′-1+(1-ut′)ct′
h ist'-1Represents said time step t' -1Hidden state of u, the ut'To update the gate, the amount of which memory of the last moment is saved to the current time step is defined, rt'To reset the gate, it is decided how to combine the new input information with the previous memory, ct'For the stored content at the t' time step, the ht'Is said t'Output state at time step, FCL (-) being the multi-graph convolution result at time step t', Wu、Wr、WcIs a weight matrix, said bu、br、bcIs a deviation vector, the sigma is a sigmoid function, and the ⊙ is an element-by-element multiplication operation.
Step six, using Smooth L1 as the loss function, training the weight matrix and the deviation vector by adopting a back propagation and Adam optimization algorithm based on a training set to obtain the prediction model, wherein the target of the prediction model is based on input historical data [ (I)0，O0)，(I1，O1)，...，(It-1，Ot-1)]Learning a function f (-) and mapping the inlet amount and the outlet amount of each area in the historical data to obtain the inlet amount and the outlet amount of the next time step
Figure BDA0002282852750000071
So that
Figure BDA0002282852750000072
Said (I)t，Ot) And selecting a model with the minimum Root Mean Square Error (RMSE) as a final prediction model for the real flow value of the next time step t according to the verification set, predicting by adopting the final prediction model based on the test set, and performing reverse normalization on an output result to obtain a final prediction result.

Claims (8)

1. The irregular area flow prediction method based on the multi-graph convolution sum GRU is characterized by comprising the following steps of:
dividing a region into N unconnected irregular regions;
step two, performing space-time simplification on the historical track data, and calculating to obtain the inflow and outflow of all the regions at each time step;
establishing a plurality of correlation diagrams among the regions, constructing a corresponding adjacent matrix, and expressing diversified spatial correlation among irregular regions;
designing a multi-graph convolution neural network based on the correlation diagram among the regions, fusing diversified spatial correlation characteristics among the regions, and obtaining a multi-graph convolution fusion result;
step five, based on the multi-graph convolution fusion result, capturing time correlation by adopting a GRU neural network;
and sixthly, selecting a proper loss function, training to obtain a prediction model, and predicting through the prediction model to obtain the inflow and outflow of each region.
2. The irregular area traffic prediction method based on multi-graph convolution and GRU as claimed in claim 1, wherein in the first step, the region is divided into N unconnected irregular areas by an irregular area division method based on road network structure data of the region.
3. The irregular area traffic prediction method based on multi-map convolution and GRU as claimed in claim 1, wherein in step two, the whole time period of processing is firstly divided into a plurality of time steps according to unit time, and then based on the area division result, the original historical track data is mapped into the area according to unit time, so as to obtain a simplified track:
TRSimp＝(startRegion，startDate，startHour，endRegion，endDate，endHour)
wherein startRegion is the ID of the departure area, startDate is the departure date, startHour is the departure unit time, endRegion is the ID of the arrival area, endDate is the arrival date, endHour is the arrival unit time, and then the entering amount of each area is obtained by aggregating simplified track calculation
Figure FDA0002282852740000011
And outflow volume
Figure FDA0002282852740000012
Wherein said
Figure FDA0002282852740000013
Represents the amount of entry of zone i at the t-th time step, said
Figure FDA0002282852740000014
Represents the outflow of zone i at the t-th time step, based finally on the
Figure FDA0002282852740000015
And said
Figure FDA0002282852740000016
Calculating to obtain the entering amount of all the areas under the time step t
Figure FDA0002282852740000017
And outflow volume
Figure FDA0002282852740000018
4. The irregular area traffic prediction method based on multi-graph convolution and GRU (generalized regression analysis) of claim 3, wherein in the third step, different association graphs are established to represent diversified spatial associations between irregular areas, including distance graphs, traffic interaction graphs and traffic association graphs, the different association graphs are all represented by G ═ V (E), and node V is represented by G ═ EiE.g. V represents an irregular region, an edge (V)i，vj) E encodes the degree of association between irregular regions, represented by the adjacency matrix A E RN×NRepresents;
the weight of an edge of the distance map is the distance between two regions, and the adjacent matrix element A in the distance mapdValue of (i, j)The calculation is as follows:
Figure FDA0002282852740000019
the dist (i, j) represents the distance between the center points of the regions i and j, and the adjacency matrix AdNormalized to [0, 1 ]]And based on a predefined distance threshold thresdThe A is addeddConvert to an 0/1 matrix if Ad(i，j)≤thresdIndicating that the distance between the regions i and j is very close, let A bed(i, j) ═ 1, otherwise the said Ad(i，j)＝0；
The flow interaction diagram indicates whether there is frequent bidirectional flow between the two areas by aiming at the TRSimpAggregating the data to obtain the flow value fn (i, j) from the area i to the area j and the flow value fn (j, i) from the area j to the area i in the whole analysis time period, wherein the flow interaction diagram is adjacent to the matrix element AinterThe values of (i, j) are calculated as follows:
Figure FDA0002282852740000021
the adjacency matrix AinterNormalized to [0, 1 ]]And based on a predefined traffic interaction threshold thresinterThe A is addedinterConvert to an 0/1 matrix if Ainter(i，j)≥thresinterIndicating that the interaction between the regions i and j is very strong, let A beinter(i, j) ═ 1, otherwise the said Ainter(i，j)＝0；
The flow correlation diagram indicates the time correlation of flow among the areas, the historical flow value of each area in each time step is obtained, the time sequence of the inlet flow and the outlet flow of each area in the time period is set according to the time period needing to be analyzed, and for the area i, the time sequence is expressed as:
Figure FDA0002282852740000022
the T represents the time period length, the correlation between the area i and the area j is calculated by adopting a Pearson correlation coefficient, and the adjacent matrix element A in the flow correlation diagramcorrThe values of (i, j) are calculated as follows:
Figure FDA0002282852740000023
h isiAnd h is saidjRepresenting said time series of said regions i and j, said adjacency matrix AcorrNormalized to [0, 1 ]]And based on a predefined flow-related threshold threscorrThe A is addedcorrConvert to an 0/1 matrix if Acorr(i，j)≥threscorrIndicating that the regions i and j have similar time usage patterns, let Acorr(i, j) ═ 1, otherwise the said Acorr(i，j)＝0。
5. The irregular area traffic prediction method based on multi-graph convolution and GRU according to claim 4, wherein the fourth step proposes the multi-graph convolution neural network to fully mine useful information hidden in different graphs and capture complex spatial dependency relationships based on a plurality of the correlation graphs among areas, and comprises the following steps:
(1) using a graph convolution neural network model f (X)t′A) processing each graph at time step t':
Figure FDA0002282852740000024
Figure FDA0002282852740000025
Figure FDA0002282852740000026
said Xt′＝(It′，Ot′) Is the input of the model at the t' time step, representing the ingress and egress of the region,
Figure FDA0002282852740000027
is an adjacency matrix with self-flow, said INIs an identity matrix, the
Figure FDA0002282852740000029
Is a diagonal matrix in which
Figure FDA0002282852740000028
The W isd、Winter、WcorrIs a trainable weight matrix, said tanh (-) represents an activation function;
(2) adding additional attribute attr to each time stept′Coding the flow influence factors at each time step;
(3) combining a plurality of association graphs and the additional attributes by adopting a full connection layer to fuse various spatial association characteristics among the regions;
FCL(Xt′，Ad，Ainter，Acorr，attrt′)＝Wfcl[fd(Xt′，Ad)，finter(Xt′，Ainter)，fcorr(Xt′，Acorr)，attrt′]
the W isfclFor the weight matrix, the FCL (-) is the multi-graph convolution fusion result.
6. The method of claim 5, wherein the additional attributes comprise a date attribute
Figure FDA0002282852740000031
An hour attribute
Figure FDA0002282852740000032
Weather Properties
Figure FDA0002282852740000033
And temperature property
Figure FDA0002282852740000034
The above-mentioned
Figure FDA0002282852740000035
The dimension is 7 dimensions, and represents the week number of one week; the above-mentioned
Figure FDA0002282852740000036
The dimension is 24 dimensions, which represents the hours of the day; the above-mentioned
Figure FDA0002282852740000037
Is divided into 8 categories including sunny, cloudy, light rain, medium rain, heavy rain, light snow, medium snow and heavy snow; the above-mentioned
Figure FDA0002282852740000038
Is divided into 8 levels from 10 DEG F to 90 DEG F, each 10 DEG F corresponds to one level, all the additional attributes use one-hot coding, and vectors are obtained by connection
Figure FDA0002282852740000039
7. The irregular area traffic prediction method based on multi-map convolution and GRU according to claim 5, wherein in the fifth step, based on the multi-map convolution fusion result FCL (-) and using GRU neural network to capture time sequence correlation in history, the GRU takes hidden state at time step t '-1 and multi-map convolution result as input to obtain traffic state at time step t', and the calculation flow is as follows:
ut'＝σ(Wu[FCL(·),ht'-1]+bu)
rt'＝σ(Wr[FCL(·),ht'-1]+br)
ct'＝tanh(Wc[FCL(·),(rt'⊙ht'-1)]+bc)
ht'＝ut'ht'-1+(1-ut')ct'
h ist'-1Represents the hidden state at said time step t' -1, said ut'To update the gate, the amount of which memory of the last moment is saved to the current time step is defined, rt'To reset the gate, it is decided how to combine the new input information with the previous memory, ct'For the stored content at the t' time step, the ht'For the output state at the t 'time step, the FCL (-) is the multi-graph convolution result at the t' time step, the Wu、Wr、WcIs a weight matrix, said bu、br、bcIs a deviation vector, the sigma is a sigmoid function, and the ⊙ is an element-by-element multiplication operation.
8. The irregular area traffic prediction method based on multi-graph convolution and GRU (generalized regression analysis) of claim 7, wherein in the sixth step, the weight matrix and the deviation vector are trained by using a back propagation and Adam optimization algorithm based on a training set by using Smooth L1 as the loss function to obtain the prediction model, and the target of the prediction model is based on input historical data [ (I)0，O0)，(I1，O1)，...，(It-1，Ot-1)]Learning a function f (-) and mapping the inlet amount and the outlet amount of each area in the historical data to obtain the inlet amount and the outlet amount of the next time step
Figure FDA00022828527400000310
So that
Figure FDA00022828527400000311
Said (I)t，Ot) Is composed ofAnd selecting a model with the minimum root mean square error as a final prediction model according to the verification set for the real flow value of the next time step t, predicting by adopting the final prediction model based on the test set, and carrying out reverse normalization on the output result to obtain a final prediction result.
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