CN109598939A - A kind of prediction of short-term traffic volume method based on multitask multiple view learning model - Google Patents
A kind of prediction of short-term traffic volume method based on multitask multiple view learning model Download PDFInfo
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Abstract
The invention discloses a kind of prediction of short-term traffic volume methods based on multitask multiple view learning model, include the following steps: step 1, individually construct Spatio-Temporal Data Model for Spatial for each road segment;Step 2 constructs Multiple Kernel Learning model;Step 3 constructs objective function using multitask multiple view feature learning model;Step 4 introduces particle swarm algorithm, and the objective function obtained to step 3 optimizes;Step 5 repeats step 1 and step 2 for any road segment to obtain input feature vector, input feature vector is brought into the objective function after optimization, realizes and carries out prediction of short-term traffic volume to any road segment.This method realizes the efficient prediction of traffic in short-term, solve the problems, such as that the global prediction ability of temporal-spatial heterogeneity and model is unable to reach equilibrium, it solves the Parametric optimization problem of model, can be widely applied to urban planning, flow of personnel investigation, auto navigation, emergency response, space-time approachability analysis and traffic pollution modeling.
Description
Technical field
The present invention relates to a kind of prediction of short-term traffic volume methods more particularly to a kind of based on multitask multiple view learning model
Prediction of short-term traffic volume method belongs to information technology service field.
Background technique
Not with sensor network, running fix, wireless telecommunications, mobile Internet, high-performance calculation and memory technology
It is disconnected to develop and popularize, there are a series of time series datas with location tags, referred to as space-time data.These space-time datas
In contain abundant and useful information and knowledge is needed by automatic mining, to expedite the emergence of the continuous of spatiotemporal data structure technology
Development.Traffic becomes the test site of space-time modeling technology, many space-time modeling Technology applications as a kind of typical space-time data
The relevant application of traffic.And space-time prediction of short-term traffic volume is as the major class in spatiotemporal data structure family, recent years by
To extensive concern.
However, under urban transportation space-time auto-correlation and the background of temporal-spatial heterogeneity, existing space-time prediction of short-term traffic volume
Model still remains shortcomings: 1) in modeling process, existing method is by each geographical unit (road segment or subregion)
As individual prediction task, and ignore holistic correlation between geographical unit, so that the reasonability of existing method has shortcoming.
2) by the constraint of temporal-spatial heterogeneity, existing space-time modeling method needs individually to model for each geographical unit, so that model exists
After having trained, the set of one group of prediction model is obtained.Also, for unbred geographical unit, need to be fitted ginseng again
Number, this allows for temporal-spatial heterogeneity and the global prediction ability of model is unable to reach equilibrium.3) existing spatio-temporal segmentation is being instructed
During white silk, the method for grid search is generallyd use to determine the hyper parameter of model.The training time of model is usually with parameter
Number is exponentially increased, and is difficult to determine optimal model structure.More than solving the problems, such as, the present invention is based on population calculations
Method integrates the efficient prediction of traffic in short-term of multitask multiple view feature learning model realization.
Summary of the invention
In order to solve shortcoming present in above-mentioned technology, the present invention provides one kind to be learnt based on multitask multiple view
The prediction of short-term traffic volume method of model.
In order to solve the above technical problems, the technical solution adopted by the present invention is that: one kind is learnt based on multitask multiple view
The prediction of short-term traffic volume method of model, includes the following steps:
Step 1 individually constructs Spatio-Temporal Data Model for Spatial for each road segment;
Spatio-Temporal Data Model for Spatial includes Spatial Dimension and time dimension, wherein Spatial Dimension indicates to influence target road segment
Spatial neighbors number, time dimension indicate influence current time historical traffic condition time window length;
Spatial neighbors are obtained using cross-correlation function to portray Spatial Dimension, meanwhile, when stacking each sequentially in time
Between the spatio-temporal state matrix that is spaced, then can be obtained three-dimensional space-time tensor, including space-time adjacent to tensor, when tensor sum null cycle when
Empty trend tensor, then to three space-time tensors of building respectively according to the history number of days of setting, training number of days and test number of days
It is divided, then history space-time tensor, training space-time tensor, test space-time tensor can be obtained for prediction of short-term traffic volume model
Building, to characterize the transportation condition of each road segment all historical junctures, and finally obtain space-time adjacent to view, when null cycle
View, space-time trend view;
Step 2 constructs Multiple Kernel Learning model;
Using STKNN model as kernel function, respectively to step 1 obtain space-time adjacent to view, space-time period view,
The result of space-time trend view predicted, then the prediction result of each view is carried out to high-rise Semantic mapping, as when
The input feature vector of empty multitask multiple view learning model;
Step 3 constructs objective function using multitask multiple view feature learning model;
The input feature vector obtained according to step 2, while the prediction of each road segment transportation condition being appointed as one
Business, so that each task has consistent characteristic dimension, using multitask multiple view feature learning model come between learning tasks
Correlation and view between consistency, while one group of sharing feature of all road Piece Selections is limited, to realize target
Function can predict the synchronous of entire road network transportation condition;
Step 4 introduces particle swarm algorithm, and the objective function obtained to step 3 optimizes;
Step 5 repeats step 1 and step 2 to obtain input feature vector, by input feature vector band for any road segment
Enter the objective function to after optimization, realizes and prediction of short-term traffic volume is carried out to any road segment.
Further, a kind of cross-correlation function ccf of stepU, l(φ) is expressed as formula I:
Wherein, φ indicates time delay, and time delay E indicates desired value operator,It is road segment u in the time
It is spaced the transportation condition of t,Indicate road segment l time interval t transportation condition,Indicate that road segment l exists
The transportation condition of time interval t+ φ, μuAnd μlRespectivelyWithMean value,For the historical traffic condition of road segment u
Time series,For the historical traffic condition time sequence of road segment l.
Further, objective function Π (W) is expressed as formula VII in step 3:
Wherein,It indicates so that the weight matrix W, y that objective function Π (W) is minimizedlIndicate road segment l
The feature vector of the true value of predicted value, XlwlIndicate the global feature matrix of road segment l and the weight vectors of road segment l
Between product,Indicate feature vector of the space-time adjacent to view of road segment l,Indicate the space-time week of road segment l
The feature vector of phase view,Indicate the feature vector of the space-time trend view of road segment l,WithTable respectively
Show weight of the space-time adjacent to view of task l, the weight of space-time period view, the weight of space-time trend view,It indicates
2- norm, SL, mFor the cross-correlation coefficient between road segment l and road segment m, SL, mValue it is bigger, show two road pieces
Section transportation condition is more related, and Laplce's regular terms of figure makes WlAnd WmIt is more like, therefore can be with autocoding space correlation
Property, WlThe weight vectors of expression task l, wmIndicate the weight vectors of expression task m;For enhancing the robustness of model;
||W||2,1Indicate the L of W2,1Norm, so that all tasks automatically select one group of shared determinant attribute, it can be by calculating W
In every a line L2The sum of norm obtains;α, β,For coupling parameter, for adjusting the inconsistent intensity of different views, γ is used
The inconsistency of mapping function, θ are used for the sparsity of controlling feature, μ L between punishment different task2Norm regularization ginseng
Number.
Further, particle swarm algorithm mainly comprises the steps that in step 4
1) the number of iterations is set, population scale initializes the position and speed of all particles;
2) training multi task model, calculates the fitness value of each particle;
3) individual is determined according to the appropriateness value of particle using the MAPE error extension of training data as fitness function
Extreme value place and group's extreme value place;
4) according to the speed and position of formula VIII and the more new particle of formula Ⅸ;
5) judge whether the termination condition for reaching particle swarm algorithm, i.e., whether reach maximum number of iterations, if not up to
Then return step 2), the fitness value of particle, more new individual extreme value and kind are recalculated according to the particle rapidity of update and position
Group's extreme value;If reached, using the position of population extreme value as the optimized parameter set of multi-task learning model.
Further, formula VIII and formula Ⅸ are as follows:
Wherein,Indicate the speed of i-th of particle in kth time iterative process,It indicates in+1 iterative process of kth
The speed of i-th of particle,Indicate the individual extreme value place of i-th of particle in kth time iterative process,Indicate kth time iteration
I-th of particle be in the position of search space in the process,Indicate that i-th of particle is in search space in+1 iterative process of kth
Position, GkIndicate the population extreme value place of kth time iteration, ω is inertia weight, c1And c2For acceleration factor, r1And r2For
[0,1] random number between.
The invention discloses a kind of prediction of short-term traffic volume methods based on multitask multiple view learning model, can help to solve
Certainly many city management problems, including urban planning, flow of personnel investigation, auto navigation, emergency response, space-time approachability analysis
It is modeled with traffic pollution, there is very extensive answering property.Method of the invention is more reasonable, overcomes existing space-time and hands in short-term
Many defects present in logical prediction model make the global prediction ability of temporal-spatial heterogeneity and model reach balanced, obtain optimal
Model structure.
Detailed description of the invention
Fig. 1 is overall step flow chart of the invention.
Fig. 2 is the flow chart of particle swarm algorithm of the present invention.
Specific embodiment
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in Figure 1, presenting overall step flow chart of the invention, specifically are as follows:
Step 1 individually constructs Spatio-Temporal Data Model for Spatial for each road segment;
Spatio-Temporal Data Model for Spatial includes Spatial Dimension and time dimension, wherein Spatial Dimension indicates to influence target road segment
Spatial neighbors number, time dimension indicate influence current time historical traffic condition time window length.Therefore, really
The key for determining Spatio-Temporal Data Model for Spatial is how to choose suitable spatial neighbors and time window length.For each road segment
The transportation condition of any time can be indicated using space-time adjacent to matrix, space-time period matrix, space-time trend matrix simultaneously,
To indicate the space-time dependence and temporal-spatial heterogeneity of transportation condition.
In terms of spatial neighbors selection, it is contemplated that road network, which has significant heterogeneous, different road segment, has difference
Spatial neighbors, therefore, the present invention for each road segment construct Spatio-Temporal Data Model for Spatial.Assuming that there are M road pieces for road network
Section, TN are total historical time intervals length, and transportation condition time series is represented by
Wherein,Indicate that dimension is the set of real numbers of M × TN,Indicate the historical traffic item of m-th road segment
Part time series,Indicate that dimension is the one-dimensional vector of TN.The spatial neighbors of road segment can be selected by cross-correlation function
It takes, it is assumed thatFor the historical traffic condition time sequence of road segment u, road segment u and l is defined in the mutual of time delay φ
Correlation function ccfU, l(φ) is shown in formula I:
Wherein, E indicates desired value operator,For road segment u time interval t transportation condition,It indicates
Road segment l time interval t transportation condition,Indicate road segment l time interval t+ φ transportation condition,
μuAnd μlRespectivelyWithMean value,For the historical traffic condition time sequence of road segment u,For road segment l
Historical traffic condition time sequence.
By definition, it can be seen that, cross-correlation function can regard the function about time delay as, so that cross-correlation function takes
Being worth maximum time-delay value is the average delay time ψ that section l (surrounding section) influences section u (prediction section)l, ψl
Meet formula II:
Wherein,For as dependent variable ccfU, l(φ) seeks the function of variable φ when maximum.
And if only if ψlIn predicted time range delta t, prediction road segment could be had an impact, mesh can be chosen as
The spatial neighbors of road segment are marked, formal definitions are as shown in formula III:
Wherein,It indicates to meet 0≤ψ of condition for alllThe road segment of≤Δ t,For the space of road segment u
The set of neighbours,Indicate the spatial neighbors number of road segment u.
In terms of time window selection, it is assumed that the collection of the time adjacent partition of time interval t is combined into TC, the collection of period distances
It is combined into TP, the collection at trend interval is combined into TQ, then TC={ t-lc, t- (lc-1) ..., t-1 }, TP={ t-lpp, t- (lp-
1) p ..., t-p }, TQ={ t-lqq, t- (lq-1) q ..., t-q }, wherein lc is time adjacent partition number, i.e.,
The lc time interval that time interval t is adjacent is taken, lc=| TC |;Lp be time cycle gap length, lp=| TP |, p is the time
Cycle length, p=1 indicate to take the transportation condition of the previous day time interval t;Lq be time trend gap length, lq=| TQ |, q
For time trend length, q=1 indicates to take the transportation condition of the last week time interval t.
Spatial neighbors and time window are being obtained, road segment u can be obtained in the space-time of time interval t adjacent to matrixSpace-time period matrixSpace-time trend matrixIt is i.e. each
The transportation condition of road segment any time utilizes three spatio-temporal state matrix synchronization representations, whereinExpression dimension is lc
The real number matrix of × ln,WithIt indicates by a similar method.It can be taken simultaneously in each time interval of guarantee
To history under the premise of time series, history cycle time series, the historical trending time sequence, stack sequentially in time
The spatio-temporal state matrix of each time interval, then can be obtained three-dimensional space-time tensor, including space-time adjacent to tensor, when null cycle
Amount and space-time trend tensor.Finally, we are to three space-time tensors of building respectively according to the history number of days of setting, training number of days
It is divided with test number of days, then history space-time tensor, training space-time tensor, test space-time tensor can be obtained for traffic in short-term
The building of prediction model to characterize the transportation condition of each road segment all historical junctures, and finally obtains the neighbouring view of space-time
Figure, space-time period view, space-time trend view.
Step 2 constructs Multiple Kernel Learning model;
On the basis of step 1, the present invention respectively models three views as kernel function using STKNN model, then
The prediction result of each view is carried out to high-rise Semantic mapping, the input as space-time multitask multiple view learning model is special
Sign.Assuming that road segment u time interval t space-time adjacent to matrix be MCU, t, by with the space-time shape in history space-time tensor
State matrix compares distance, chooses K apart from nearest spatio-temporal state matrix, to lower a period of time of the spatio-temporal state matrix of each selection
The value at quarter is integrated, and the predicted value of t+1 moment road segment u can be obtained.Therefore, key herein be how to determine away from
From function and the integration function of neighbours.
By taking space-time is adjacent to matrix as an example, since the transportation condition of road segment is indicated using spatio-temporal state matrix,
The present invention constructs weight matrix in time and Spatial Dimension respectively, calculates different moments by weight euclidean distance function
The distance between spatio-temporal state matrix.Its basic thought is: closer with predicted time in time dimension, the weight of distribution is bigger;
In Spatial Dimension, more related to the prediction space of road segment, weight is bigger.Temporal Weight matrix form such as IV institute of formula
Show:
dp=| | WTu·MCU, t·WSu-WTu·MCU, p·WSu||2
Wherein, WTuAnd WSuRespectively indicate time and the Spatial weight matrix of road segment u, wtU, iAnd wsU, iIt respectively indicates
I-th of diagonal entry of weight matrix, ti indicate the ti time interval of the neighbouring time series set of time interval t.vsi
Indicate the si spatial neighbors in the set of the spatial neighbors of road segment u.MCU, tIt is road segment u time interval t's
Space-time is adjacent to matrix, MCU, pIndicate p-th of the history of road segment u adjacent to spatio-temporal state matrix, dpIt is neighbouring for p-th of history
The distance between spatio-temporal state matrix and the spatio-temporal state matrix of t time interval,0 element (i.e. element-free) is indicated, only right
Linea angulata has element.
Finally, distributing weight, distribution using the transportation condition for the subsequent time that Gauss weighting function is each candidate neighbor
Strategy be it is closer with the spatio-temporal state matrix distance of current time interval, then distribute bigger weight.With space-time adjacent to matrix
For, weight distribution function is defined as formula V:
Wherein,Indicate predicted value of the road segment u at the t+1 moment, subscript C indicates space-time propinquity.For the transportation condition of space-time k-th of candidate neighbor subsequent time of road segment u in tensor, ωU, kFor road
The weight of k-th of candidate neighbor of segment u, dkIt is Gauss power for the distance between k-th of candidate neighbor and road segment u, a
Weight parameter.Using with space-time adjacent to tensor it is similar by the way of, when can respectively obtain null cycle tensor, road in space-time trend tensor
Prediction result of the segment u in time interval t+1WithSubscript p and q respectively indicate time-space periodicity and space-time becomes
Gesture.
Step 3 constructs objective function using multitask multiple view feature learning model;
Space-time is obtained adjacent to view, space-time period view, all training of space-time trend view and testing time by step 2
The prediction result at interval.The prediction result of three views is mapped as high-rise semantic feature by the present invention, utilizes heterogeneous space-time
Semantic feature constructs the input of objective function, while by the prediction of each road segment transportation condition as a task, so that
Each task has consistent characteristic dimension.Using multitask multiple view feature learning model come the correlation between learning tasks
Consistency between view, while one group of sharing feature of all road Piece Selections is limited, to realize entire road network
The synchronous prediction of transportation condition.
Specific embodiment is as follows:
Assuming that the space-time of road segment l is adjacent to the feature vector of viewIts
Middle NlFor the sample number of road segment l,Expression dimension is NlReal vector,Corresponding i-th of sample of road segment l
Space-time adjacent to predicted valueI.e.Similar,Indicate road
The feature vector of the space-time period view of road segment l,Indicate road segment l when
The feature vector of empty trend view,Respectively indicate i-th of sample of road segment l when
Null cycle predicted valueWith trend prediction valueThe feature vector of the true value of road segment l predicted value isThe feature vector of single view is integrated together, the whole of road segment l can be obtained
Body characteristics matrix is
Different views features the transportation condition of road segment from different levels, therefore each view is with different
Weight.Assuming that space-time is adjacent to the weight of viewThe weight of space-time period view isThe weight of space-time trend view isIn order to simplify processing, by the way of Linear Mapping, the weight contribution value of each view is obtained, i.e.,
Multiple single-views are integrated together, so that being complementary to one another between view to obtain the knowledge of enhancing, to obtain each task
The predicted value f of integrationl(Xl), i.e., shown in formula VI:
Wherein,The weight vectors of expression task l,WithRespectively indicate task l when
The empty neighbouring weight of view, the weight of space-time period view, the weight of space-time trend view.
Indicate the weight matrix of M task, XlwlIt indicates between the global feature matrix of road segment l and the weight vectors of road segment l
Product.Since different views can not be got herein to the priori knowledge of whole prediction result contribution margin, using average
Mode come the predicted value integrated.
In view of (such as propinquity, periodicity, tendency) describes same link piece to different views in terms of different
The internal characteristics of section, the prediction result of each view should be close as far as possible, i.e. consistency between holding view.Therefore,
Enhance the learning ability of single view using regularization term, introducesSo that neighbouring
The prediction result of view and the prediction result of period view are as close possible to improve the precision of prediction model, wherein α is
Coupling parameter, for adjusting the inconsistent intensity of different views, M indicates the number of road segment existing for road network,Indicate 2- norm.On the other hand, it is contemplated that road segment is organically linked together by road network, the friendship of road segment
Gating condition is influenced by its direct or indirect upstream and downstream road segment transportation condition, and the transportation condition between two road segments is got over
Correlation, travel pattern more have similitude, and therefore, we introduce Laplce's regular terms based on figure to grab road segment
The global temporal correlation of l, so that the transportation condition of the road segment with similar travel pattern has lesser deviation, such asIt is shown.In addition, wlIn i-th of element representation first of task ith feature
Importance, we limit the correlation that one group of common characteristic set of all road Piece Selections comes between characterization task,
That is all tasks can reach this target by introducing Lasso penalty term based on one group of common character subset.
Using least square loss function, the objective function Π (W) of multitask multiple view feature learning model can form turn to a prison
The learning framework superintended and directed, as shown in formula VII:
Wherein,It indicates so that the weight matrix W, y that objective function Π (W) is minimizedlIndicate road segment l
The feature vector of the true value of predicted value, XlwlIndicate the global feature matrix of road segment l and the weight vectors of road segment l
Between product,Indicate feature vector of the space-time adjacent to view of road segment l,Indicate the space-time week of road segment l
The feature vector of phase view,Indicate the feature vector of the space-time trend view of road segment l,WithTable respectively
Show weight of the space-time adjacent to view of task l, the weight of space-time period view, the weight of space-time trend view,It indicates
2- norm, SL, mFor the cross-correlation coefficient between road segment l and road segment m, SL, mValue it is bigger, show two road pieces
Section transportation condition is more related, and Laplce's regular terms of figure makes wlAnd wmIt is more like, therefore can be with autocoding space correlation
Property, wlThe weight vectors of expression task l, wmIndicate the weight vectors of expression task m;For enhancing the robustness of model;
||W||2,1Indicate the L of W2,1Norm, so that all tasks automatically select one group of shared determinant attribute, it can be by calculating W
In every a line L2The sum of norm obtains;For coupling parameter, for adjusting the inconsistent intensity of different views, γ is used
The inconsistency of mapping function, θ are used for the sparsity of controlling feature, μ L between punishment different task2Norm regularization ginseng
Number.
Since there are non-smooth L2,1Norm, therefore multitask multiple view feature learning model can be regarded as one
Non- smooth convex optimization problem, can be used FISTA, AGD scheduling algorithm to solve the problems, such as this.
Step 4 introduces particle swarm algorithm, optimizes to objective function;
Space-time multitask multiple view learning model includes multiple regularization parameters for needing to adjust, such asReasonable parameter setting will affect the precision of prediction model to a certain extent, and therefore, present invention introduces grains
The objective function of swarm optimization (PSO) Lai Zidong optimal prediction model obtains space-time multitask multiple view feature learning model
Optimized parameter set, while accelerating the training speed of model.
Assuming that there are the molecular population Z=(Z of np grain can have in solution space1, Z2..., Znp), each particle benefit
It is characterized with position, speed and fitness value.It is i-th
Position of a particle in search space represents a potential optimal solution of extremal optimization problem, respectively corresponds multitask and regards more
The regularization parameter of graphics habit model objective functionSuch as locI, αIndicate the regularization parameter α of i-th of particle
In the position of search space,It is all made of similar representation method.
Speed for i-th of particle in search space, vI, αIndicate i-th
Son regularization parameter α search space speed,It is all made of similar representation method.
For individual extreme value place, pI, αIndicate the extreme value of the regularization parameter α of i-th of particle
Position,It is all made of similar representation method.Population extreme value place is
gαIndicate extreme value place of the regularization parameter α in population,It is all made of similar representation method.It utilizes
The MAPE error extension of training data is as fitness function, and for judging the superiority and inferiority degree of particle, particle is every to be updated once just
Recalculate a fitness value.Each particle is moved in solution room with certain speed, passes through more new individual extreme value
Pbest and group extreme value Gbest carrys out the position and speed of more new individual.In iterative process each time, the position and speed of particle
It updates respectively as shown in formula VIII and formula Ⅸ:
Wherein,Indicate the speed of i-th of particle in kth time iterative process,It indicates in+1 iterative process of kth
The speed of i-th of particle,Indicate the individual extreme value place of i-th of particle in kth time iterative process,Indicate kth time iteration
I-th of particle be in the position of search space in the process,Indicate that i-th of particle is in search space in+1 iterative process of kth
Position, GkIndicate the population extreme value place of kth time iteration, ω is inertia weight, c1And c2For acceleration factor, r1And r2For
[0,1] random number between.The flow chart of particle swarm algorithm (PSO algorithm) is as shown in Fig. 2, mainly include following 5 steps:
1) the number of iterations is set, population scale initializes the position and speed of all particles;
2) training multi task model, calculates the fitness value of each particle;
3) individual is determined according to the appropriateness value of particle using the MAPE error extension of training data as fitness function
Extreme value place and group's extreme value place;
4) according to the speed and position of formula VIII and the more new particle of formula Ⅸ;
5) judge whether the termination condition for reaching PSO algorithm, i.e., whether reach maximum number of iterations, returned if not up to
Step 2) is returned, the fitness value of particle, more new individual extreme value and population pole are recalculated according to the particle rapidity of update and position
Value;If reached, using the position of population extreme value as the optimized parameter set of multi-task learning model.
Step 5 is optimized objective function by step 4, therefore.It can be applied to carry out any road segment
Prediction of short-term traffic volume repeats step 1 and step 2 to obtain input feature vector, by input feature vector band for any road segment
Enter the objective function to after optimization, realizes and prediction of short-term traffic volume is carried out to any road segment.
The invention discloses a kind of prediction of short-term traffic volume methods based on multitask multiple view learning model, for each road
Road segment indicates the transportation condition of any time using space-time adjacent to matrix, space-time period matrix, space-time trend matrix, from
And indicate the space-time dependence and temporal-spatial heterogeneity of transportation condition;Using one group of kernel function respectively obtain space-time adjacent to view, when
Null cycle view, space-time trend view prediction result, it is further that the prediction result of each view is special as high-rise semanteme
Sign;By the prediction of each road segment as a task, pass through the correlation and view between constraint learning tasks holding task
Consistency between figure allows prediction model to grab the global temporal correlation of road network and enhance it and predicts energy
Power.Meanwhile one group of all road Piece Selections shared feature is limited to realize the synchronous prediction of all road segments;It considers
Include multiple hyper parameters in space-time multitask multiple view learning model, be further introduced into particle swarm algorithm and carry out solving optimization problem,
The optimized parameter set of space-time multi-task learning model is obtained, while accelerating the training speed of model.The present invention is synchronous to consider road
Space-time dependence, temporal-spatial heterogeneity existing for road network, the global temporal correlation between prediction task and multiple views it
Between consistency, the efficient prediction of traffic in short-term is realized using multitask multiple view learning model;It is built by unified space-time
Mold framework solves temporal-spatial heterogeneity and model using the input feature vector of heterogeneous Spatio-Temporal Data Model for Spatial building prediction model
Global prediction ability is unable to reach balanced problem;Particle swarm algorithm is applied to space-time multitask multiple view model, is solved
The Parametric optimization problem of model.By the above strategy, to realize the efficient prediction of traffic in short-term.
The invention has the following advantages:
Space-time is constructed using cross-correlation function adjacent to view, space-time period view, space-time trend view to portray respectively
The transportation condition of each road segment, to portray the space-time dependence and temporal-spatial heterogeneity of transportation condition;Based on Multiple Kernel Learning
Thought, each view respectively corresponds a kernel function, to obtain the prediction result of each view.This group of prediction result is worked as
Make high-level semantic feature mapping to construct the input feature vector matrix of space-time multitask multiple view learning model;Building unification
Space-time multitask multiple view model, by increase in objective function one group of regular terms come between guarantee task correlation and
Consistency between view, so that prediction model has global prediction ability and can grab the global temporal and spatial correlations of road network
Property;Introduce particle swarm algorithm and carry out the parameter selection of optimization object function so that prediction model have optimal model structure and
Accelerate the training speed of model.The present invention can help to solve the problems, such as many city management, including urban planning, flow of personnel tune
It looks into, auto navigation, emergency response, space-time approachability analysis and traffic pollution modeling.
Above embodiment is not limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the technical staff in domain is made within the scope of technical solution of the present invention, also belong to this hair
Bright protection scope.
Claims (5)
1. a kind of prediction of short-term traffic volume method based on multitask multiple view learning model, it is characterised in that: the traffic in short-term
Prediction technique includes the following steps:
Step 1 individually constructs Spatio-Temporal Data Model for Spatial for each road segment;
Spatio-Temporal Data Model for Spatial includes Spatial Dimension and time dimension, wherein Spatial Dimension indicates to influence the sky of target road segment
Between neighbours number, time dimension indicate influence current time historical traffic condition time window length;
Spatial neighbors are obtained using cross-correlation function to portray Spatial Dimension, meanwhile, it is stacked between each time sequentially in time
Every spatio-temporal state matrix, then can be obtained three-dimensional space-time tensor, including space-time adjacent to tensor, when null cycle tensor sum space-time become
Then gesture tensor carries out three space-time tensors of building according to the history number of days of setting, training number of days and test number of days respectively
It divides, then the structure that history space-time tensor, training space-time tensor, test space-time tensor are used for prediction of short-term traffic volume model can be obtained
Build, to characterize the transportation condition of each road segment all historical junctures, and finally obtain space-time adjacent to view, when null cycle regard
Figure, space-time trend view;
Step 2 constructs Multiple Kernel Learning model;
Using STKNN model as kernel function, respectively to the space-time of step 1 acquisition adjacent to view, space-time period view, space-time
The result of trend view is predicted, then the prediction result of each view is carried out to high-rise Semantic mapping, more as space-time
The input feature vector of task multiple view learning model;
Step 3 constructs objective function using multitask multiple view feature learning model;
The input feature vector obtained according to step 2, while the prediction of each road segment transportation condition being made as a task
Obtaining each task has consistent characteristic dimension, using multitask multiple view feature learning model come the correlation between learning tasks
Consistency between property and view, while one group of sharing feature of all road Piece Selections is limited, thus function to achieve the objective energy
Synchronous prediction to entire road network transportation condition;
Step 4 introduces particle swarm algorithm, and the objective function obtained to step 3 optimizes;
Step 5 repeats step 1 and step 2 for any road segment to obtain input feature vector, input feature vector is brought into
Objective function after optimization is realized and carries out prediction of short-term traffic volume to any road segment.
2. the prediction of short-term traffic volume method according to claim 1 based on multitask multiple view learning model, feature exist
In: a kind of cross-correlation function ccf of stepU, l(φ) is expressed as formula I:
Wherein, φ indicates time delay, and time delay E indicates desired value operator,It is road segment u in time interval t
Transportation condition,Indicate road segment l time interval t transportation condition,Indicate road segment l between the time
Every the transportation condition of t+ φ, μuAnd μlRespectivelyWithMean value,For the historical traffic condition time sequence of road segment u
Column,For the historical traffic condition time sequence of road segment l.
3. the prediction of short-term traffic volume method according to claim 1 based on multitask multiple view learning model, feature exist
In: objective function П (W) is expressed as formula VII in step 3:
Wherein,It indicates so that the weight matrix W, y that objective function П (W) is minimizedlIndicate road segment l prediction
The feature vector of the true value of value, XlwlIt indicates between the global feature matrix of road segment l and the weight vectors of road segment l
Product,Indicate feature vector of the space-time adjacent to view of road segment l,Indicate road segment l when null cycle regard
The feature vector of figure,Indicate the feature vector of the space-time trend view of road segment l,WithIt respectively indicates and appoints
Be engaged in l space-time adjacent to the weight of view, the weight of space-time period view, the weight of space-time trend view,Indicate 2- model
Number, SL, mFor the cross-correlation coefficient between road segment l and road segment m, SL, mValue it is bigger, show two road segment traffic
Condition is more related, and Laplce's regular terms of figure makes wlAnd wmIt is more like, therefore can be with autocoding spatial coherence, wlTable
Show the weight vectors of task l, wmIndicate the weight vectors of expression task m;For enhancing the robustness of model;||W|
|2,1Indicate the L of W2,1Norm, so that all tasks automatically select one group of shared determinant attribute, it can be every in W by calculating
The L of a line2The sum of norm obtains;α, β,For coupling parameter, for adjusting the inconsistent intensity of different views, γ is for punishing
The inconsistency of mapping function between different task is penalized, θ is used for the sparsity of controlling feature, μ L2Norm regularization parameter.
4. the prediction of short-term traffic volume method according to claim 1 based on multitask multiple view learning model, feature exist
In: particle swarm algorithm mainly comprises the steps that in step 4
1) the number of iterations is set, population scale initializes the position and speed of all particles;
2) training multi task model, calculates the fitness value of each particle;
3) individual extreme value is determined according to the appropriateness value of particle using the MAPE error extension of training data as fitness function
Position and group's extreme value place;
4) according to the speed and position of formula VIII and the more new particle of formula Ⅸ;
5) judge whether the termination condition for reaching particle swarm algorithm, i.e., whether reach maximum number of iterations, returned if not up to
Step 2) is returned, the fitness value of particle, more new individual extreme value and population pole are recalculated according to the particle rapidity of update and position
Value;If reached, using the position of population extreme value as the optimized parameter set of multi-task learning model.
5. the prediction of short-term traffic volume method according to claim 4 based on multitask multiple view learning model, feature exist
In: the formula VIII and formula Ⅸ are as follows:
Wherein,Indicate the speed of i-th of particle in kth time iterative process,It indicates in+1 iterative process of kth i-th
The speed of particle,Indicate the individual extreme value place of i-th of particle in kth time iterative process,Indicate kth time iterative process
In i-th of particle in the position of search space,Indicate that i-th of particle is in the position of search space in+1 iterative process of kth
It sets, GkIndicate the population extreme value place of kth time iteration, ω is inertia weight, c1And c2For acceleration factor, r1And r2For [0,1]
Between random number.
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