CN104064023B  A kind of Dynamic Traffic Flow Prediction method based on space time correlation  Google Patents
A kind of Dynamic Traffic Flow Prediction method based on space time correlation Download PDFInfo
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 CN104064023B CN104064023B CN201410272800.9A CN201410272800A CN104064023B CN 104064023 B CN104064023 B CN 104064023B CN 201410272800 A CN201410272800 A CN 201410272800A CN 104064023 B CN104064023 B CN 104064023B
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Abstract
The present invention relates to intelligent transportation field, particularly relate to a kind of Dynamic Traffic Flow Prediction method based on space time correlation, the method sets up spacetime matrix after traffic flow data is carried out pretreatment, by neighbour's local linear reconstructing method, spacetime matrix is trained, find one group of neighbour and the weights of prediction, it is predicted after nonnegative correction, finally by predictive value, spacetime matrix is updated.The beneficial effects of the present invention is: 1, the suitability is strong, can be suitably used for any microwave detection section；2, feasibility is strong, it is only necessary to given historical traffic flows data base, just can be trained data and predict；3, calculating speed fast, complexity is relatively low, and the calculating time is second level；4, precision of prediction is high, eliminates randomness and the undulatory property of dynamic data, improves the accuracy and reliability predicted the outcome；5, predictive efficiency is high, can realize the multistep forecasting traffic flow of multiple 5 minutes sections, can accomplish efficient the most in shortterm with forecasting traffic flow time long.
Description
Technical field
The present invention relates to intelligent transportation field, particularly relate to a kind of Dynamic Traffic Flow Prediction method based on space time correlation.
Background technology
Along with development and the economic growth of society, urban traffic jam is the most serious.For Effective Regulation traffic
Flow, optimizes the service efficiency of road, and intelligent transportation system becomes emphasis of concern, and along with the most progressively intelligence of research
Energyization, mobilism and informationization.As the important component part of intelligent transportation system, vehicle guidance system has become traffic administration
The effective way of road traffic is dredged by department, and its key technology is the prediction to road traffic condition, the most effectively utilizes history
Traffic data and real time traffic data carry out dynamic prediction to the traffic flow of future time instance road.Traffic flow forecasting mainly wraps
Include two parts: set up traffic flow histories data base and build forecast model.By historical data base is carried out pretreatment also
Data are trained drawing and predict the outcome by the algorithm utilizing forecast model to provide.In terms of the two, it was predicted that model algorithm is
The most insoluble key component, its direct relation the quality predicted the outcome, and is emphasis and the difficulty of traffic flow forecasting
Point.
At present the method to traffic flow forecasting mainly has history averaging method, neural network model, support vector regression,
Little square law, time series method etc..Said method is simple to operate, convenience of calculation, is suitable for the prediction of relatively rule data, but for
Road model is more complicated and the unstable irregular fluctuation of data stream caused of traffic flow, it was predicted that accuracy rate is relatively low.It addition, mostly
Counting method is all to predict according to temporal associativity, have ignored traffic flow relatedness on section, space so that prediction
Result is the most accurate.Therefore, in order to overcome random fluctuation and the unstable prediction effect that brings of fluctuation of dynamic data and examine
Consider the impact in space, section, need to introduce new method and traffic flow is predicted on space time correlation.
Summary of the invention
The present invention is to overcome abovementioned weak point, it is therefore intended that provide a kind of have high accuracy, reliability based on
The Dynamic Traffic Flow Prediction method of space time correlation.
The present invention is to reach abovementioned purpose by the following technical programs: a kind of Dynamic Traffic Flow Prediction based on space time correlation
Method, including:
1) pass through layout data acquisition equipment on microwave section to gather historical traffic flows data, predict and work as day interval
Time is the traffic flow data of T；
2) traffic flow data carries out pretreatment, cleans extraneous data according to arranging threshold value, and to missing data number
According to interpolation；
3) with data acquisition time as the longitudinal axis, microwave section is transverse axis, builds traffic flow data spacetime matrix；
4) carry out traffic flow data spacetime matrix training based on the reconstruct of neighbour's local linear, find the weights square of prediction
Battle array and test sample neighbour；
5) weight matrix is carried out nonnegative correction, set up the weight matrix that neighbour is the most just weighting；
6) traffic flow forecasting is carried out according to weight matrix and test sample neighbour, it was predicted that formula is:
Wherein: X_{ij}For test sample X_{i}Jth neighbour, w_{ij}For sample X_{i}The weights of jth neighbour, K ' is long for weight matrix
Degree, K ' is less than test sample neighbour's number；
7) traffic flow data of prediction is contrasted with truthful data, obtain forecast error；
8) predictive value adding traffic flow data spacetime matrix, and remove the flow value in moment the earliest, combination is formed new
Traffic flow data spacetime matrix, repeats step 4).
As preferably, described traffic flow data pretreatment comprises the following steps:
2.1) data cleansing: T minute inside lane flow data more than 300 replace with this time point data on flows in history
Meansigma methods；
2.2) missing values interpolation: for the disappearance of T minutes groove data on certain microwave section, utilize linear programming side
Method:
minx_{1}St. Ax=y (2)
Wherein, the list of matrix A shows that each microwave link flow in the most complete T minutes groove, vector y represent
There is the nonlack part of each link flow of T time groove of disappearance, x represent in A each column vector coefficient when linear reconstruct y to
Amount, solving the x obtained is sparse vector.
As preferably, described step 4) in traffic flow data spacetime matrix training based on the reconstruct of neighbour's local linear, tool
Body comprises the following steps:
4.1) with predicted time point traffic flow data as test sample, moment and historical time before this time point
Traffic flow data is training sample, based on the data of different microwave sections, calculates training sample and closes at spacetime with test sample
Euclidean distance under Lian, finds out K the minimum training sample of distance K the neighbour as test sample；Europe between two vectors
Family name is apart from as follows:
Wherein n is vector length, X_{i}For test sample, x_{i}And y_{i}It is respectively the element in vector X and Y；
4.2) weight matrix is calculated:
Set up error minimize function:
Wherein, X_{i}For test sample, N is total sample number, X_{ij}For test sample X_{i}Jth neighbour, neighbour's sum is K, w_{ij}For
Sample X_{i}The weights of jth neighbour.The local covariance matrix of sample is:
C_{jk}(i)=(X_{i}X_{ij})^{T}(X_{i}X_{ik}) (5),
It is the matrix of a K x K, it is carried out Regularization and obtains
C_{jk}(i)=C_{jk}(i)+rI (6),
Wherein I is the unit matrix of K x K, and r is regularization coefficient；Can get weights according to local covariance matrix:
As preferably, described interval time, T was 5 minutes.
The beneficial effects of the present invention is: 1, the suitability is strong, acquire the traffic flow data of multiple different microwave point, energy
It is applicable to any microwave detection section；2, feasibility is strong, it is only necessary to given historical traffic flows data base, just can carry out data
Training and prediction；3, calculating speed fast, the method complexity is relatively low, and for hundreds of thousands data, the calculating time is second level；4, pre
Survey precision is high, and the inventive method eliminates randomness and the undulatory property of dynamic data, reduces prediction data error, improves prediction
The accuracy of result and reliability；5, predictive efficiency is high, and the inventive method can realize the multistep traffic flow of multiple 5 minutes sections
Prediction, can accomplish efficient the most in shortterm with forecasting traffic flow time long, and precision of prediction kept stable.
Accompanying drawing explanation
Fig. 1 is the flow chart of steps of the present invention；
Fig. 2 is that the inventive method and the history averaging method volume forecasting result at [201395] 10:45 on the same day is with true
The fitting effect contrast schematic diagram of value；
Fig. 3 is that the inventive method and the history averaging method volume forecasting result at [201395] 10:50 on the same day is with true
The fitting effect contrast schematic diagram of value；
Fig. 4 is that the inventive method and the history averaging method volume forecasting result at [201395] 10:55 on the same day is with true
The fitting effect contrast schematic diagram of value；
Fig. 5 is that the inventive method and the history averaging method volume forecasting result at [201395] 11:00 on the same day is with true
The fitting effect contrast schematic diagram of value；
Fig. 6 be the inventive method and history averaging method in 45 minutes grooves based on predictive value on the basis of predict again
Error schematic diagram.
Detailed description of the invention
Below in conjunction with specific embodiment, the present invention is described further, but protection scope of the present invention is not limited in
This:
Embodiment 1: as it is shown in figure 1, a kind of Dynamic Traffic Flow Prediction method based on space time correlation comprises the following steps:
Step 1: gather historical traffic flows data and multiple microwave road section traffic volume flow data of prediction Time of Day point, its
Middle data are the ShortTerm Traffic Flow data of every 5 minutes.
Step 2: traffic flow microwave data pretreatment.
2.1) data cleansing.The data caused due to data acquisition unit fault or other reasons are abnormal, need it
Being carried out, cleaning rule is as follows:
Think that these data are abnormal when 5 minutes inside lane flows are more than 300, these data are replaced with in history this time
Between put the meansigma methods of data on flows.
2.2) missing values interpolation.The shortage of data caused due to factors such as communication equipment faults, needs to carry out missing values
Interpolation, interpolation rule is as follows:
For the disappearance of certain 5 minutes groove data of some section, linear programming method is utilized to train other times
This disappearance section of point and the linear relationship in other sections, lack the data of section time point further according to the coefficient reconstruct trained.
Linear programming is for solving following problems:
minx_{1}St. Ax=y (2)
Wherein, the list of matrix A shows that each link flow in 5 the most complete minutes grooves, vector y represent existence
The nonlack part of each link flow of certain time slot of disappearance, x represents each column vector coefficient vector when linear reconstruct y in A.
According to formula (2), solving the x obtained is sparse vector.
Step 3: build traffic flow data spacetime matrix.By pretreated data with the 5 minutes grooves of continuous 30 days
For the time longitudinal axis, all microwave sections are space transverse axis, build the spacetime matrix of traffic flow microwave data.
Step 4: spacetime matrix training based on the reconstruct of neighbour's local linear.
4.1) neighbour is found.With certain time point traffic flow data as test sample, before this time point the moment and
The data of historical time are training sample, based on the data of different microwave sections, calculate training sample and test sample time
Euclidean distance under null Context, finds out K the minimum training sample of distance K the neighbour as test sample.Between two vectors
Euclidean distance as follows:
Wherein n is vector length, X_{i}For test sample, x_{i}And y_{i}It is respectively the element in vector X and Y.By trying to achieve test
Sample and the distance of each time point of training sample, and be ranked up by distance size, K sample of distance minimum is tested exactly
The neighbour of sample.
4.2) weight matrix W is calculated.
Set up error minimize function:
Wherein, X_{i}For test sample, N is total sample number, X_{ij}For test sample X_{i}Jth neighbour, neighbour's sum is K, w_{ij}For
Sample X_{i}The weights of jth neighbour.The local covariance matrix of sample is:
C_{jk}(i)=(X_{i}X_{ij})^{T}(X_{i}X_{ik}) (5),
It is the matrix of a K x K, it is carried out Regularization and obtains
C_{jk}(i)=C_{jk}(i)+rI (6),
Wherein I is the unit matrix of K x K, and r is regularization coefficient；Can get weights according to local covariance matrix:
Step 5: weight matrix is carried out nonnegative correction, sets up the weight matrix that neighbour is the most just weighting.
To weight matrix W being negative item, more accurate for making to predict the outcome, W will be set to 0 for negative item, then carry out
Normalization, the weight matrix just weighted of so available training set, the length of weight matrix will become K ', K '≤K.
Step 6: forecasting traffic flow.Predictive value is calculated, it was predicted that be worth for test sample time point according to weight matrix and neighbour
The traffic flow of next 5 minutes, computing formula is as follows:
Wherein X_{ij}It is the weights corresponding in the neighbour items more than 0.
Step 7: calculating predictive value and the error of actual value:
ε_{i}=Σ  Y_{i}Z_{i}/ΣY_{i} (9)
Wherein, Z_{i}It it is the actual value in predictive value correspondence moment.
Step 8: spacetime matrix incremental update.Predictive value is added spacetime matrix, and removes the flow value in moment the earliest, group
Close and form new spacetime matrix, repeat said process, carry out multistep prediction.
The a plurality of section of microwave point is set in city, Hangzhou as acquisition target, with [201387] to [201395] even
In continuous 30 days, 10:00 to 11:00 is sampling periods every day, adds up every 5 minutes and passes through the vehicle number of microwave point on section, as friendship
Throughcurrent capacity source data.When training, half an hour and this time point and above half in 30 days in history before predicted time point
Hour data be predicted.With the data of 10:00 to 10:40 as training sample set, 201395 10:45 to 11:00 on the same day
Data be test sample collection, carry out 201395 10:45 to 11:00 on the same day according to the model of traffic flux forecast that obtains of training
Volume forecasting.
Before prediction, it is necessary first to traffic flow data is carried out pretreatment:
Data cleansing.The traffic flow data collected is carried out according to above described cleaning rule, cleans threshold
Value is 300, will be greater than the flow of 300 and replaces with the meansigma methods of this time point data on flows in history.
Missing values interpolation.To in the data collected, for the disappearance of certain 5 minutes groove data of some section, profit
Train the linear relationship in this disappearance section of other times point and other sections with linear programming method, further according to train be
The data of number reconstruct disappearance section time point.Linear programming can realize with the linprog function of matlab.
The traffic flow data that pretreatment is good is utilized to build traffic flow data spacetime matrix.With [201395] 10:35 on the same day
Traffic flow data be test sample, [201387] to [201394] in continuous 29 days 10:00 to 10:30 and
The data of [201395] 10:00 to 10:25 on the same day are training sample set, i.e. have 209 time point data, and each time point has
330 section microwave points, i.e. have 330 data, then test sample is the array of 330*1, and training sample is the spacetime of 330*209
Matrix.
Traffic flow data spacetime matrix is carried out based on the reconstruct training of neighbour's local linear.
First look for neighbour.Calculate test sample and the Euclidean distance of 209 training samples in spacetime matrix, find out K
The training sample of distance minimum is as K neighbour of test sample, and in the present invention, K takes 20.In like manner, [201395] same day 10:
When the traffic flow data of 35 and 10:40 is test sample with the testing time put front 209 time points data on flows for training
Collection.
Secondly weight matrix W is calculated.Set up error minimize function:
Wherein, wherein, X_{i}For test sample, N is total sample number, X_{ij}For test sample X_{i}Jth neighbour, neighbour sum
For K=20, w_{ij}For sample X_{i}The weights of jth neighbour.The local covariance matrix of sample is:
C_{jk}(i)=(X_{i}X_{ij})^{T}(X_{i}X_{ik}) (5),
It is the matrix of a K x K, it is carried out Regularization and obtains
C_{jk}(i)=C_{jk}(i)+rI (6),
Wherein I is the unit matrix of K x K, and r is regularization coefficient, and in the present invention, r value is that the mark of C is multiplied by 0.001, asks mark to transport
The trace function calculating available Matlab is tried to achieve；Can get weights according to local covariance matrix:
Step 5: weight matrix is carried out nonnegative correction, sets up the weight matrix that neighbour is the most just weighting:
Each weight, after nonnegative correction, meets w_{j}＞ ＞ 0 andWherein k ' is that nonnegative is revised just
Weights number.
Predictive value is calculated, it was predicted that be worth for test sample according to the revised weight matrix of nonnegative and 20 test sample neighbours
The time point traffic flow of next 5 minutes, i.e. with the training result of [201395] 10:30 on the same day predict [201395] when
The traffic flow of it 10:35.Computing formula is as follows:
Wherein X_{ij}It is the weights corresponding in the neighbour items more than 0.
Calculate predictive value and the error of actual value: ε_{i}=Σ  Y_{i}Z_{i}/ΣY_{i} (9)
Wherein, Z_{i}It it is the actual value in predictive value correspondence moment.
After completing abovementioned prediction, in order to reduce data random error, increase forecasting accuracy, to traffic flow data spacetime square
Battle array increment is updated.By the predictive value of [201395] 10:35 on the same day and this moment traffic flow data addition in first 29 days
Spacetime matrix, and the flow value of 10:00 in removing 30 days, combination forms new spacetime matrix, repeats said process, it was predicted that
The traffic flow of [201395] 10:40 on the same day, the most repeatedly, carries out multistep prediction.
As shown in Figures 2 to 5, the flow of prediction [201395] same day 10:45,10:50,10:55,11:00 it is respectively
Predict the outcome the fitting effect contrast schematic diagram with actual value, in figure, on fitting result chart 330 section microwave points is divided into 3
Part, (a) figure is the volume forecasting fitting result chart of front 1 to No. 110 microwaves point, and (b) figure is the plan of 111 to No. 220 microwave points
Closing design sketch, (c) figure is the fitting result chart of 221 to No. 330 microwave points.It will be seen that it is bigger for traffic flow fluctuation
Section, the inventive method can obtain predicting the outcome more accurately.
As shown in Figure 6, the inventive method is compared with history averaging method, show that history averaging method precision of prediction is
78%, the inventive method precision of prediction reaches 85%, substantially increases the accuracy of prediction, meets the precision of traffic flow forecasting
Demand.It addition, for the multistep prediction on fundamentals of forecasting, the inventive method precision of prediction is maintained at about 83%, it was predicted that precision
The highest with stability.
It is the specific embodiment of the present invention and the knowwhy used described in Yi Shang, if conception under this invention institute
Make change, function produced by it still without departing from description and accompanying drawing contained spiritual time, must belong to the present invention's
Protection domain.
Claims (4)
1. a Dynamic Traffic Flow Prediction method based on space time correlation, it is characterised in that including:
1) pass through layout data acquisition equipment on microwave section to gather historical traffic flows data, predict interval time on the same day
Traffic flow data for T；
2) traffic flow data carries out pretreatment, cleans extraneous data according to arranging threshold value, and missing data is carried out data benefit
Insert；
3) with data acquisition time as the longitudinal axis, microwave section is transverse axis, builds traffic flow data spacetime matrix；
4) traffic flow data spacetime matrix training based on the reconstruct of neighbour's local linear is carried out, by calculating the Euclidean of spacetime matrix
Distance finds test sample neighbour, minimizes error function by foundation and finds the weight matrix of prediction；
5) weight matrix is carried out nonnegative correction, set up the weight matrix that neighbour is the most just weighting；
6) traffic flow forecasting is carried out according to weight matrix and test sample neighbour, it was predicted that formula is:
Wherein: X_{ij}For test sample X_{i}Jth neighbour, w_{ij}For sample X_{i}The weights of jth neighbour, K ' is long for weight matrix
Degree, K ' is less than test sample neighbour's number；
7) traffic flow data of prediction is contrasted with truthful data, obtain forecast error；
8) predictive value adding traffic flow data spacetime matrix, and remove the flow value in moment the earliest, combination forms new traffic
Flow data spacetime matrix, repeats step 4).
A kind of Dynamic Traffic Flow Prediction method based on space time correlation the most according to claim 1, it is characterised in that described
Traffic flow data pretreatment comprises the following steps:
2.1) data cleansing: T minute inside lane flow data more than 300 replace with the flat of this time point data on flows in history
Average；
2.2) missing values interpolation: for the disappearance of T minutes groove data on certain microwave section, utilizes linear programming method:
minx_{1}St. Ax=y (2)
Wherein, the list of matrix A shows that each microwave link flow in the most complete T minutes groove, vector y represent existence
The nonlack part of each link flow of T time groove of disappearance, x represents each column vector coefficient vector when linear reconstruct y in A,
Solving the x obtained is sparse vector.
A kind of Dynamic Traffic Flow Prediction method based on space time correlation the most according to claim 1, it is characterised in that described
Step 4) in traffic flow data spacetime matrix training based on the reconstruct of neighbour's local linear, specifically include following steps:
4.1) with predicted time point traffic flow data as test sample, moment and the traffic of historical time before this time point
Flow data is training sample, based on the data of different microwave sections, calculates training sample and test sample under space time correlation
Euclidean distance, find out K the minimum training sample of distance K the neighbour as test sample；Euclidean between two vectors away from
From as follows:
Wherein n is vector length, X_{i}For test sample, x_{i}And y_{i}It is respectively the element in vector X and Y；
4.2) weight matrix is calculated:
Set up error minimize function:
Wherein, X_{i}For test sample, N is total sample number, X_{ij}For test sample X_{i}Jth neighbour, neighbour's sum is K, w_{ij}For
Sample X_{i}The weights of jth neighbour；The local covariance matrix of sample is:
C_{jk}(i)=(X_{i}X_{ij})T(X_{i}X_{ik}) (5),
It is the matrix of a KxK, it is carried out Regularization and obtains
C_{jk}(i)=C_{jk}(i)+rI (6),
Wherein I is the unit matrix of KxK, and r is regularization coefficient；Can get weights according to local covariance matrix:
Wherein, w_{ij}For sample X_{i}The weights of jth neighbour, K is neighbour's sum, and k represents the kth neighbour in K neighbour.
A kind of Dynamic Traffic Flow Prediction method based on space time correlation the most according to claim 1, it is characterised in that described
Interval time, T was 5 minutes.
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