CN106128098B - A kind of multi-display apparatus that can carry out traffic flow forecasting - Google Patents

A kind of multi-display apparatus that can carry out traffic flow forecasting Download PDF

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CN106128098B
CN106128098B CN201610513353.0A CN201610513353A CN106128098B CN 106128098 B CN106128098 B CN 106128098B CN 201610513353 A CN201610513353 A CN 201610513353A CN 106128098 B CN106128098 B CN 106128098B
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traffic flow
data
module
τ
magnitude
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CN201610513353.0A
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CN106128098A (en
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不公告发明人
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山西通畅工程勘察设计咨询有限公司
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Abstract

A kind of multi-display apparatus that can carry out traffic flow forecasting of the present invention, the prediction meanss being connected including multi-display apparatus and with multi-display apparatus, the prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix generation module, predictive factor selection module and forecast model construction module.Precision of prediction of the present invention it is higher and construction prediction model it is more targeted.

Description

A kind of multi-display apparatus that can carry out traffic flow forecasting

Technical field

The present invention relates to intelligent transportation fields, and in particular to a kind of multihead display dress that can carry out traffic flow forecasting It sets.

Background technology

The magnitude of traffic flow refers to the actual vehicle number by a certain section of road in the unit interval, is the weight for describing traffic behavior Want characteristic parameter.The magnitude of traffic flow variation again be one in real time, higher-dimension, non-linear, non-stationary random process, correlative factor Variation may all influence the magnitude of traffic flow of subsequent time.In the related technology, strong about prediction meanss limitation in short-term, prediction essence To spend relatively low, prediction in real time fails to achieve satisfactory results, and fails to provide the selection of the Real-time Road of people and effectively suggest, from And traffic flow forecasting largely rests on the medium- and long-term forecasting of the magnitude of traffic flow.

Invention content

In view of the above-mentioned problems, the present invention provides a kind of multi-display apparatus that can carry out traffic flow forecasting.

The purpose of the present invention is realized using following technical scheme:

A kind of multi-display apparatus that can carry out traffic flow forecasting, including multi-display apparatus and with multihead display fill Connected prediction meanss are set, the multi-display apparatus includes:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted as Xjm M=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1', x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | τ ∈ [0, L] and ρij(τ) ' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Beneficial effects of the present invention are:

1, data categorization module and stationarity inspection module are set, increase the accuracy of data, and make the prediction of construction Model is more targeted;

2, setting related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix generate Module, predictive factor choose module and forecast model construction module, wherein predictive factor directly affect precision of prediction, related coefficient It is the index for measuring stochastic variable correlation, can helps to choose instruction of the variable closely related with future position as prediction model Practice sample, chooses multiple related coefficients as predictive factor, eliminate the subjectivity that initial predictive factor is chosen, can increase pre- Precision is surveyed, keeps forecast model construction module more stable and accurate;

3, the space correlation coefficient in related coefficient computing module reflects influence of the accessibility to prediction model of road network, Time correlation coefficient can express the time sequencing of flow sequence, reflect the causality on two sequence times, pre- to improve Survey the efficiency of predictor selection;Due to the Weekly similarity of the magnitude of traffic flow, the history phase of history correlation matrix generation module is introduced Relationship number, is used cooperatively with time correlation coefficient and space correlation coefficient, and providing more data for Accurate Prediction supports.

Description of the drawings

Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings Other attached drawings.

Fig. 1 is the connection diagram of each module of prediction meanss of the present invention.

Fig. 2 is multi-display apparatus dimensional structure diagram of the present invention.

Specific implementation mode

The invention will be further described with the following Examples.

Embodiment 1

Referring to Fig. 1, Fig. 2, a kind of multi-display apparatus that can carry out traffic flow forecasting of the present embodiment, including multi-screen are aobvious Showing device and the prediction meanss being connected with multi-display apparatus, the multi-display apparatus include:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1',x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | τ ∈ [0, L] and ρij(τ) ' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Data categorization module and stationarity inspection module is arranged in the present embodiment, increases the accuracy of data, and make construction Prediction model it is more targeted;Related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictive factor choose module and forecast model construction module, eliminate the master that initial predictive factor is chosen The property seen, can increase precision of prediction, keep forecast model construction module more stable and accurate;The present embodiment value L=8, M=3, Precision of prediction improves 1.5% relative to the relevant technologies.

Embodiment 2

Referring to Fig. 1, Fig. 2, a kind of multi-display apparatus that can carry out traffic flow forecasting of the present embodiment, including multi-screen are aobvious Showing device and the prediction meanss being connected with multi-display apparatus, the multi-display apparatus include:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1',x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | τ ∈ [0, L] and ρij(τ) ' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Data categorization module and stationarity inspection module is arranged in the present embodiment, increases the accuracy of data, and make construction Prediction model it is more targeted;Related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictive factor choose module and forecast model construction module, eliminate the master that initial predictive factor is chosen The property seen, can increase precision of prediction, keep forecast model construction module more stable and accurate;The present embodiment value L=9, M=3, Precision of prediction improves 2% relative to the relevant technologies.

Embodiment 3

Referring to Fig. 1, Fig. 2, a kind of multi-display apparatus that can carry out traffic flow forecasting of the present embodiment, including multi-screen are aobvious Showing device and the prediction meanss being connected with multi-display apparatus, the multi-display apparatus include:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1',x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | τ ∈ [0, L] and ρij(τ) ' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Data categorization module and stationarity inspection module is arranged in the present embodiment, increases the accuracy of data, and make construction Prediction model it is more targeted;Related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictive factor choose module and forecast model construction module, eliminate the master that initial predictive factor is chosen The property seen, can increase precision of prediction, keep forecast model construction module more stable and accurate;The present embodiment value L=10, M= 4, precision of prediction improves 2.6% relative to the relevant technologies.

Embodiment 4

Referring to Fig. 1, Fig. 2, a kind of multi-display apparatus that can carry out traffic flow forecasting of the present embodiment, including multi-screen are aobvious Showing device and the prediction meanss being connected with multi-display apparatus, the multi-display apparatus include:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1',x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | τ ∈ [0, L] and ρij(τ) ' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Data categorization module and stationarity inspection module is arranged in the present embodiment, increases the accuracy of data, and make construction Prediction model it is more targeted;Related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictive factor choose module and forecast model construction module, eliminate the master that initial predictive factor is chosen The property seen, can increase precision of prediction, keep forecast model construction module more stable and accurate;The present embodiment value L=11, M= 5, precision of prediction improves 3.2% relative to the relevant technologies.

Embodiment 5

Referring to Fig. 1, Fig. 2, a kind of multi-display apparatus that can carry out traffic flow forecasting of the present embodiment, including multi-screen are aobvious Showing device and the prediction meanss being connected with multi-display apparatus, the multi-display apparatus include:

First display device, first display device include an at least display screen, which shows smaller User interface, it is characterised in that:The multi-display apparatus further includes the second display device, and second display device is first aobvious with this Showing device is rotatablely connected, which includes an at least display screen, when first display device and second display fill When setting in same plane, an at least display screen for first display device and an at least display screen for second display device without Seam docking is to show larger user interface.

Preferably, the first display device includes first shell, which includes the first main body, which has One first face and one adjacent to first face first side, on the direction of the both ends of the first side along the vertical first side Protrude out the first drive-connecting shaft and the second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft it is coaxial, first drive-connecting shaft and should One first container is formed between main body, one second container is formed between second drive-connecting shaft and the main body, this is second aobvious Showing device includes second shell, which includes the second main body, which has one opposite with first face the Two faces and one adjacent to second face second side, the both ends of the second side are protruded out along the direction perpendicular to the second side Go out third drive-connecting shaft and the 4th drive-connecting shaft, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in First container, the 4th drive-connecting shaft are contained in second container.

Preferably, first shell includes that the master control key being located on first face and volume port, first drive-connecting shaft are separate It is provided with an earphone interface on the side of second drive-connecting shaft, is arranged on the side of second drive-connecting shaft far from first drive-connecting shaft There is a power port.

Preferably, characterized in that prediction meanss include sequentially connected acquisition module, data preprocessing module, data point Generic module, stationary test module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictive factor choose module and forecast model construction module:

(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe magnitude of traffic flow of corresponding each period Data and passage situation;

(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met friendship The data of logical actual conditions;

(3) data categorization module, for carrying out classification of type, the class by the traffic flow data of data prediction Type includes festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;

(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction Section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:

Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate Xx Magnitude of traffic flow sequence after time delay τ, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;

When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested Row pass through stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then wait for described Continue stationary test after examining magnitude of traffic flow sequence to carry out calm disposing;

(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence Xi With prediction section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij (w), if there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:

Space correlation coefficient ρij(w) calculation formula is:

Preferably, characterized in that prediction meanss further include:

(6) threshold setting module, for setting time delay maximum value L, temporal and spatial correlations coefficient threshold between each section T1With history correlation coefficient threshold T2

(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space phase Relationship number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ under τ in different time (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L be [8, 12], the calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:

ρij(τ) '=ρij(τ)ρij(w);

(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):

Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory it is related Sequence is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase Relationship number ρjm(t) calculation formula is:

(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2 It chooses and predicts the relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, Selection principle is:

If ρij(τ) ' > T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow composition for meeting condition is new Sequence and as the first predictive factor, be denoted as X', X'=(x1',x2',...,xp'), wherein p is the friendship for meeting condition Through-current capacity number, if L1For the maximum value of time delay in the first predictive factor, L1=max τ | and τ ∈ [0, L] | and ρij(τ)' > T1, then the first predictive factor X' can state following matrix form as:

If ρjm(t) > T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y'={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can be stated as Following matrix form:

(10) forecast model construction module, by by the first predictive factor and the second predictive factor be used as training sample come Prediction model of the predictable section of construction in the magnitude of traffic flow of subsequent time.

Wherein, in the data preprocessing module, the rule that the data of traffic actual conditions are not met described in rejecting is: In one data update cycle, the threshold range of total traffic flow data in each section is set separately, if certain collected section Total traffic flow data fall in corresponding threshold range, then show that this group of data are reliable, retain this group of data;If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that this group of data are unreliable, and picked It removes.

Wherein, the stationary test module includes following submodule:

(1) submodule is examined, for in the same type of magnitude of traffic flow sequence for observing section and prediction section Magnitude of traffic flow sequence carries out stationary test respectively;

(2) continuity check submodule is connect with submodule is examined, for not passing through the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if not meeting continuity, the continuity check submodule uses average interpolation method pair Data carry out polishing;

(3) misarrangement submodule is connect with continuity check submodule, and the data for deleting apparent error use simultaneously Average interpolation method carries out polishing to data;

(4) difference processing submodule, connection misarrangement submodule and inspection submodule, it is poor for being carried out to the data after polishing Point processing, and by the data transmission after difference processing to inspection submodule.

Data categorization module and stationarity inspection module is arranged in the present embodiment, increases the accuracy of data, and make construction Prediction model it is more targeted;Related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictive factor choose module and forecast model construction module, eliminate the master that initial predictive factor is chosen The property seen, can increase precision of prediction, keep forecast model construction module more stable and accurate;The present embodiment value L=12, M= 5, precision of prediction improves 3.5% relative to the relevant technologies.

Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention Matter and range.

Claims (5)

1. a kind of multi-display apparatus that can carry out traffic flow forecasting, including multi-display apparatus and and multi-display apparatus Connected prediction meanss, the multi-display apparatus include:
First display device, first display device include an at least display screen, which shows smaller user Interface, it is characterised in that:The multi-display apparatus further includes the second display device, which fills with first display Rotation connection is set, which includes an at least display screen, when first display device and the second display device position When same plane, an at least display screen for first display device and an at least display screen for second display device are seamless right It connects to show larger user interface;
The prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, stationary test Module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix Generation module, predictive factor choose module and forecast model construction module:
(1) acquisition module observes section S for acquiring in road network Si, prediction section SjThe traffic flow data of corresponding each period And passage situation;
(2) data preprocessing module for carrying out data prediction to the traffic flow data, and is rejected and is not met traffic reality The data of border situation;
(3) data categorization module, for carrying out classification of type, the type packet by the traffic flow data of data prediction Include festivals or holidays traffic flow data, weekend traffic flow data and working day traffic flow data;
(4) stationary test module, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction section SjMagnitude of traffic flow sequence XjStationary test is carried out respectively, examines the auto-correlation function of stationarity to be:
Wherein, XxIndicate magnitude of traffic flow sequence to be tested, νiIndicate the mean value of magnitude of traffic flow sequence to be tested, Xx+τIndicate XxWhen Between delay τ after magnitude of traffic flow sequence, νx+τFor Xx+τMean value, σ2For XxWith Xx+τBetween variance;
When auto-correlation function P (τ) can rapid decay level off to and 0 or fluctuate near 0, then the magnitude of traffic flow sequence to be tested is logical Cross stationary test;When auto-correlation function P (τ) is unable to rapid decay and levels off to 0 or fluctuate near 0, then to described to be tested Magnitude of traffic flow sequence continues stationary test after carrying out calm disposing;
(5) related coefficient computing module, for calculating the observation section S by stationary testiMagnitude of traffic flow sequence XiWith it is pre- Survey section SjMagnitude of traffic flow sequence XjTime correlation coefficient ρ at time delay τij(τ) and space correlation coefficient ρij(w), If there is N number of section in road network S, magnitude of traffic flow sequence Xi=[xi(1),xi(2),...,xi(n)], magnitude of traffic flow sequencexi(t) observation section S is indicatediIn the flow of t moment, xj(t) prediction section S is indicatedjIn t The flow at quarter, t=1,2 ... n, time correlation coefficient ρijThe calculation formula of (τ) is:
Space correlation coefficient ρij(w) calculation formula is:
(6) threshold setting module, for setting the time delay maximum value L between each section, temporal and spatial correlations coefficient threshold T1With go through History correlation coefficient threshold T2
(7) temporal and spatial correlations coefficient matrix generation module, for the time correlation coefficient ρ according to each sectionij(τ) and space correlation system Number ρij(w) each observation section S is builtiWith prediction section SjPostpone the temporal and spatial correlations coefficient matrix ρ (τ) ' under τ in different time, And calculate the temporal and spatial correlations coefficient ρ in each sectionij(τ) ', the wherein value range of i ∈ [1, N] and τ ∈ [0, L], L are [8,12], The calculation formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' is:
ρij(τ) '=ρij(τ)ρij(w);
(8) history correlation matrix generation module, for generating prediction section SjHistory correlation matrix ρ (t):
Wherein, it chooses the nearly M weeks same period and same type of historical traffic is as magnitude of traffic flow sequence XjHistory correlated series, It is denoted asM=1,2 ... the value range of M, M are [3,5], the history phase relation Number ρjm(t) calculation formula is:
(9) predictive factor chooses module, for according to the temporal and spatial correlations coefficient threshold T1With history correlation coefficient threshold T2It chooses With the prediction relevant predictive factor of target point, and matrix reconstruction is carried out according to its selected spatial position j and time delay τ, chosen Principle is:
If ρij(τ)'>T1, then section S will be observediMagnitude of traffic flow sequence XiThe middle magnitude of traffic flow for meeting condition forms new sequence And as the first predictive factor, it is denoted as X', X'=(x1',x2',...,xp'), wherein p is the magnitude of traffic flow for meeting condition Number, if L1For the maximum value of time delay in the first predictive factor,Then One predictive factor X' can state following matrix form as:
If ρjm(t)>T2, then by all history correlated series X for meeting conditionjm(t) it is used as the second predictive factor, is denoted as Y', Y' ={ y1',y2',...,yq', wherein q is the historical traffic number for the condition that meets, and the second predictive factor Y' can state following square as Formation formula:
(10) forecast model construction module is constructed by regarding the first predictive factor and the second predictive factor as training sample Prediction model of the predictable section in the magnitude of traffic flow of subsequent time.
2. a kind of multi-display apparatus that can carry out traffic flow forecasting according to claim 1, characterized in that first Display device includes first shell, which includes the first main body, which has one first face and one adjacent to this The first side in the first face, the both ends of the first side protruded out on the direction along the vertical first side the first drive-connecting shaft and Second drive-connecting shaft, first drive-connecting shaft and second drive-connecting shaft are coaxial, and one first is formed between first drive-connecting shaft and the main body Container is formed with one second container between second drive-connecting shaft and the main body, which includes second shell, The second shell includes the second main body, which has second face opposite with first face and one adjacent to second face Second side, the both ends of the second side protrude out third drive-connecting shaft and the 4th pivot along perpendicular to the direction of the second side Spindle, the third drive-connecting shaft and the 4th drive-connecting shaft are coaxial, and the third drive-connecting shaft is contained in first container, the 4th pivot Spindle is contained in second container.
3. a kind of multi-display apparatus that can carry out traffic flow forecasting according to claim 2, characterized in that first Shell includes the master control key being located on first face and volume port, on the side of first drive-connecting shaft far from second drive-connecting shaft It is provided with an earphone interface, a power port is provided on the side of second drive-connecting shaft far from first drive-connecting shaft.
4. a kind of multi-display apparatus that can carry out traffic flow forecasting according to claim 3, characterized in that described The rule that the data of traffic actual conditions are not met in data preprocessing module, described in rejecting is:In a data update cycle It is interior, the threshold range of total traffic flow data in each section is set separately, if total traffic flow data in certain collected section It falls in corresponding threshold range, then shows that this group of data are reliable, retain this group of data;If total traffic in certain collected section Data on flows is fallen not in corresponding threshold range, then shows that this group of data are unreliable, and rejected.
5. a kind of multi-display apparatus that can carry out traffic flow forecasting according to claim 4, characterized in that described Stationary test module includes following submodule:
(1) submodule is examined, for being in same type of observation section SiMagnitude of traffic flow sequence XiWith prediction section Sj's Magnitude of traffic flow sequence XjStationary test is carried out respectively;
(2) continuity check submodule is connect with submodule is examined, for not passing through the traffic flow to be tested of stationary test It measures sequence and carries out continuity check, if not meeting continuity, the continuity check submodule is using average interpolation method to data Carry out polishing;
(3) misarrangement submodule is connect with continuity check submodule, the data for deleting apparent error, while using average Interpolation method carries out polishing to data;
(4) difference processing submodule, connection misarrangement submodule and inspection submodule, for being carried out at difference to the data after polishing Reason, and by the data transmission after difference processing to inspection submodule.
CN201610513353.0A 2016-06-29 2016-06-29 A kind of multi-display apparatus that can carry out traffic flow forecasting CN106128098B (en)

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