CN106157615B - A kind of traffic flow information management handheld terminal - Google Patents

A kind of traffic flow information management handheld terminal Download PDF

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Publication number
CN106157615B
CN106157615B CN201610522052.4A CN201610522052A CN106157615B CN 106157615 B CN106157615 B CN 106157615B CN 201610522052 A CN201610522052 A CN 201610522052A CN 106157615 B CN106157615 B CN 106157615B
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traffic flow
handheld terminal
data
module
magnitude
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CN106157615A (en
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不公告发明人
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Taizhou Longze Environmental Technology Co., Ltd.
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Taizhou Longze Environmental Technology Co Ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count

Abstract

A kind of traffic flow information of the present invention manages handheld terminal, including handheld terminal and the prediction meanss being connected with handheld terminal, 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 traffic flow information management handheld terminal
Technical field
The present invention relates to intelligent transportation fields, and in particular to a kind of traffic flow information management handheld terminal.
Background technique
The magnitude of traffic flow refers to the actual vehicle number for passing through a certain section of road in the unit time, 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 Spend lower, prediction fails to achieve satisfactory results in real time, and fail 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.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of traffic flow information management handheld terminal.
The purpose of the present invention is realized using following technical scheme：
A kind of traffic flow information manages handheld terminal, including handheld terminal and the prediction meanss being connected with handheld terminal, The handheld terminal includes：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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, the accuracy of data is increased, and makes the prediction of construction Model is more targeted；
2, related coefficient computing module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix is arranged to generate Module, predictive factor choose module and forecast model construction module, and wherein predictive factor directly affects 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, to improve pre- 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, provides more data for Accurate Prediction and supports.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the 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 schematic diagram of each module of prediction meanss of the present invention.
Fig. 2 is handheld terminal structural schematic diagram of the present invention.
Specific embodiment
The invention will be further described with the following Examples.
Embodiment 1
Referring to Fig. 1, a kind of Fig. 2, traffic flow information of the present embodiment manages handheld terminal, including handheld terminal and with it is hand-held The connected prediction meanss of terminal, the handheld terminal include：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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 to construct 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, in advance It surveys precision and improves 1.5% relative to the relevant technologies.
Embodiment 2
Referring to Fig. 1, a kind of Fig. 2, traffic flow information of the present embodiment manages handheld terminal, including handheld terminal and with it is hand-held The connected prediction meanss of terminal, the handheld terminal include：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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 to construct 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, in advance It surveys precision and improves 2% relative to the relevant technologies.
Embodiment 3
Referring to Fig. 1, a kind of Fig. 2, traffic flow information of the present embodiment manages handheld terminal, including handheld terminal and with it is hand-held The connected prediction meanss of terminal, the handheld terminal include：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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 to construct 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, a kind of Fig. 2, traffic flow information of the present embodiment manages handheld terminal, including handheld terminal and with it is hand-held The connected prediction meanss of terminal, the handheld terminal include：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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 to construct 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, a kind of Fig. 2, traffic flow information of the present embodiment manages handheld terminal, including handheld terminal and with it is hand-held The connected prediction meanss of terminal, the handheld terminal include：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal It is provided with the first aperture at upper edge that is positive and being located at first display screen, in the handheld terminal front and is located at described It is provided with the second aperture at the lower edge of first display screen, the first side of the handheld terminal is provided with third aperture, The second side of the handheld terminal is provided with the 4th aperture, wherein the first side be the handheld terminal in it is described The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of first display screen, described Two side faces are another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described in the handheld terminal Two tappings are built-in with the second loudspeaker, and the 4th tapping in the handheld terminal is built-in with the first microphone, described The third tapping in handheld terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the place Device connection is managed, the processor also connects with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively It connects, the processor is used for the current spatial appearance being presently according to the handheld terminal that the spatial attitude sensor detects State selects corresponding audio parts to carry out from first loudspeaker, the second loudspeaker, the first microphone and second microphone Audio processing.
Preferably, the handheld terminal further includes second display screen, and the second display screen is arranged in the handheld terminal Reverse side.
Preferably, in the handheld terminal reverse side and the position opposite with first aperture is provided with the 5th aperture, The handheld terminal reverse side and position opposite with second aperture is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
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 to 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 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 Column 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 to it is described to 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 Choose with the relevant predictive factor of prediction target point, and according to spatial position j and time delay τ progress matrix reconstruction selected by it, 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 using the first predictive factor and the second predictive factor as training sample come Predictable section is constructed in the prediction model of 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, for the friendship to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of apparent error, is used 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 to construct 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 explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention Matter and range.

Claims (5)

1. a kind of traffic flow information manages handheld terminal, including handheld terminal and the prediction meanss being connected with handheld terminal, institute Stating handheld terminal includes：
First display screen, wherein the front of the handheld terminal is arranged in first display screen, in the handheld terminal front And be located at the upper edge of first display screen and be provided with the first aperture, in the handheld terminal front and it is located at described first It is provided with the second aperture at the lower edge of display screen, the first side of the handheld terminal is provided with third aperture, described The second side of handheld terminal is provided with the 4th aperture, wherein the first side is in the handheld terminal with described first The one side nearest with the upper edge of first display screen in the parallel two sides in the upper edge of display screen, described second side Face is another side of the two sides in addition to the first side；
First tapping in the handheld terminal is built-in with the first loudspeaker, and described second in the handheld terminal opens The second loudspeaker is built-in at hole, the 4th tapping in the handheld terminal is built-in with the first microphone, described hand-held The third tapping in terminal is built-in with second microphone；
The handheld terminal is also built-in with spatial attitude sensor and processor, the spatial attitude sensor and the processor Connection, the processor are also connect with first loudspeaker, the second loudspeaker, the first microphone and second microphone respectively, The processor is used for the current spatial posture being presently according to the handheld terminal that the spatial attitude sensor detects, Corresponding audio parts are selected to carry out sound from first loudspeaker, the second loudspeaker, the first microphone and second microphone Frequency is handled；
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 to 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 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 postpone 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 for passing through 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 constructediWith 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) ', wherein the 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, the nearly M weeks same period and same type of historical traffic are chosen 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 To the relevant predictive factor of prediction target point, and matrix reconstruction is carried out according to spatial position j and time delay τ selected by it, 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：
Y'=[y1'(t) y2'(t) ... yq'(t)]
(10) forecast model construction module, by constructing 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 traffic flow information according to claim 1 manages handheld terminal, characterized in that the handheld terminal is also Including second display screen, the reverse side of the handheld terminal is arranged in the second display screen.
3. a kind of traffic flow information according to claim 2 manages handheld terminal, characterized in that in the handheld terminal The reverse side and position opposite with first aperture is provided with the 5th aperture, is opened in the handheld terminal reverse side and with described second The opposite position in hole is provided with the 6th aperture；
The handheld terminal is also built-in with third loudspeaker, and described in the third loudspeaker is arranged in the handheld terminal 5th tapping；
The handheld terminal is also built-in with the 4th loudspeaker, and described in the 4th loudspeaker is arranged in the handheld terminal 6th tapping.
4. a kind of traffic flow information according to claim 3 manages handheld terminal, characterized in that the data prediction The rule that the data of traffic actual conditions are not met in module, described in rejecting is：Within a data update cycle, it is set separately The threshold range of total traffic flow data in each section, if total traffic flow data in certain collected section falls in corresponding threshold It is worth in range, then shows that this group of data are reliable, retain this group of data；If total traffic flow data in certain collected section is fallen not In corresponding threshold range, then show that this group of data are unreliable, and rejected.
5. a kind of traffic flow information according to claim 4 manages handheld terminal, characterized in that the stationary test Module includes following submodule：
(1) submodule is examined, for 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, for the traffic flow to be tested to stationary test is not passed through with submodule is examined 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, for deleting the data of 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.
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