CN105957329B - A kind of highway information intelligence system - Google Patents
A kind of highway information intelligence system Download PDFInfo
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- CN105957329B CN105957329B CN201610524331.4A CN201610524331A CN105957329B CN 105957329 B CN105957329 B CN 105957329B CN 201610524331 A CN201610524331 A CN 201610524331A CN 105957329 B CN105957329 B CN 105957329B
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- 239000011159 matrix material Substances 0.000 claims abstract description 84
- 238000012360 testing method Methods 0.000 claims abstract description 64
- 230000002123 temporal effect Effects 0.000 claims abstract description 63
- 238000010276 construction Methods 0.000 claims abstract description 29
- 238000007781 pre-processing Methods 0.000 claims abstract description 22
- 238000004364 calculation method Methods 0.000 claims description 35
- 230000001939 inductive effect Effects 0.000 claims description 35
- 230000000875 corresponding effect Effects 0.000 claims description 33
- 238000007689 inspection Methods 0.000 claims description 27
- 238000005311 autocorrelation function Methods 0.000 claims description 21
- 238000005498 polishing Methods 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 20
- 238000000034 method Methods 0.000 claims description 16
- 230000002093 peripheral effect Effects 0.000 claims description 14
- 230000005540 biological transmission Effects 0.000 claims description 8
- 230000002596 correlated effect Effects 0.000 claims description 8
- 108010014173 Factor X Proteins 0.000 claims description 7
- 230000006698 induction Effects 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 7
- 230000015572 biosynthetic process Effects 0.000 claims 1
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
- G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
- G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
-
- 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"
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- G06Q50/40—
-
- 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
-
- 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 highway information intelligence system of the present invention, including information-based intelligence system and the prediction meanss being connected with information-based intelligence system, 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
Technical field
The present invention relates to intelligent transportation fields, and in particular to a kind of highway information intelligence system.
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 highway information intelligence system.
The purpose of the present invention is realized using following technical scheme:
A kind of highway information intelligence system, including information-based intelligence system and the prediction being connected with information-based intelligence system
Device, the informationization intelligence system include:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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.
The invention has the benefit that
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 information-based intelligence system schematic diagram of the invention.
Specific embodiment
The invention will be further described with the following Examples.
Embodiment 1
Referring to Fig. 1, Fig. 2, a kind of highway information intelligence system of the present embodiment, including information-based intelligence system and and information
Change the connected prediction meanss of intelligence system, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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,
Precision of prediction improves 1.5% relative to the relevant technologies.
Embodiment 2
Referring to Fig. 1, Fig. 2, a kind of highway information intelligence system of the present embodiment, including information-based intelligence system and and information
Change the connected prediction meanss of intelligence system, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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,
Precision of prediction improves 2% relative to the relevant technologies.
Embodiment 3
Referring to Fig. 1, Fig. 2, a kind of highway information intelligence system of the present embodiment, including information-based intelligence system and and information
Change the connected prediction meanss of intelligence system, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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, Fig. 2, a kind of highway information intelligence system of the present embodiment, including information-based intelligence system and and information
Change the connected prediction meanss of intelligence system, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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, Fig. 2, a kind of highway information intelligence system of the present embodiment, including information-based intelligence system and and information
Change the connected prediction meanss of intelligence system, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed on the shell of the intelligence system
It is interior；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and
The gesture operation that will test is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for being turned according to gesture operation
The different gesture operations of the electric signal judgement identification user of change, and the gesture operation judged is led in a manner of control instruction
It crosses wireless transport module and is sent to the peripheral equipment for needing to control, execute the peripheral equipment corresponding with the gesture operation
Control function or the microprocessor directly execute control function corresponding with the gesture operation.
Preferably, the inductive pick-up is specifically set to the outer edge region of the shell.
Preferably, the inductive pick-up is capacitive induction sensor.
Preferably, prediction meanss include sequentially connected acquisition module, data preprocessing module, data categorization module, put down
Stability inspection module, related coefficient computing module, threshold setting module, temporal and spatial correlations coefficient matrix generation module, history are related
Coefficient 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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
Preferably, 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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 are as follows:
(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 are as follows:
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 of the data of traffic actual conditions is not met described in rejecting are as follows:
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 (3)
1. a kind of highway information intelligence system is filled including information-based intelligence system and the prediction being connected with information-based intelligence system
It sets, the informationization intelligence system includes:
Inductive pick-up, microprocessor, wireless transport module, above each section are placed in the shell of the intelligence system；
The inductive pick-up is arranged around the intelligence system fringe region, for detecting the gesture operation of user, and will inspection
The gesture operation measured is sent to microprocessor after being converted into electric signal；
The microprocessor connects the inductive pick-up and the wireless transport module, for what is converted according to gesture operation
The different gesture operations of electric signal judgement identification user, and the gesture operation judged is passed through into nothing in a manner of control instruction
Line transmission module is sent to the peripheral equipment for needing to control, and the peripheral equipment is made to execute control corresponding with the gesture operation
Function；
The inductive pick-up is specifically set to the outer edge region of the shell；
The inductive pick-up is capacitive induction sensor；
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 are as follows:
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) are as follows:
Space correlation coefficient ρij(w) calculation formula are as follows:
(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) ' are as follows:
Temporal and spatial correlations coefficient ρijThe calculation formula of (τ) ' are as follows:
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 asThe value range of M is [3,5], the history phase relation
Number ρjm(t) calculation formula are as follows:
(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 are as follows:
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, L1=max τ | τ ∈ [0, L] and ρij(τ)>T1, then
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 state following square as
Formation formula:
(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 highway information intelligence system according to claim 1, characterized in that the data preprocessing module
In, the rule of the data of traffic actual conditions is not met described in rejecting are as follows: within a data update cycle, each road is set separately
The threshold range of total traffic flow data of section, if total traffic flow data in certain collected section falls in corresponding threshold value model
In enclosing, 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 right
In the threshold range answered, then show that this group of data are unreliable, and rejected.
3. a kind of highway information intelligence system according to claim 2, characterized in that the stationary test module packet
Include 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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