CN106097712A - A kind of traffic flow optimization guides system - Google Patents

Abstract

One traffic flow optimization of the present invention guides system, including guiding system and the prediction means that is connected with guiding system, described prediction means includes that the acquisition module being sequentially connected with, data preprocessing module, data categorization module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix generation module, predictor choose module and forecast model construction module.Higher and structure the forecast model of precision of prediction of the present invention is more targeted.

Description

A kind of traffic flow optimization guides system

Technical field

The present invention relates to intelligent transportation field, be specifically related to a kind of traffic flow optimization and guide system.

Background technology

Traffic flow passes through the actual vehicle number of a certain section of road in referring to the unit interval, be the weight describing traffic behavior Want characteristic parameter.The change of traffic flow is again real-time, higher-dimension, non-linear a, stochastic process for non-stationary, correlative factor Change all may affect the traffic flow of subsequent time.In correlation technique, strong about prediction means limitation in short-term, it was predicted that essence Spending relatively low, real-time estimate fails to achieve satisfactory results, and fails the Real-time Road to people and selects to provide effectively suggestion, from And traffic flow forecasting major part rests on the medium-and long-term forecasting of traffic flow.

Summary of the invention

For the problems referred to above, the present invention provides a kind of traffic flow optimization to guide system.

The purpose of the present invention realizes by the following technical solutions:

A kind of traffic flow optimization guides system, including guiding system and the prediction means that is connected with guiding system, described in draw Guiding systems, for guiding the vehicle in multi-route, receives vehicle destination call data and by described vehicle destination call data The benchmark being allocated as vehicle route data；Described guiding system includes: guidance information is stored described guiding system Corresponding intrument, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right];$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The invention have the benefit that

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

2, Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, the generation of history correlation matrix are set Module, predictor choose module and forecast model construction module, and wherein predictor directly affects precision of prediction, correlation coefficient It is the index measuring stochastic variable dependency, it is possible to help to choose the variable closely-related with the future position instruction as forecast model Practice sample, choose multiple correlation coefficient as predictor, eliminate the subjectivity that initial predictor is chosen, by increasing capacitance it is possible to increase be pre- Survey precision, make forecast model construction module more stable and accurate；

3, the space correlation coefficient in Calculation of correlation factor module reflects the accessibility impact on forecast model of road network, Time correlation coefficient can express the time sequencing of flow sequence, reflects the cause effect relation on two sequence times, thus improves pre- Survey the efficiency of predictor selection；Due to the Weekly similarity of traffic flow, introduce the history phase of history correlation matrix generation module Close coefficient, with time correlation coefficient and space correlation coefficient with the use of, provide more data support for Accurate Prediction.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

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

Detailed description of the invention

The invention will be further described with the following Examples.

Embodiment 1

See Fig. 1, the present embodiment one traffic flow optimization guides system, including guiding system and is connected with guiding system Prediction means, described guiding system, for guiding the vehicle in multi-route, receives vehicle destination call data and by described car The benchmark that destination call data are allocated as vehicle route data；Described guiding system includes: guidance information is deposited Described guiding system corresponding intrument is arrived in storage, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right];$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The present embodiment arranges data categorization module and stationarity inspection module, adds the accuracy of data, and makes structure Forecast model more targeted；Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictor choose module and forecast model construction module, eliminate the master that initial predictor is chosen The property seen, by increasing capacitance it is possible to increase precision of prediction, makes forecast model construction module more stable and accurate；The present embodiment value L=8, M=3, Precision of prediction improves 1.5% relative to correlation technique.

Embodiment 2

See Fig. 1, the present embodiment one traffic flow optimization guides system, including guiding system and is connected with guiding system Prediction means, described guiding system, for guiding the vehicle in multi-route, receives vehicle destination call data and by described car The benchmark that destination call data are allocated as vehicle route data；Described guiding system includes: guidance information is deposited Described guiding system corresponding intrument is arrived in storage, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right];$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The present embodiment arranges data categorization module and stationarity inspection module, adds the accuracy of data, and makes structure Forecast model more targeted；Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictor choose module and forecast model construction module, eliminate the master that initial predictor is chosen The property seen, by increasing capacitance it is possible to increase precision of prediction, makes forecast model construction module more stable and accurate；The present embodiment value L=9, M=3, Precision of prediction improves 2% relative to correlation technique.

Embodiment 3

See Fig. 1, the present embodiment one traffic flow optimization guides system, including guiding system and is connected with guiding system Prediction means, described guiding system, for guiding the vehicle in multi-route, receives vehicle destination call data and by described car The benchmark that destination call data are allocated as vehicle route data；Described guiding system includes: guidance information is deposited Described guiding system corresponding intrument is arrived in storage, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right]:$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The present embodiment arranges data categorization module and stationarity inspection module, adds the accuracy of data, and makes structure Forecast model more targeted；Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictor choose module and forecast model construction module, eliminate the master that initial predictor is chosen The property seen, by increasing capacitance it is possible to increase precision of prediction, makes forecast model construction module more stable and accurate；The present embodiment value L=10, M= 4, it was predicted that precision improves 2.6% relative to correlation technique.

Embodiment 4

See Fig. 1, the present embodiment one traffic flow optimization guides system, including guiding system and is connected with guiding system Prediction means, described guiding system, for guiding the vehicle in multi-route, receives vehicle destination call data and by described car The benchmark that destination call data are allocated as vehicle route data；Described guiding system includes: guidance information is deposited Described guiding system corresponding intrument is arrived in storage, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right];$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The present embodiment arranges data categorization module and stationarity inspection module, adds the accuracy of data, and makes structure Forecast model more targeted；Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictor choose module and forecast model construction module, eliminate the master that initial predictor is chosen The property seen, by increasing capacitance it is possible to increase precision of prediction, makes forecast model construction module more stable and accurate；The present embodiment value L=11, M= 5, it was predicted that precision improves 3.2% relative to correlation technique.

Embodiment 5

See Fig. 1, the present embodiment one traffic flow optimization guides system, including guiding system and is connected with guiding system Prediction means, described guiding system, for guiding the vehicle in multi-route, receives vehicle destination call data and by described car The benchmark that destination call data are allocated as vehicle route data；Described guiding system includes: guidance information is deposited Described guiding system corresponding intrument is arrived in storage, described guidance information and described route are corresponding.

Preferably, guidance information described at least one is completely or partially by guiding sound to constitute.

Preferably, guiding sound used is: a piece of music or the melody of a first scaled-down version；A kind of plant equipment or system Specific sound；A kind of biospecific sound；And/or the sound of a kind of natural phenomena.

Preferably, it is characterized in that, it was predicted that device includes that the acquisition module being sequentially connected with, data preprocessing module, data are divided Generic module, stationary test module, Calculation of correlation factor module, threshold value setting module, temporal and spatial correlations coefficient matrix generation module, History correlation matrix generation module, predictor choose module and forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow of corresponding each time period Data and passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets friendship The data of logical practical situation；

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

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction Section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

$P\left(\mathrm{\τ}\right)=\frac{E\[(X_x-v_i)(X_{x+\mathrm{\τ}}-v_{x+\mathrm{\τ}})\]}{{\mathrm{\σ}}^2}$

Wherein, X_xRepresent traffic flow sequence to be tested, ν_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_x Traffic flow sequence after time delay τ, ν_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can rapid decay level off to 0 or 0 near fluctuation, the most described traffic flow sequence to be tested Row pass through stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then treat described Inspection traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_i With prediction section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij W (), if having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

${\mathrm{\ρ}}_{ij}\left(\mathrm{\τ}\right)=\frac{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right){\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})}{{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_i\left(t\right)-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_i\left(t\right)\]}}^2\×{\sqrt{{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}\[x_j(t+\mathrm{\τ})-\frac{1}{n-\mathrm{\τ}}{\mathrm{\Σ}}_{t=1}^{n-\mathrm{\τ}}{x}_j(t+\mathrm{\τ})\]}}^2}$

Space correlation coefficient ρ_ijW the computing formula of () is:

Preferably, it is characterized in that, it was predicted that device also includes:

(6) threshold value setting module, is used for time delay maximum L, the temporal and spatial correlations coefficient threshold setting 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 section_ij(τ) with space phase Close coefficient ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ under different time postpones τ (τ) ', and calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L be [8, 12], the computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

$\mathrm{\ρ}{\left(\mathrm{\τ}\right)}^{\′}=\left[\begin{array}{cccc}{\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& {\mathrm{\ρ}}_{1j}{\left(0\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(0\right)}^{\′}\\ {\mathrm{\ρ}}_{1j}{\left(1\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(1\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(1\right)}^{\′}\\ ...& ...& ...& ...\\ {\mathrm{\ρ}}_{1j}{\left(L\right)}^{\′}& {\mathrm{\ρ}}_{2j}{\left(L\right)}^{\′}& ...& {\mathrm{\ρ}}_{Nj}{\left(L\right)}^{\′}\end{array}\right];$

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory be correlated with Sequence, is designated asThe span of M is [3,5], described history phase Close coefficient ρ_jmT the computing formula of () is:

${\mathrm{\ρ}}_{jm}\left(t\right)=\frac{{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right){\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)}{\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_j\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_j\left(t\right)\]}^2}\×\sqrt{{\mathrm{\Σ}}_{t=1}^n{\[x_{jm}\left(t\right)-\frac{1}{n}{\mathrm{\Σ}}_{t=1}^n{x}_{jm}\left(t\right)\]}^2}}$

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂ Choose the predictor relevant to predicting impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, Selection principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn to meet the traffic flow composition of condition new Sequence and as the first predictor, be denoted as X', X'=(x₁',x₂',...,x_p'), wherein p is the described friendship meeting condition Through-current capacity number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, then the first predictor X' can state following matrix form as:

$X^{\′}=\left[\begin{array}{cccc}{x_1}^{\′}\left(1\right)& {x_2}^{\′}\left(1\right)& ...& {x_p}^{\′}\left(1\right)\\ {x_1}^{\′}\left(2\right)& {x_2}^{\′}\left(2\right)& ...& {x_p}^{\′}\left(2\right)\\ ...& ...& ...& ...\\ {x_1}^{\′}(n-L_1)& {x_2}^{\′}(n-L_1)& ...& {x_p}^{\′}(n-L_1)\end{array}\right];$

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y'={y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can be stated as Following matrix form:

(10) forecast model construction module, it is by coming the first predictor and the second predictor as training sample Construct the measurable section forecast model in the traffic flow of subsequent time.

Wherein, in described data preprocessing module, the rule of the data not meeting traffic practical situation described in rejecting is: In one data update cycle, set the threshold range of total traffic flow data in each section respectively, if certain section collected Total traffic flow data fall in corresponding threshold range, then show that these group data are reliable, retain this group data；If collecting Total traffic flow data in certain section fall not in corresponding threshold range, then show that these group data are unreliable, and picked Remove.

Wherein, described stationary test module includes following submodule:

(1) syndrome module, for traffic flow sequence and the prediction section being in same type of observation section Traffic flow sequence carries out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the friendship to be tested of stationary test Through-current capacity sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method pair Data carry out polishing；

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

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for poor to the data after polishing Divisional processing, and the data after difference processing are sent to syndrome module.

The present embodiment arranges data categorization module and stationarity inspection module, adds the accuracy of data, and makes structure Forecast model more targeted；Calculation of correlation factor module, temporal and spatial correlations coefficient matrix generation module, history phase relation are set Matrix number generation module, predictor choose module and forecast model construction module, eliminate the master that initial predictor is chosen The property seen, by increasing capacitance it is possible to increase precision of prediction, makes forecast model construction module more stable and accurate；The present embodiment value L=12, M= 5, it was predicted that precision improves 3.5% relative to correlation technique.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (7)

1. traffic flow optimization guides a system, including guiding system and the prediction means that is connected with guiding system, described guiding System, for guiding the vehicle in multi-route, receives vehicle destination call data and described vehicle destination call data is made The benchmark being allocated for vehicle route data；Described guiding system includes: guidance information is stored described guiding system pair Answer device, described guidance information and described route are corresponding.

A kind of traffic flow optimization the most according to claim 1 guides system, it is characterized in that, guidance information described at least one Completely or partially by guiding sound to constitute.

A kind of traffic flow optimization the most according to claim 2 guides system, it is characterized in that, guiding sound used is: one First melody or the melody of a first scaled-down version；Sound specific to a kind of plant equipment or system；A kind of biospecific sound；With And/or the sound of a kind of natural phenomena of person.

A kind of traffic flow optimization the most according to claim 3 guides system, it is characterized in that, described prediction means includes successively The acquisition module of connection, data preprocessing module, data categorization module, stationary test module, Calculation of correlation factor module, threshold Value setting module, temporal and spatial correlations coefficient matrix generation module, history correlation matrix generation module, predictor choose module With forecast model construction module:

(1) acquisition module, is used for gathering observation section S in road network S_i, prediction section S_jThe traffic flow data of corresponding each time period And passage situation；

(2) data preprocessing module, for described traffic flow data carries out data prediction, and rejecting does not meets traffic in fact The data of border situation；

(3) data categorization module, for carrying out classification of type, described type bag to the traffic flow data through data prediction Include traffic flow data festivals or holidays, traffic flow data at weekend and traffic flow data on working day；

(4) stationary test module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction section S_jTraffic flow sequence X_jCarrying out stationary test respectively, the auto-correlation function of inspection stationarity is:

Wherein, X_xRepresent traffic flow sequence to be tested, v_iRepresent the average of traffic flow sequence to be tested, X_x+τRepresent X_xTime Between postpone the traffic flow sequence after τ, v_x+τFor X_x+τAverage, σ²For X_xWith X_x+τBetween variance；

When auto-correlation function P (τ) can level off to 0 or fluctuate 0 near by rapid decay, and the most described traffic flow sequence to be tested is logical Cross stationary test；When auto-correlation function P (τ) can not rapid decay level off to 0 or near 0 fluctuate, then to described to be tested Traffic flow sequence proceeds stationary test after carrying out calm disposing；

(5) Calculation of correlation factor module, for calculating the observation section S by stationary test_iTraffic flow sequence X_iWith in advance Survey section S_jTraffic flow sequence X_jTime correlation coefficient ρ under time delay τ_ij(τ) with space correlation coefficient ρ_ij(w), If having N number of section, traffic flow sequence X in road network S_i=[x_i(1),x_i(2),...,x_i(n)], traffic flow sequencex_iT () represents observation section S_iAt the flow of t, x_jT () represents prediction section S_jWhen t The flow carved, t=1,2 ... n, time correlation coefficient ρ_ij(τ) computing formula is:

Space correlation coefficient ρ_ijW the computing formula of () is:

。

A kind of traffic flow optimization the most according to claim 4 guides system, it is characterized in that,

(6) threshold value setting module, for setting time delay maximum L, the temporal and spatial correlations coefficient threshold T between each section₁With 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 section_ij(τ) with space correlation system Number ρ_ijW () builds each observation section S_iWith prediction section S_jTemporal and spatial correlations coefficient matrix ρ (τ) ' under different time postpones τ, And calculate the temporal and spatial correlations coefficient ρ in each section_ij(τ) ', wherein i ∈ [1, N] and τ ∈ [0, L], the span of L is [8,12], The computing formula of temporal and spatial correlations coefficient matrix ρ (τ) ' is:

Temporal and spatial correlations coefficient ρ_ij(τ) ' computing formula be:

ρ_ij(τ) '=ρ_ij(τ)ρ_ij(w)；

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

Wherein, the same period all for nearly M and same type of historical traffic are chosen as traffic flow sequence X_jHistory correlated series, It is designated asM=1,2 ... the span of M, M is [3,5], described history phase relation Number ρ_jmT the computing formula of () is:

(9) predictor chooses module, for according to described temporal and spatial correlations coefficient threshold T₁With history correlation coefficient threshold T₂Choose The predictor relevant to prediction impact point, and carry out matrix reconstruction according to locus j selected by it with time delay τ, choose Principle is:

If ρ_ij(τ) ' ＞ T₁, then will observation section S_iTraffic flow sequence X_iIn meet the traffic flow new sequence of composition of condition Arrange and as the first predictor, be denoted as X', X'=(x₁', x₂' ..., x_p'), wherein p is the described traffic flow meeting condition Amount number, if L₁It is the maximum of time delay, L in the first predictor₁=max{ τ | τ ∈ [0, L] and ρ_ij(τ) ' ＞ T₁, Then the first predictor X' can state following matrix form as:

If ρ_jm(t) ＞ T₂, then by all history correlated series X meeting condition_jmT (), as the second predictor, is denoted as Y', Y' ={ y₁',y₂',...,y_q', wherein q is the historical traffic number meeting condition, and the second predictor Y' can state following square as Formation formula:

(10) forecast model construction module, it is by constructing the first predictor and the second predictor as training sample Measurable section is at the forecast model of the traffic flow of subsequent time.

A kind of traffic flow optimization the most according to claim 5 guides system, it is characterized in that, described data preprocessing module In, the rule of the data not meeting traffic practical situation described in rejecting is: within a data update cycle, set each road respectively The threshold range of total traffic flow data of section, if total traffic flow data in certain section collected falls at corresponding threshold value model In enclosing, then show that these group data are reliable, retain this group data；If total traffic flow data in certain section collected falls not right In the threshold range answered, then show that these group data are unreliable, and rejected.

A kind of traffic flow optimization the most according to claim 6 guides system, it is characterized in that, described stationary test module bag Include following submodule:

(1) syndrome module, for being in same type of observation section S_iTraffic flow sequence X_iWith prediction section S_j's Traffic flow sequence X_jCarry out stationary test respectively；

(2) continuity check submodule, is connected with syndrome module, for not by the traffic flow to be tested of stationary test Amount sequence carries out continuity check, if the seriality of not meeting, described continuity check submodule uses average interpolation method to data Carry out polishing；

(3) misarrangement submodule, is connected with continuity check submodule, for deleting the data of apparent error, uses average simultaneously Interpolation method carries out polishing to data；

(4) difference processing submodule, connects misarrangement submodule and syndrome module, for carrying out the data after polishing at difference Reason, and the data after difference processing are sent to syndrome module.