CN103413428A - Expression method of road traffic information credibility space characteristics based on sensor network - Google Patents

Abstract

The invention discloses an expression method of road traffic information credibility space characteristics based on a sensor network, and relates to the technical field of the road traffic information fusion. The method includes: establishing a road traffic network model under the sensor network; obtaining traffic information space distribution based on the sensor network, and determining a measure for describing the traffic information credibility space related characteristics; providing typical characteristics of an information credibility function; providing a commonly-used space superposition algorithm; based on double measures of correlation and distance among sensor traffic information, drawing a scatter diagram of a traffic information correlation coefficient and the distance; obtaining the sensor information credibility function through a curve fitting; and calibrating the information credibility function suitable for a city expressway to obtain a mathematical expression of the information credibility function of the city expressway. According to the invention, the spatial distribution law which is formed when the sensor detects the information is used to guide and optimize the spatial topological structure of the sensor network.

Description

Traffic Information Faith Degree Set characteristic method for expressing based on sensor network

Technical field

The invention belongs to Traffic Information integration technology field, relate in particular to a kind of Faith Degree Set of Traffic Information based on sensor network characteristic method for expressing.

Background technology

Practice for many years shows, intelligent transportation system (ITS, Intelligent Transportation System) has proposed theoretical, technical support and put into practice direction for solving urban transport problems.Along with the development of intelligent transportation system, traffic-information service and related system are more and more important to tourist and decision maker.A lot of scholars obtain in transport information, a large amount of research has been carried out in communication, traffic information fusion and the aspects such as excavation and transport information issue.But transport information is an extensive concept, can to transport information, estimate from many aspects.Traditional transport information is estimated mainly for traffic flow itself and relevant extension the thereof.The magnitude of traffic flow, speed, occupation rate are three basic parameters of traffic flow, and they are by the reflection of traffic flow to transport information itself, belong to once estimating of transport information, and they have meaned traffic flow modes intuitively.In addition, the information parameter that also has much important reflection road net traffic state, as hourage, the rate of blocking up, unimpeded degree, service level, traffic delay etc., these parameters can be used as the secondary of transport information to be estimated, and they have provided highway section level or the road network level measurement index of traffic behavior in certain space scope and time scope.These indexs can be from the room and time characteristic of more macroscopical meaning reflection transport information, can be good at instructing us to road traffic congestion state, to pass judgment on, and carry out short-term or long-term forecasting traffic flow, in order to the transport information reference of quantification is provided at aspects such as traffic control, traffic guidance, Traffic Evaluations.

But along with people are more and more extensive to the demand of transport information at Spatial Dimension and time dimension, traditional transport information is estimated in some aspects can't carry out accurate description to the spatial characteristics of transport information confidence level.Along with the development of sensor network and the application in traffic network thereof, people need to obtain transport information from the sensor node of laying some fixed position of road.Such as, the decision maker only is concerned about certain position transport information of (being called point of interest location) sometimes, and the transport information of this position is unknowable, or can't directly obtain, and can only transplant near the transport information of other positions this position.So for the decision maker, the transport information of understanding other positions is necessary to percentage contribution or the credibility of this position.Like this, spatially, the decision maker can know according to the transport information of known location the transport information at point of interest location place by inference.Have this demand that can't meet the decision maker of estimating of transport information now, need a kind of new this spatial character of estimating to describe the transport information confidence level.

Obtaining of existing transport information mainly depends on the fixation of sensor be laid on road and the sensor network consisted of these sensors, and these sensors and network are point-like more or film micro area distributes, as coil checker, geomagnetism detecting device, microwave and video detector etc. commonly used, their detector-range mostly is bicycle road section or multilane section.The present invention proposes the road network topology model that is laid with traffic sensor, obtaining the required all the sensors of one-way road section transport information, regard a section sensor as, highway section is counted as directed line segment usually, crossing is counted as summit, for connecting each highway section, each section sensor is regarded the point in directed line segment as.Provided the describing method based on the transport information Faith Degree Set nonunf ormity of sensor network.Because road conditions is diversified, so the space distribution of the transport information on corresponding road also can be different.By sensor network, must obtain different transport information spatial characteristics to the obtaining of Traffic Information of these Different Traffic Flows characteristics, and these differences can directly be reflected in embodying in form of sensor information reliability function.The present invention proposes three kinds of common typical characteristics-negative exponent characteristics, slope characteristic, step-characteristic, has provided respectively the mathematical expression of each characteristic function under the symmetric case.On this basis, the present invention has studied the space superposition method of information credibility function, and has proposed two kinds of space overlay model commonly used-maximal value pattern and information fusion patterns.By each sensor transport information correlation calculations and add distance parameter, the present invention provides the scaling method based on the information credibility function of related coefficient.Calculate the related coefficient between sensor information in twos, draw facies relationship numerical value with apart between scatter diagram, utilize fitting algorithm to obtain the matched curve of this scatter diagram, according to the calibration function of curve, obtain the mathematical expression of information credibility function.

The present invention has defined and a kind ofly has been used for describing the transport information of known location to the new method of percentage contribution or the credibility at point of interest location place-information credibility function.The information credibility function is based on the spatial description of the transport information confidence level of sensor network.Space computing by a plurality of known location space of points reliability functions on highway section has provided highway section confidence level and road network confidence level.The information credibility function can be used as three times of transport information to be estimated, and is by physical features, to reflect a kind of new form of transport information.

Summary of the invention

The object of the invention is to, propose a kind of Faith Degree Set of Traffic Information based on sensor network characteristic method for expressing, the deficiency existed be used to solving prior art.

To achieve these goals, the technical scheme of the present invention's proposition is that a kind of Faith Degree Set of Traffic Information based on sensor network characteristic method for expressing, is characterized in that described method comprises the following steps:

Step 1: by analyzing fixation of sensor for obtaining the characteristics of Traffic Information, set up the road traffic road net model under sensor network;

Step 2: obtain the space distribution of transport information based on sensor network, be identified for describing estimating of transport information Faith Degree Set correlation properties;

Step 3: by the character of analyte sensors information credibility function, provide the typical characteristics of information credibility function;

Step 4: consider the space superposition of different information credibility functions, provide space superposition algorithm commonly used;

Step 5: estimate based on the dual of the correlativity between the sensor transport information and distance, draw the scatter diagram of transport information related coefficient and distance, obtain the sensor information reliability function by curve;

Step 6: the information credibility function that is applicable to city expressway is demarcated, obtained the information credibility function mathematic(al) representation of city expressway.

The present invention proposes newly estimating of Traffic Information Faith Degree Set characteristic, namely based on the Traffic Information Faith Degree Set characteristic method for expressing of sensor network.This method example show sensor points and near the transport information outer than certain distance abundanter, the transport information confidence level is higher, and along with the increase of distance, the transport information confidence level is a certain trend and descends.Utilize this method can be for the Reasonable Arrangement of traffic sensor and optimization, based on the transport information space distribution mechanism research of sensor network, provide theoretical foundation.

The accompanying drawing explanation

Fig. 1 is that dissimilar traffic sensor is laid mode and coverage schematic diagram; Wherein, be (a) that the coil checker of commonly using is laid mode and coverage schematic diagram, be (b) the geomagnetism detecting device is laid mode and coverage schematic diagram, be (c) microwave and video detector are laid mode and coverage schematic diagram;

Fig. 2 is based on the road network topology model structure figure of sensor network;

Fig. 3 is the negative exponent characteristic schematic diagram of transport information degree space distribution;

Fig. 4 is the slope characteristic schematic diagram of transport information degree space distribution;

Fig. 5 is the step-characteristic schematic diagram of transport information degree space distribution;

Fig. 6 is the space superposition schematic diagram of information credibility function;

Fig. 7 is flow related coefficient and detector spacings scatter diagram;

Fig. 8 is average discharge related coefficient and detector spacings scatter diagram and matched curve figure.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that, following explanation is only exemplary, rather than in order to limit the scope of the invention and to apply.

Embodiment 1

Traffic Information based on sensor network Faith Degree Set characteristic method for expressing provided by the invention, contain following steps:

Step 1: by analyzing fixation of sensor for obtaining the characteristics of Traffic Information, set up the road traffic road net model under sensor network:.

The obtaining of existing transport information mainly depends on the fixation of sensor that is laid on road and the sensor network of composition thereof, and these sensors and network thereof are point-like more or film micro area distributes, as coil checker (Fig. 1 (a)), geomagnetism detecting device (Fig. 1 (b)), microwave and video detector (Fig. 1 (c)) etc. commonly used, their detector-range mostly is bicycle road section (Fig. 1 (a) and (b)) or multilane section (Fig. 1 (c)), as shown in Figure 1.

In the actual traffic Information Acquisition System, transport information obtained mainly with the one direction section to be unit, namely to obtain simultaneously each track transport information of one direction road section.Transport information of the present invention is also to take the one-way road section to be unit, regards a section sensor (hereinafter directly being called sensor) as obtaining the required all the sensors of one-way road section transport information.From macroscopic road network topology angle, highway section is counted as directed line usually, and crossing is counted as summit, be used to connecting each highway section, is laid with road network topology model such as Fig. 2 of traffic sensor.Each open circles is each crossing or highway section end points in road network, directed line segment is the highway section between each crossing, and little filled circles is each section sensor in road network, this topological model has provided the position of each sensor in the road network simultaneously, each sensor can detect various transport information in road according to the actual requirements, as vehicle flowrate, speed, occupation rate, intersection delay etc.

As can be seen from Figure 2, in road network, obtain based on the transport information of sensor network the obtaining of transport information that its essence is each point in road network topology, and the transport information of other positions can only be obtained by these known points to a certain extent.

Step 2: analyze the space distribution unevenness of obtaining transport information based on sensor network, definition is for describing newly estimating of transport information Faith Degree Set correlation properties.

Transport information is mainly derived from the detection data of each sensor be laid on road, because each sensor is spot distribution on highway section, so the transport information in whole road network has heterogeneity.

So for the traffic sensor of laying on each unidirectional highway section, can a kind ofly estimate to describe the space distribution rule that obtains transport information based on sensor network with what weigh sensor transport information space distribution.If do not consider the accuracy rate problem of sensor self, in whole unidirectional highway section, in the confidence level of the section transport information at sensing station place, must be 100%, because sensor just in time detects and can reflect the transport information at sensing station place; Expand on space, whole unidirectional highway section, the position of every laying sensor, the confidence level of its transport information are 1, and other do not lay the position of sensor, because we can't be directly from the detection data of sensor, knowing the transport information of this position, so its confidence level must be less than or equal to 1.

Step 3: by the character of analyte sensors information credibility function, provide several typical characteristics of information credibility function, and provide the step of corresponding mathematical expression for every specific character.

For the transport information Faith Degree Set characteristic of more definite description based on sensor network, the present invention has three rational hypothesis: (1) is on time-limited highway section, the traffic behavior of upstream and downstream position has certain causality, and namely the traffic behavior of diverse location is not completely random; (2) on highway section, the traffic behavior of each position can be determined to a certain extent by other known location traffic behavior, namely has a degree of certainty; (3) known location from point of interest location more close to, its degree of certainty to the point of interest transport information is higher, namely point of interest is higher to the confidence level of its information.Based on above-mentioned hypothesis, for each the unidirectional highway section in road network, the position that reaches certain sensor is initial point o, the information credibility function (Sensor ICF) of definition sensor:

ICF ^sensor=f(x)x∈(-∞,+∞) (1)

Wherein: x is the distance apart from sensor, because distance is two-way, definition along oriented highway section right-hand side direction for just, otherwise for bearing.

The information credibility function has reflected this heterogeneity of the transport information of being obtained by fixation of sensor.The information credibility functional value at some x place has represented the confidence level of the transport information of this sensor collection at the x place.

The sensor information reliability function has following character:

(a) appoint and get x, 0≤f (x)≤1 is arranged; (b) f (0)=1; (c) along road to field of definition from x ₁To x ₂Definite integral can be defined as sensor at distance x ₁To x ₂Absolute contribution degree (d _Ac), namely

(d) sensor is at distance x ₁To x ₂Relative contribution (d _Rc) be $d_{\mathrm{rc}}(x_1\→x_2)={\∫}_{x1}^{x2}f\left(x\right)\mathrm{dx}/{\∫}_{-\∞}^{+\∞}f\left(x\right)\mathrm{dx}.$

According to the character of above information credibility function, below provide three kinds of possible function characteristics.

1. negative exponent characteristic

The negative exponent characteristic is a kind of expression of the non-homogeneous gradual change of transport information space distribution, and available negative exponential function is expressed.Here arranging sensing station point place is that true origin o(slope characteristic and step-characteristic are same therewith).Fig. 3 has provided the information credibility function schematic diagram of negative exponent characteristic.The characteristics of negative exponent characteristic are that along with the continuous increase of distance sensor distance, the confidence level of the transport information of relevant position is higher but decline rate is very fast in the nearer zone of distance sensor distance (as A1); And in distance sensor distance zone far away (as A2), along with the continuous increase of distance sensor distance, the confidence level of the transport information of relevant position is lower but decline rate is slow gradually.

Due to gradually changeable and the continuity of road traffic flow bulk parameter in whole unidirectional highway section, the transport information of certain position of being reflected by these traffic flow bulk parameters can be detected data and obtain by the sensor at its place ahead or rear; The traffic data that same certain position transducer detects can be to reflect the transport information of this forward and backward position of sensor to a certain degree, therefore the sensor information reliability function also has corresponding functional form (slope characteristic and step-characteristic are same therewith) in (∞, 0) zone.Suppose (∞, 0) zone the same index of coincidence form of sensor information reliability function, (∞ ,+∞) in, the expression formula of the transport information reliability function of single-sensor is:

$f=\left\{\begin{array}{cc}{e}^{-\mathrm{kx}}& x\∈[0,+\∞)\\ e^{k^{\′}x}& x\∈(-\∞,0)\end{array}\right.---\left(2\right)$

Wherein: k, k' are the exponential function coefficient, and k, k' >=0.If f is longitudinal axis symmetric function, k, k', now the expression formula of sensor information reliability function is: f=e ^-k|x|X ∈ (∞ ,+∞).

2. slope characteristic

Different with the negative exponent characteristic, slope characteristic is a kind of expression of the even gradual change of transport information space distribution, the useable linear function representation.The slope characteristic that under the single-sensor effect, the transport information Faith Degree Set distributes as shown in Figure 4.

The characteristics of slope characteristic are in the zone of distance sensor different distance (as A3, A4), and along with the continuous increase of distance sensor distance, the continuous reduction of the confidence level of the transport information of relevant position and decline rate are identical.Now the sensor information reliability function is mainly determined by Slope Parameters, and its expression formula is:

Wherein: a, a' are the negative exponential function coefficient, and a, a' >=0.Equally, if f is longitudinal axis symmetric function, i.e. a=a', following formula can be reduced to: f=-a|x|x ∈ [1/a, 1/a].

3. step-characteristic

Step-characteristic is the another kind of representation of transport information changes in spatial distribution rule, and it is a kind of distribution mode of segmentation saltus step, according to different ladder numbers, can show diversified saltus step form.Its principal feature is, in a segment distance, functional value remains unchanged (A5 in Fig. 5), and along with the increase from sensor distance, functional value can be reduced to suddenly another value, and in a segment distance, remain unchanged (A6 in Fig. 5).Fig. 8 has provided the step-characteristic that the ladder number is 2 transport information space distribution.

As shown in Figure 5, the expression formula of 2 ladder sensor information reliability functions is:

Wherein: p ₁, p ₂, p' ₁, p' ₂, q ₁, q' ₁For the ladder parameter, and all be greater than 0.If 2 ladder sensor information reliability function longitudinal axis symmetry, i.e. p ₁=p' ₁, p ₂=p' ₂, q ₁=q' ₁:

Because the sensor information functional value of step-characteristic is discrete value, easy in order to describe, can mean by the starting point of ladder.2 ladder sensor information reliability functions mainly contain 4 key points, i.e. (p ₁, q ₁), (p ₂, 0), (p' ₁,-q' ₁), (p' ₂, 0), 6 parameters of these 4 points have been determined function shape fully, therefore the expression formula of 2 ladder sensor information reliability functions can be reduced to: f={ (p ₁, q ₁, p ₂), (p ₁', q ₁', p ' ₂), if f longitudinal axis symmetry, its reduced form is: f={ (p ₁, q ₁, p ₂) ².

Step 4: consider the space superposition of different information credibility functions, provide space superposition algorithm commonly used.

For different sensors information credibility function, carry out the space superposition, different overlay models can be arranged.Fig. 6 (dash area) has illustrated the superposition phenomenon between two sensor information reliability functions.According to different applicable cases, can select corresponding overlay model.Two kinds of common overlay models (Fig. 6) are discussed here.

The first overlay model is the maximal value pattern.

The maximal value pattern Superposition Formula of two sensors ICF is as follows:

ff(x)=f _i(x)⊕f _j(x)=max(f _i(x-x _i),f _j(x-x _j)) (6)

Wherein: ⊕ is sensor information reliability function space superposition symbol; Ff (x) is for specifying the superpositing function of two sensors ICF under initial point o; f _i(x) and f _j(x) be respectively the information credibility function of sensor i and j; x _iAnd x _jBe respectively the coordinate of specified coordinate lower sensor i and j.

Its stacked system is to choose the maximal value at each summing point place as the value at this some place road section information reliability function.The basic thought of maximal value pattern is based on following thinking: for same object, we are easy to accept to mean confidence level or the higher information of reliability of this object, and cast out confidence level or the low information of reliability, so the maximal value pattern has its practical significance, and be convenient to calculate (see figure 6).

The second overlay model is the information fusion pattern.

The information fusion pattern Superposition Formula of two sensors ICF is as follows:

ff(x)=f _i(x)⊕f _j(x)=1-(1-f _i(x-x _i))(1-f _j(x-x _j)) (7)

By some traffic data blending algorithms, can access abundanter transport information or make the accuracy of the original traffic information obtained (flow, speed or other traffic parameters) higher.Therefore by data fusion, the integrality of transport information, reliability can be higher, and the road section information reliability function value after two sensor information confidence levels stacks can become greatly, the more pervasive (see figure 6) of the practical significance of this pattern.

For research ，Ji highway section, a unidirectional highway section starting point, be initial point o, the coordinate of i sensor is x _i, when on unidirectional highway section, laying n sensor, road information reliability function (Road ICF) is defined as follows:

${\mathrm{ICF}}^{\mathrm{road}}=\underset{i=1}{\overset{n}{\⊕}}{\mathrm{ICF}}^{\mathrm{sensor}}=\underset{i=1}{\overset{n}{\⊕}}{f}_i\left(x\right)---\left(8\right)$

Step 5: utilize the correlativity between the different sensors transport information, and consider the distance factor between sensor, draw the scatter diagram of transport information related coefficient and distance, obtain the sensor information reliability function by curve.

Related coefficient (Correlation Coefficient) is the linear dependence degree of two groups of time serieses of reflection (sensing data can be regarded the time series of transport information at the diverse location place as), if do not consider further data fusion, related coefficient has reflected the linear relationship two groups of data, facies relationship numerical value absolute value larger (comprising positive correlation degree and negative correlation degree), linear expression more mutually.When the correlativity of the traffic time sequence of two sensors position is 1, illustrate that time series herein can be fully by other side linear expression, do not consider other data fusion methods, can think herein time series can be fully by the probability of other side linear expression, be 1.

Step 6: the information credibility function is demarcated, obtained the reliability function mathematic(al) representation.

Due to other side linear degree, can weigh a time series and know another seasonal effect in time series confidence level by inference, can utilize related coefficient to demarcate the sensor information reliability function.Here, consider the distance measure of information, definition two seasonal effect in time series spatial dependence function (SCF):

SCF(d _X,Y)=corr(X,Y) (9)

Wherein: X and Y are the time serieses with n element, and corr is two seasonal effect in time series related coefficients, namely

$\mathrm{corr}(X,Y)=\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}(X_i-\stackrel{\‾}{X})(Y_i-\stackrel{\‾}{Y})}{\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(X_i-\stackrel{\‾}{X})}^2}\sqrt{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(Y_i-\stackrel{\‾}{Y})}^2}}---\left(10\right)$

Due to the relativity of position, make SCF (d _X,Y)=SCF (d _X,Y).Utilize the real road sensing data, can calculate the spatial dependence function value under different distance, and obtain the scatter diagram of space correlation coefficient and distance.Now, the sensor information reliability function can be obtained by following formula:

f(x)=fitt(SCF(d ₁),SCF(d ₂),...,SCF(d _k)) (11)

Wherein: SCF (d ₁), SCF (d ₂) ..., SCF (d _k) be respectively apart from d ₁, d ₂..., d _kThe time the spatial dependence function value, fitt is based on the fitting function of certain fitting algorithm.

Embodiment 2

Utilize Beijing's Second Ring Road actual traffic sensor information data, the information credibility function that is applicable to city expressway is demarcated, obtain the step of the information credibility function mathematic(al) representation of through street, Beijing.

Data used herein are the traffic data of the microwave detector installed on Beijing's Second Ring Road, are provided by Beijing Traffic Administration Bureau, and it has provided the flow information of whole day, and data break is 2min.The about 33km of Second Ring Road total length, namely get research road section length L=33.Due to night vehicle flowrate less, data are not representative, this paper mainly utilizes the detector data between 6:00-21:00, research is towards the scaling method of the transport information reliability function of city expressway.

In order to obtain more accurately the character of the transport information of difference on highway section, choose the data of existing 52 microwave detectors detection on Beijing's Second Ring Road as research object, time is on April 14th to 18,2008 (five working days), and each existing microwave detector is numbered, reject 3 invalid detection device data, totally 49 valid detector data.Numbering, from ponding Tan Qiao, along clockwise direction, finishes to the Xin Jie Kou gap, is respectively No. 1-49, studies the spatial characteristics of each detecting device transport information confidence level.

At first No. 1 detecting device of take is reference point, utilize formula (9,10), first calculate the related coefficient of No. 1 detecting device and 1-25 detector data time series (flow information), calculate successively No. 2 detecting devices and 2-26 detector data seasonal effect in time series related coefficient, until No. 25 detecting devices and 25-49 detecting device.

By above-mentioned calculating, can obtain the situation of change of transport information (flow information) spatial correlation function based on different distance, utilize the distance (in Table 1) of each detector points, obtain the magnitude of traffic flow space correlation coefficient of different distance and the scatter diagram (Fig. 7) of distance.

Distance table between the adjacent two sensors of table 1

Through type (11), utilize each detector points transport information spatial correlation function of highway section to demarcate the space distribution rule of transport information confidence level, i.e. the information credibility function.Based on the sensor i of the magnitude of traffic flow and the spatial dependence function SCF of j _i,j ^Volume(i, j ∈ [Isosorbide-5-Nitrae 9]) can utilize formula (9) to try to achieve.Consider the distance of sensor i and j, calculate respectively each distance range (d _Ij∈ [l, l+1) (and l=0,1 ..., L/2-1)) and interior SCF _i,j ^VolumeMean value as the spatial correlation function value of this distance.Fig. 8 has provided magnitude of traffic flow SCF get average in each distance segment after and the scatter diagram of detector distance, and the scatter diagram of Fig. 8 is carried out to the negative exponent match, can obtain SCF ^VolumeAnd the relation between distance x, this relation as shown in the formula:

y=e ^-kx (12)

Wherein: k is the negative exponential function coefficient, and k >=0.By real data, match obtains k=0.1592, that is: SCF ^Volume(x)=e ^-0.1592x.Due to | corr (X, Y) |=| corr (Y, X) |, SCF _i,j ^Volume=SCF _j,i ^Volume.By on can obtain Beijing's Second Ring Road magnitude of traffic flow the information credibility function have the negative exponent characteristic, and expression formula is:

$f^{\mathrm{volume}}\left(x\right)=\left\{\begin{array}{cc}{e}^{-0.1592x}& x\∈[0,+\∞)\\ e^{0.1592x}& x\∈(-\∞,0)\end{array}=e^{0.1592\left|x\right|}\right.,x\∈[-\∞,+\∞]---\left(13\right)$

In order to disclose the physics law based on the transport information space distribution of sensor network, the present invention has defined the spatial characteristics that sensor information reliability function, road section information reliability function are described the transport information confidence level.By a kind of spatial correlation function and consider the distance measure of information, provided the scaling method of sensor information reliability function.Utilize formula (6,7,8), can further demarcate the highway section reliability function by the space overlap-add operation, thereby the spatial characteristics of the transport information confidence level of whole highway section or road network has been had clearly and described.This example utilizes the sensing data of Beijing's Second Ring Road to demarcate the sensor information reliability function, and result shows that the distribution of Beijing's Second Ring Road space communication information credibility is the negative exponent characteristic.Due to the similarity of traffic flow rule, this conclusion is not general, can offer reference and reference for the space distribution of the transport information confidence level of city expressway and even city road network.Towards through street, Beijing, sample result can also obtain the significant conclusion about the transport information space distribution.Sensor points and near the transport information outer than certain distance thereof are abundant, and along with the increase of distance, the transport information confidence level is exponential trend and descends; Along with the increase from sensor distance, the confidence level of transport information constantly descends, when confidence level drops to a certain degree, the transport information of this position has not been suitable for the service of intelligent transportation system related application, need to utilize the sensing data that other confidence levels are higher to compensate; By the space superposition of information credibility function, when certain of highway section a bit has a plurality of detector points to cover, can increase the transport information confidence level of this point.Traffic Information based on sensor network Faith Degree Set characteristic method for expressing of the present invention can provide theoretical foundation for Reasonable Arrangement and optimization, regional traffic sensor network and the optimization of traffic sensor.

The above; only be the present invention's embodiment preferably, but protection scope of the present invention is not limited to this, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.

Claims (3)

1. the Faith Degree Set of the Traffic Information based on a sensor network characteristic method for expressing, is characterized in that described method comprises the following steps:

Step 1: by analyzing fixation of sensor for obtaining the characteristics of Traffic Information, set up the road traffic road net model under sensor network;

Step 2: obtain the space distribution of transport information based on sensor network, be identified for describing estimating of transport information Faith Degree Set correlation properties;

Step 3: by the character of analyte sensors information credibility function, provide the typical characteristics of information credibility function;

Step 4: consider the space superposition of different information credibility functions, provide space superposition algorithm commonly used;

Step 5: estimate based on the dual of the correlativity between the sensor transport information and distance, draw the scatter diagram of transport information related coefficient and distance, obtain the sensor information reliability function by curve;

Step 6: the information credibility function that is applicable to city expressway is demarcated, obtained the information credibility function mathematic(al) representation of city expressway.

2. method according to claim 1, is characterized in that described space superposition comprises two kinds of patterns, is respectively maximal value pattern and information fusion pattern.

3. method according to claim 1, is characterized in that the scatter diagram of described drafting transport information related coefficient and distance, concrete by calculating the spatial dependence function value under different distance, draws the scatter diagram of transport information related coefficient and distance.

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