CN104332051B - Urban road RFID detector optimizes distribution method - Google Patents

Abstract

The invention discloses a kind of urban road RFID detector based on journey time estimation and flow similarity analysis and optimize distribution method.The method first passes through the volume of traffic to each basic road in city road network and carries out similarity analysis and average travel time is estimated, determine the correlation matrix between basic road flow and Loop detector layout number, then set up, with this, the Mathematical Modeling that RFID detector optimum is layouted, finally by genetic algorithm, model is solved, obtain optimizing layout scheme.The method considers the optimal selection laying problem of detection equipment own characteristic and test point, internal relation between topological structure according to urban road network and each road section traffic volume parameter, lay the traffic data that as far as possible few detector obtains given accuracy and integrity degree in road network, reduce the cost laid on section needed for RFID detector.

Description

Urban road RFID detector optimizes distribution method

Technical field

The present invention relates to urban highway traffic detector field, be specifically related to a kind of similar based on journey time estimation and flow Property analyze urban road RFID detector optimize distribution method.

Background technology

Intelligent transportation system (ITS) is acknowledged as currently solving China urban traffic congestion and improves the effective of road safety Means.Traffic detector is utilized to collect one of basic function that traffic data is ITS.Existing traffic detector mainly has Ground sense formula detector, microwave detector, Floating Car detector, video detector etc..Through years development, RFID detects Technology is progressively applied to urban highway traffic detection, compares conventional traffic detection technique, and RFID detector has: become This is relatively low, and have read convenient, can accurately track each information of vehicles, the data volume that can store greatly, not by environment The advantages such as impact, security are high and easy to maintenance.

Traditional RFID detector distribution method is mainly having the local detector of laying of traffic conflict, i.e. uses contingency Empirical detector layout method, do not consider detection equipment own characteristic, do not account for test point yet Preferably select laying problem.But with the development in city, city road network density is increasing, if will be on each section Laying RFID detector, required cost will be very surprising.So, the topological structure according to urban road network and Internal relation between each road section traffic volume parameter, research urban road RFID detector optimizes distribution method, at road network The as far as possible few detector of middle laying just can obtain the traffic data of given accuracy and integrity degree, and this is for the friendship based on RFID The construction of logical data collecting system is very important beyond doubt.

Content of the invention

In view of this, the technical problem to be solved is to provide and a kind of divides based on journey time estimation and flow similitude The urban road RFID detector of analysis optimizes distribution method.

The object of the present invention is achieved like this:

The urban road RFID detector that the present invention provides optimizes distribution method, comprises the following steps:

S1: obtain the traffic parameter of each basic road in urban road network；

S2: the journey time that is averaged the basic journey time in the traffic parameter of basic road is estimated and obtains Loop detector layout Number；

S3: carry out flow similarity analysis to the volume of traffic in the traffic parameter of basic road and set up section similar matrix；

S4: set up the Mathematical Modeling that RFID detector optimum is layouted according to section similar matrix and Loop detector layout number,

S5: Mathematical Modeling is solved by genetic algorithm；

S6: obtain RFID detector optimum and layout layout scheme.

Further, the journey time that in described step S2 is averaged the basic journey time in the traffic parameter of basic road is estimated Calculate, according to the average travel time estimated between the upstream and downstream RFID detector calculating upstream and downstream section of section；By the length estimating section Distance ratio between degree and upstream and downstream RFID detector is multiplied by average travel time and obtains Link Travel Time Estimation value；Concrete bag Include following steps:

S21: use below equation to calculate and estimate average travel time for road sections:

$\stackrel{\‾}{t}=\frac{\underset{i=1}{\overset{n}{\Σ}}(t_i^1-t_i^2)}{n}---\left(1\right)$

In formula, n is sample vehicle number,It is the i-th vehicle by time during the first detector,It is that the i-th vehicle passes through second Time during detector；

S22: the ratio between calculating estimation road section length and upstream and downstream RFID detector distance as follows:

$\mathrm{\λ}=\frac{l}{L}---\left(2\right)$

In formula, l is for estimating road section length, and L is upstream and downstream RFID detector distance；

S23: estimation Link Travel Time as follows:

$t_i=\mathrm{\λ}*\stackrel{\‾}{t}---\left(3\right)$

In formula, t_iIt is that the i-th vehicle is by estimating Link Travel Time.

Further, in described step S2, Loop detector layout number determines and specifically includes following steps:

S24: obtain Loop detector layout density straight line, the evaluated error curve of journey time and investment curve；

S25: calculate Loop detector layout density straight line and intersect at A, B 2 point respectively with error curve and investment curve, then described A, The corresponding detector density of B 2 be corresponding reasonable detector density interval (a, b).

S26: according to actual conditions choose reasonable detector density interval (a, b) in numeral as Loop detector layout number.

Further, described step S3 carry out flow similarity analysis to the volume of traffic in the traffic parameter of basic road and set up road Section similar matrix, specifically includes following steps:

S31: obtain the magnitude of traffic flow in the multiple section in city and any two road section traffic volume flows are calculated two basic roads as follows Similarity degree between the section magnitude of traffic flow:

$\mathrm{\ρ}(X,Y)=\frac{\mathrm{cov}(X,Y)}{{\mathrm{\δ}}_X\·{\mathrm{\δ}}_Y}---\left(4\right)$

Wherein,

${\mathrm{\δ}}_Y=\frac{1}{n}\underset{i=1}{\overset{n}{\Σ}}{(Y_i-\stackrel{\‾}{Y})}^2---\left(6\right)$

In formula, X represents the volume of traffic X array of section X, and Y represents the volume of traffic Y array of section Y；ρ (X, Y) represents section Coefficient correlation between X and the Y magnitude of traffic flow；

S32: differentiate whether two basic road magnitudes of traffic flow are similar according to coefficient correlation, if coefficient correlation is more than or equal to phase Like degree index, then it represents that have similitude between two basic road volume of traffic, " 1 " is used to represent similar；

S33: if coefficient correlation is less than index of similarity, then it represents that between two basic road volume of traffic, not there is similitude, adopt Represent similar with " 0 "；

S34: all basic roads carry out similarity analysis between any two to road network, and obtained coefficient correlation similarity is referred to After mark is carried out qualitatively, used the section similar matrix in all sections that " 0 " and " 1 " represent.

Further, described step S4 is set up the Mathematical Modeling that RFID detector optimum layouts as follows:

$\min Z=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i,i=1,2,...,n---\left(7\right)$

$s.t.\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\α}}_{ij}{D}_j\≥1,i,j=1,2,...,n---\left(8\right)$

$\underset{i=1}{\overset{n}{\Σ}}{D}_i=k,i=1,2,...,n---\left(9\right)$

In formula, α_ijFor element value in the similar matrix of section, C_iRepresent the comprehensive cloth dot factor in i section, represent in this section The importance degree layouted, for calculating, based on journey time estimation optimization method, the RFID detector number obtaining；Z represents detector Optimum is layouted minimum of a value；Di represents the test point in the i-th section；N represents section sum.

Further, the iterative process of model is laid in the genetic algorithm for solving urban road RFID detector optimization in described step S5, Specifically include following steps:

S51: initializing, putting genetic algebra T=0, initial population randomly generates, at the beginning of utilizing fitness function evaluation each after coding Begin individual ideal adaptation degree；

S52: select the individuality that fitness is relatively low, rejects the higher individuality of fitness；

S53: application crossover operator and mutation operator generate colony of a new generation, and keep population at individual number m constant；

S54: the fitness of a new generation's population at individual being evaluated, and checks stop criterion, if meeting end condition, then turning To step S55, otherwise T=T+1, forward step S52 to；

S55: jump out, exports optimized individual, it is thus achieved that optimum RF ID Loop detector layout method.

Further, the coding in described step S51, specific as follows:

Basic road in road network is laid test point coding and uses binary coding；If a gene segment table shows what trunk section formed Road network detector layout set, in this set represents a section detector layout situation, and detection is laid in 1 expression section Point, test point is not laid in 0 expression section；In the chromosome of a construction, according to order from left to right, meet constraint bar One gene segment table of part shows a kind of test point selection scheme.

Further, the fitness in described step S54 uses following optimization laying object function to calculate as fitness function:

$f=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i;$

In formula, f represents global minimum；C_iRepresent the comprehensive cloth dot factor in i section, represent the weight layouted in this section Spend；D_iRepresent and whether lay test point in i section.

Further, the stop criterion in described step S54, specifically employing equation below:

$Q\left(T\right)=\underset{t=2}{\overset{T}{\Σ}}|f_{min}^t-f_{min}^{t-1}|---\left(12\right)$

Algorithm end condition: Q (T)-Q (T-1)≤ε；

In formula, Q (T) tends to convergency value for current；Genetic algebra appeared in current calculating for the T,Represent in t generation Minimum fitness value, whereinε represents predetermined threshold value.

The beneficial effects of the present invention is: the present invention uses a kind of road, city based on journey time estimation and flow similarity analysis Road RFID detector optimizes distribution method.The method first passes through the volume of traffic to each basic road in city road network and carries out similar Property analysis and average travel time are estimated, determine the correlation matrix between basic road flow and Loop detector layout number, Then set up, with this, the Mathematical Modeling that RFID detector optimum is layouted, finally by genetic algorithm, model is solved, obtain excellent Change layout scheme.The method considers the optimal selection laying problem of detection equipment own characteristic and test point, according to city Internal relation between the topological structure of city's road network and each road section traffic volume parameter, lays as far as possible few detection in road network Device obtains the traffic data of given accuracy and integrity degree, reduces the cost laid on section needed for RFID detector.

Brief description

In order to make the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing, the present invention is made into one The detailed description of step, wherein:

The optimization distribution method flow chart that Fig. 1 provides for the embodiment of the present invention；

Graph of a relation between detector density that Fig. 2 provides for the embodiment of the present invention and investment, error；

The genetic algorithm that Fig. 3 provides for the embodiment of the present invention solve flow chart；

The RFID simple section artwork that Fig. 4 provides for the embodiment of the present invention；

The coding schematic diagram that Fig. 5 provides for the embodiment of the present invention；

The single-point intersection schematic diagram that Fig. 6 provides for the embodiment of the present invention.

Detailed description of the invention

Hereinafter with reference to accompanying drawing, the preferred embodiments of the present invention are described in detail.It should be appreciated that preferred embodiment is only The explanation present invention, rather than in order to limit the scope of the invention.

Embodiment 1

As shown in Figures 1 to 6, a kind of urban road based on journey time estimation and flow similarity analysis that the present invention provides RFID detector optimizes distribution method；First pass through the volume of traffic to each basic road in city road network carry out similarity analysis and Average travel time is estimated, determines the correlation matrix between basic road flow and Loop detector layout number, then with this Set up the Mathematical Modeling that RFID detector optimum is layouted, finally by genetic algorithm, model is solved, obtain optimizing laying side Case；Specifically include following steps:

S1: obtain the traffic parameter of each basic road in urban road network；

S2: the journey time that is averaged the basic journey time in the traffic parameter of basic road is estimated and obtains Loop detector layout Number；

S3: carry out flow similarity analysis to the volume of traffic in the traffic parameter of basic road and set up section similar matrix；

S4: set up the Mathematical Modeling that RFID detector optimum is layouted according to section similar matrix and Loop detector layout number,

S5: Mathematical Modeling is solved by genetic algorithm；

S6: obtain RFID detector optimum and layout layout scheme.

The journey time that in described step S2 is averaged the basic journey time in the traffic parameter of basic road is estimated, according to Estimate that section upstream and downstream RFID detector calculates the average travel time between upstream and downstream section；By the length estimating section and upstream and downstream Distance ratio between RFID detector is multiplied by average travel time and obtains Link Travel Time Estimation value；Specifically include following steps:

S21: use below equation to calculate and estimate average travel time for road sections:

$\stackrel{\‾}{t}=\frac{\underset{i=1}{\overset{n}{\Σ}}(t_i^1-t_i^2)}{n}---\left(1\right)$

In formula, n is sample vehicle number,It is the i-th vehicle by time during the first detector,It is that the i-th vehicle passes through second Time during detector；

S22: the ratio between calculating estimation road section length and upstream and downstream RFID detector distance as follows:

$\mathrm{\λ}=\frac{l}{L}---\left(2\right)$

In formula, l is for estimating road section length, and L is upstream and downstream RFID detector distance；

S23: estimation Link Travel Time as follows:

$t_i=\mathrm{\λ}*\stackrel{\‾}{t}---\left(3\right)$

In formula, t_iIt is that the i-th vehicle is by estimating Link Travel Time.

In described step S2, Loop detector layout number determines and specifically includes following steps:

S24: obtain Loop detector layout density straight line, the evaluated error curve of journey time and investment curve；

S25: calculate Loop detector layout density straight line and intersect at A, B 2 point respectively with error curve and investment curve, then described A, The corresponding detector density of B 2 be corresponding reasonable detector density interval (a, b).

S26: according to actual conditions choose reasonable detector density interval (a, b) in numeral as Loop detector layout number.

The volume of traffic in the traffic parameter of basic road is carried out flow similarity analysis by described step S3 and to set up section similar Matrix, specifically includes following steps:

S31: obtain the magnitude of traffic flow in the multiple section in city and any two road section traffic volume flows are calculated two basic roads as follows Similarity degree between the section magnitude of traffic flow:

$\mathrm{\ρ}(X,Y)=\frac{\mathrm{cov}(X,Y)}{{\mathrm{\δ}}_X\·{\mathrm{\δ}}_Y}---\left(4\right)$

Wherein,

${\mathrm{\δ}}_Y=\frac{1}{n}\underset{i=1}{\overset{n}{\Σ}}{(Y_i-\stackrel{\‾}{Y})}^2---\left(6\right)$

In formula, X represents the volume of traffic X array of section X, and Y represents the volume of traffic Y array of section Y；ρ (X, Y) represents section Coefficient correlation between X and the Y magnitude of traffic flow；

S32: differentiate whether two basic road magnitudes of traffic flow are similar according to coefficient correlation, if coefficient correlation is more than or equal to phase Like degree index, then it represents that have similitude between two basic road volume of traffic, " 1 " is used to represent similar；

S33: if coefficient correlation is less than index of similarity, then it represents that between two basic road volume of traffic, not there is similitude, adopt Represent similar with " 0 "；

S34: all basic roads carry out similarity analysis between any two to road network, and obtained coefficient correlation similarity is referred to After mark is carried out qualitatively, used the section similar matrix in all sections that " 0 " and " 1 " represent.

Described step S4 is set up the Mathematical Modeling that RFID detector optimum layouts as follows:

$\min Z=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i,i=1,2,...,n---\left(7\right)$

$s.t.\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\α}}_{ij}{D}_j\≥1,i,j=1,2,...,n---\left(8\right)$

$\underset{i=1}{\overset{n}{\Σ}}{D}_i=k,i=1,2,...,n---\left(9\right)$

In formula, α_ijFor element value in the similar matrix of section, C_iRepresent the comprehensive cloth dot factor in i section, represent in this section The importance degree layouted, for calculating, based on journey time estimation optimization method, the RFID detector number obtaining.

The iterative process of model is laid in genetic algorithm for solving urban road RFID detector optimization in described step S5, specifically wraps Include following steps:

S51: initializing, putting genetic algebra T=0, initial population randomly generates, at the beginning of utilizing fitness function evaluation each after coding Begin individual ideal adaptation degree；

S52: select the individuality that fitness is relatively low, rejects the higher individuality of fitness；

S53: application crossover operator and mutation operator generate colony of a new generation, and keep population at individual number m constant；

S54: the fitness of a new generation's population at individual being evaluated, and checks stop criterion, if meeting end condition, then turning To step S55, otherwise T=T+1, forward step S52 to；

S55: jump out, exports optimized individual, it is thus achieved that optimum RF ID Loop detector layout method.

Coding in described step S51, specific as follows:

Basic road in road network is laid test point coding and uses binary coding；If a gene segment table shows what trunk section formed Road network detector layout set, in this set represents a section detector layout situation, and detection is laid in 1 expression section Point, test point is not laid in 0 expression section；In the chromosome of a construction, according to order from left to right, meet constraint bar One gene segment table of part shows a kind of test point selection scheme.

Fitness in described step S54 uses following optimization to lay object function and calculates as fitness function:

$f=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i;$

In formula, f represents global minimum；C_iRepresent the comprehensive cloth dot factor in i section, represent the weight layouted in this section Spend；D_iRepresent and whether lay test point in i section.

Stop criterion in described step S54, specifically employing equation below:

$Q\left(T\right)=\underset{t=2}{\overset{T}{\Σ}}|f_{min}^t-f_{min}^{t-1}|---\left(12\right)$

Algorithm end condition: Q (T)-Q (T-1)≤ε；

In formula, Q (T) tends to convergency value for current；Genetic algebra appeared in current calculating for the T,Represent in t generation Minimum fitness value, whereinε represents predetermined threshold value.

Embodiment 2

The present embodiment differs only in embodiment 1:

Lay spacing there is certain relation based on the Link Travel Time Estimation and RFID detector of RFID detector data, detection The density that device is laid is bigger, and detected data more can reflect road network traffic flow operation characteristic exactly, same journey time Evaluated error reduces as well as the increase of Loop detector layout density, as shown in Figure 1.But it is as the increase of detector density Corresponding cost also can linear rise, in the case of so rational investment and error should be considered, select rational detector Layout density.

The relation of Loop detector layout density, investment and error three is as shown in Figure 1.Fig. 1 is detector density and investment, error Between relation；As can be seen from Figure 1, with the increase of detector density invest accordingly also can linear rise, examine so comprehensive Considering rational investment and in the case of error, this straight line and error curve and investment curve meet at A, B 2 point respectively, and 2 corresponding Detector density is that (what detector density institute no matter a b), choose right on this interval in corresponding reasonable detector density interval The Travel Time Error answered and investment are all in the range of can receiving.In actual application, if evaluated error can be allowed Determine, then Loop detector layout density selects close proximity to the border a in above-mentioned interval, thus to reduce investment.

It based on the Link Travel Time Estimation method of RFID detector is: first according to estimation section upstream and downstream RFID detector Calculate the average travel time between upstream and downstream section, rear with the distance ratio between the length estimating section and upstream and downstream RFID detector Value is multiplied by average travel time then can obtain Link Travel Time Estimation value.

The method of the present embodiment is applicable to the planning of city road network detector and layouts, and its scope of application is to need certain city layouted many There is between the magnitude of traffic flow in individual section certain similitude.

Just it is believed that there is linear dependence, so between two link flows when index of similarity α refers to that coefficient correlation is much Just can quantitative coefficient correlation qualitatively.Index of similarity determine that have between two basic road volume of traffic similitude or Not there is the threshold values of similitude." 1 " represents similar, and " 0 " represents dissmilarity.The determination of index of similarity and data acquisition system The precision of data to be gathered of uniting is relevant.If the data precision requiring is high, then index of similarity to be arranged greatly, otherwise As the same.

For key road segment, detector can be laid in this section and directly gather data, to improve the Data Detection essence of key road segment Degree.For the neighbouring section having auxiliary facility (communication line, mounting bracket), lay detector in this section, in order to can fill Divide and utilize existing resource, reduce deployment cost.

Determine C_iMethod be: if it is considered to certain section important (being such as major trunk roads or a traffic particular location), Then make C_iTaking less, vice versa.If it is considered to all of section is of equal importance, then make C_i=1.

During genetic algorithm for solving, an initial population can be extracted at the beginning of m by random from the feasible zone meet constraints Beginning test point selection scheme (m chromosome), i.e. feasible is deconstructed into, and wherein each feasible solution is referred to as a parent.

During genetic algorithm for solving, calculate each ideal adaptation degree functional value in current population, and press fitness function value row Row population at individual order, if the fitness of the optimized individual in current group is higher than the fitness of best individuality up to now, The then new preferably individual optimized individual making in current group of being changed so far, otherwise, with fitness up to now the highest Body replaces the minimum individuality of the fitness in current group.

During genetic algorithm for solving, the present embodiment uses single-point cross method, and as shown in Figure 6, Fig. 6 is that the present invention implements The single-point intersection schematic diagram that example provides；First two father's strings of A and B are chosen by roulette and optimal save strategy method choice mechanism, so After randomly choose a cross-point locations, the individual M of father and the individual N of father exchanges being positioned at the portion gene code on the right side of crosspoint, shape The individual M ' of son of Cheng Xin and the individual N ' of son, what same method was similar completes the intersection operation of other individualities.

During genetic algorithm for solving, crossing operation can not produce new gene, can only carry out permutation and combination to existing gene. And mutation operator is some genic value changing individual in population string.Mutation operation is to negate some gene for two-value sequence Genic value on seat, namely 1 → 0 or 0 → 1.

Through repeatedly genetic algorithm for solving, it is possible to obtain the many groups of optimization layout schemes meeting constraints, can answer as in reality Alternative in, prevents the scheme that makes a difference of special circumstances from implementing.

Finally illustrate, above example only in order to technical scheme being described and unrestricted, although by referring to Invention has been described for the preferred embodiments of the present invention, it should be appreciated by those of ordinary skill in the art that can To make various change, the spirit and scope being limited without departing from the present invention in the form and details to it.

Claims (8)

1. urban road RFID detector optimizes distribution method, it is characterised in that: comprise the following steps:

S1: obtain the traffic parameter of each basic road in urban road network；

S2: the journey time that is averaged the basic journey time in the traffic parameter of basic road is estimated and obtains Loop detector layout Number；

S3: carry out flow similarity analysis to the volume of traffic in the traffic parameter of basic road and set up section similar matrix；

S4: set up the Mathematical Modeling that RFID detector optimum is layouted according to section similar matrix and Loop detector layout number,

S5: Mathematical Modeling is solved by genetic algorithm；

S6: obtain RFID detector optimum and layout layout scheme；

In described step S2, Loop detector layout number determines and specifically includes following steps:

S24: obtain Loop detector layout density straight line, the evaluated error curve of journey time and investment curve；

S25: calculate Loop detector layout density straight line and intersect at A, B 2 point respectively with error curve and investment curve, then described A, The corresponding detector density of B 2 be corresponding reasonable detector density interval (a, b)；

S26: according to actual conditions choose reasonable detector density interval (a, b) in numeral as Loop detector layout number.

2. urban road RFID detector according to claim 1 optimizes distribution method, it is characterised in that: described step The journey time that in S2 is averaged the basic journey time in the traffic parameter of basic road is estimated, upper and lower according to estimation section Trip RFID detector calculates the average travel time between upstream and downstream section；With the length estimating section and upstream and downstream RFID detector Between distance ratio be multiplied by average travel time obtain Link Travel Time Estimation value；Specifically include following steps:

S21: use below equation to calculate and estimate average travel time for road sections:

$\stackrel{\‾}{t}=\frac{\underset{i=1}{\overset{n}{\Σ}}(t_i^1-t_i^2)}{n}---\left(1\right)$

In formula, n is sample vehicle number,It is the i-th vehicle by time during the first detector,It is that the i-th vehicle passes through second Time during detector；

S22: the ratio between calculating estimation road section length and upstream and downstream RFID detector distance as follows:

$\mathrm{\λ}=\frac{l}{L}---\left(2\right)$

In formula, l is for estimating road section length, and L is upstream and downstream RFID detector distance；

S23: estimation Link Travel Time as follows:

$t_i=\mathrm{\λ}*\stackrel{\‾}{t}---\left(3\right)$

In formula, t_lIt is that the i-th vehicle is by estimating Link Travel Time.

3. urban road RFID detector according to claim 1 optimizes distribution method, it is characterised in that: described step The volume of traffic in the traffic parameter of basic road carried out flow similarity analysis by S3 and sets up section similar matrix, specifically including Following steps:

S31: obtain the magnitude of traffic flow in the multiple section in city and any two road section traffic volume flows are calculated two basic roads as follows Similarity degree between the section magnitude of traffic flow:

$\mathrm{\ρ}(X,Y)=\frac{\mathrm{cov}(X,Y)}{{\mathrm{\δ}}_X\·{\mathrm{\δ}}_Y}---\left(4\right)$

Wherein,

${\mathrm{\δ}}_Y=\frac{1}{n}\underset{i=1}{\overset{n}{\Σ}}{(Y_i-\stackrel{\‾}{Y})}^2---\left(6\right)$

In formula, X represents the volume of traffic X array of section X, and Y represents the volume of traffic Y array of section Y；ρ (X, Y) represents section Coefficient correlation between X and the Y magnitude of traffic flow；

S32: differentiate whether two basic road magnitudes of traffic flow are similar according to coefficient correlation, if coefficient correlation is more than or equal to phase Like degree index, then it represents that have similitude between two basic road volume of traffic, " 1 " is used to represent similar；

S33: if coefficient correlation is less than index of similarity, then it represents that between two basic road volume of traffic, not there is similitude, adopt Represent similar with " 0 "；

S34: all basic roads carry out similarity analysis between any two to road network, and obtained coefficient correlation similarity is referred to After mark is carried out qualitatively, used the section similar matrix in all sections that " 0 " and " 1 " represent.

4. urban road RFID detector according to claim 1 optimizes distribution method, it is characterised in that: described step S4 sets up the Mathematical Modeling that RFID detector optimum layouts as follows:

$\min Z=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i,i=1,2,...,n---\left(7\right)$

$s.t.\underset{j=1}{\overset{n}{\Σ}}{\mathrm{\α}}_{ij}{D}_j\≥1,i,j=1,2,...,n---\left(8\right)$

$\underset{i=1}{\overset{n}{\Σ}}{D}_i=k,i=1,2,...,n---\left(9\right)$

In formula, α_ijFor element value in the similar matrix of section, C_iRepresent the comprehensive cloth dot factor in i section, represent in this section The importance degree layouted, k is for calculating, based on journey time estimation optimization method, the RFID detector number obtaining；Z represents detector Optimum is layouted minimum of a value；Di represents the test point in the i-th section；N represents section sum.

5. urban road RFID detector according to claim 1 optimizes distribution method, it is characterised in that: described step The iterative process of model is laid in genetic algorithm for solving urban road RFID detector optimization in S5, specifically includes following steps:

S51: initializing, putting genetic algebra T=0, initial population randomly generates, at the beginning of utilizing fitness function evaluation each after coding Begin individual ideal adaptation degree；

S52: select the individuality that fitness is relatively low, rejects the higher individuality of fitness；

S53: application crossover operator and mutation operator generate colony of a new generation, and keep population at individual number m constant；

S54: the fitness of a new generation's population at individual being evaluated, and checks stop criterion, if meeting end condition, then turning To step S55, otherwise T=T+1, forward step S52 to；

S55: jump out, exports optimized individual, it is thus achieved that optimum RF ID Loop detector layout method.

6. urban road RFID detector according to claim 5 optimizes distribution method, it is characterised in that: described step Coding in S51, specific as follows:

Basic road in road network is laid test point coding and uses binary coding；If a gene segment table shows what trunk section formed Road network detector layout set, in this set represents a section detector layout situation, and detection is laid in 1 expression section Point, test point is not laid in 0 expression section；In the chromosome of a construction, according to order from left to right, meet constraint bar One gene segment table of part shows a kind of test point selection scheme.

7. urban road RFID detector according to claim 5 optimizes distribution method, it is characterised in that: described step Fitness in S54 uses following optimization to lay object function and calculates as fitness function:

$f=\underset{i=1}{\overset{n}{\Σ}}{C}_i{D}_i;$

In formula, f represents global minimum；C_iRepresent the comprehensive cloth dot factor in i section, represent the weight layouted in this section Spend；D_iRepresent and whether lay test point in i section.

8. urban road RFID detector according to claim 5 optimizes distribution method, it is characterised in that: described step Stop criterion in S54, specifically employing equation below:

$Q\left(T\right)=\underset{t=2}{\overset{T}{\Σ}}|f_{min}^t-f_{min}^{t-1}|---\left(12\right)$

Algorithm end condition: Q (T)-Q (T-1)≤ε；

In formula, Q (T) tends to convergency value for current；Genetic algebra appeared in current calculating for the T,Represent in t generation Minimum fitness value, whereinε represents predetermined threshold value.