CN100468481C - Intelligent analysis system for municipal traffic journey time - Google Patents

Abstract

This invention relates to one city traffic journey time intelligent analysis system, which comprises each road catch identification device in the city traffic internet and intelligent servo with each catch device connected with intelligent servo, wherein, the servo comprises city traffic network topological structure module, car catch identification module, road section even drive time computation module, break judgment module to establish road topological structure and OD matrix relationship; due to number information from catch device, it uses road list to store router topological structure.

Description

Intelligent analysis system for municipal traffic journey time

(1) technical field

The present invention relates to urban transportation intellectualizing system field, especially a kind of urban transportation journey time analytic system.

(2) background technology

Intelligent traffic system utilizes advanced infotech and network technology, and original road is fully utilized, and increases the movement capacity of traffic, shortens haulage time, saves the energy.On the other hand, intelligent transportation relies on various advanced control devices and method, improves travel safety, and reliability reduces accident rate greatly, reduces personnel's injures and deaths and vehicle loss, also brings social benefit and economic benefit.

On this complicated road network of urban road, present domestic proposition utilize least square method and based on the prediction of the dynamic route journey time of fuzzy comprehensive evoluation, all be only applicable to simple path and traffic state is had certain restriction, be difficult to be applied to actual road network and get on.The judgement that for example utilizes least square method to carry out journey time is only applicable to the track stability of flow or changes situation relatively uniformly, and can not be applied to the interwoven region highway section; Though and considered the running time of vehicle and traffic time-delay two parts time to have proposed a series of hypothesis based on the dynamic route journey time Forecasting Methodology of fuzzy comprehensive evoluation: 1) to enter the time in highway section be separate to each vehicle; 2) suppose to be in when waiting for red light phase, arrive this crossing adding and wait for that the vehicle stand-by period obedience parameter of troop is the Poisson distribution of K at each crossing; 3) vehicle can not stop because of meeting red light reason in addition in the process of moving.Therefore this brings all restrictions to practical application.

(3) summary of the invention

The constraint that brings for the simple consideration vehicle flowrate that overcomes existing urban transportation intellectualizing system, be only applicable to simple path, deficiency poor for applicability, the invention provides a kind of based on the OD matrix, the intelligent analysis system for municipal traffic journey time that consider constraint that vehicle flowrate, journey time bring simultaneously, can be applied to the city complex network, applicability is good.

The technical solution adopted for the present invention to solve the technical problems is:

A kind of intelligent analysis system for municipal traffic journey time comprises the candid photograph identification equipment at each crossing that is installed in urban traffic network, intelligent server, and described each captured identification equipment and connected intelligent server; Described intelligent server comprises: urban traffic network topological structure module, be used for setting up the road network topology structure according to each crossing and the annexation thereof of urban traffic network, set the road speed limit in stroke between the interconnective crossing and this highway section, and set up the corresponding relation of road network topology structure and OD matrix; The vehicle snapshot identification module is used for when vehicle passes through the crossing, sends the candid photograph instruction to capturing identification equipment, and reads the data of capturing identification equipment, the license plate number and the candid photograph time of writing down this vehicle; Road-section average running time computing module, be used for according to the road network topology structure, by passing through the vehicle at this crossing in a period of time before the license plate number statistics current time, the vehicle coupling of passing through with interconnective crossing again, find the identical vehicle of license plate number, this vehicle was subtracted each other through the time at two different crossings, obtain the running time at two crossings; Discrimination module violating the regulations, be used for running time according to described two crossings, and according to the stroke and the road speed limit at two heavy crossings of net topology structure, calculate the average overall travel speed of vehicle in this highway section, comparison road speed limit, bigger as average overall travel speed than road speed limit, be judged to be violating the regulationsly, and write down the license plate number of this vehicles peccancy, time violating the regulations and highway section violating the regulations.

As preferred a kind of scheme: described intelligent server also comprises: query script violating the regulations, be used for record according to logging modle violating the regulations, set up the corresponding sequence of time violating the regulations, highway section violating the regulations and the license plate number of vehicles peccancy, and set up the query pattern of this sequence.

As preferred another kind of scheme: described intelligent server also comprises: the flow rate calculation module, be used for the vehicle flowrate at each crossing being added up according to the vehicle snapshot identification module, and according to the vehicle flowrate of timeslice record statistics.

As preferred another scheme: described intelligent server also comprises: the graph inquiring module, be used for road network topology structure and electronic chart are set up corresponding relation, during the adjacent crossing of on clicking electronic chart two, can carry out query display to the relevant telecommunication flow information of this adjacent intersection.

Technical conceive of the present invention is: OD (Origin-Destination) matrix (the OD magnitude of traffic flow (ODtrafficflow)), be to describe the trip traffic quantity between all Origin And Destinations in the transportation network, reflected the basic demand of user to transportation network, it is the important evidence of carrying out Transportation Network Planning and traffic administration.The OD matrix has then reflected time dependent transport need pattern in the specific period; It is on this basis existing OD data to be handled again that OD analyzes, thereby excavates the process of profound information.The OD journey time analytic system that native system is developed with regard to being based on this purpose based on urban road.

Because there is a large amount of uncertainties in the Dynamic OD Matrix data, analyzes to OD simultaneously and also bring great uncertainty.The method that the present invention takes is to become when a large amount of on the basis of OD matrix data, with the certain hour sheet is unit, with the volume of traffic between each crossing that is studied is research object, in the section Dynamic OD Matrix is considered as static OD matrix at this moment, data are wherein handled, thus the result that the approximate overall process Dynamic OD of match is analyzed.Here key factor is choosing of timeslice and the analysis of the OD data of being obtained being carried out traffic parameter according to timeslice.

We carry out choosing of timeslice according to the historical data at each crossing at this.Because traffic flow exists periodically, by mating historical OD matrix, obtain target OD in a traffic flow cycle, (being generally a week) with certain vehicle flowrate as threshold value, handle required timeslice as following one-period with this through the required minimum interval of this vehicle flowrate.Therefore seem very important as the choosing of wagon flow value of threshold value here, chose senior general and directly cause the change of timeslice big, can influence the precision of analyzing the analysis of match Dynamic OD with static OD; Chose the expense that the young pathbreaker causes system handles and became big, to a certain degree also will cause down " imperial lattice phenomenon " generation---along with dwindling of timeslice, error can enlarge on the contrary.

Below we with any two OD in the OD matrix to being example, the specific implementation process is:

Definition: OD pair set { (O _kD _k) | k=1,2 ..., the history of m} is captured record set and is $O_k=\left(\begin{array}{cc}{x_{11}}^{\left(k\right)}& {t_{11}}^{\left(k\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {x_{1n}}^{\left(k\right)}& {t_{1n}}^{\left(k\right)}\end{array}\right),$ $D_k=\left(\begin{array}{cc}{x_{21}}^{\left(k\right)}& {t_{21}}^{\left(k\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {x_{2n}}^{\left(k\right)}& {t_{2n}}^{\left(k\right)}\end{array}\right),$ Here x _Ij ^(k)Represent k (k=1,2) group OD to the j that is positioned at starting point (i=1) or terminal point (i=2) (j=1 ..., the n) signature identification of group vehicle, t _Ij ^(k)Be the candid photograph time of correspondence, (x _Ij ^(k)t _Ij ^(k)) key-value pair of formation.

Definition: the real-time OD data matrix of road network $A=\left(\begin{array}{ccc}{x}_1& t_1& s_1\\ \·& \·& \·\\ \·& \·& \·\\ \·& \·& \·\\ x_n& t_n& s_n\end{array}\right),$ X wherein _i(i=1 ..., n) be vehicle characteristics information, t _i(i=1 ..., n) for capturing temporal information, s _i(i=1 ..., n) for capturing the characteristic information in place.

1) with OD matrix (O ₁, D ₁), (O ₂, D ₂) in the key-value pair deposited mate to remove redundancy and garbage wherein, to obtain new matrix and be $\stackrel{\‾}{O_1}=\left(\begin{array}{cc}{\stackrel{\‾}{x_{11}}}^{\left(1\right)}& {\stackrel{\‾}{t_{11}}}^{\left(1\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {\stackrel{\‾}{x_{1m}}}^{\left(1\right)}& {\stackrel{\‾}{t_{1m}}}^{\left(1\right)}\end{array}\right),$ $\stackrel{\‾}{D_1}=\left(\begin{array}{cc}{\stackrel{\‾}{x_{21}}}^{\left(1\right)}& {\stackrel{\‾}{t_{21}}}^{\left(1\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {\stackrel{\‾}{x_{2m}}}^{\left(1\right)}& {\stackrel{\‾}{t_{2m}}}^{\left(1\right)}\end{array}\right),$ $\stackrel{\‾}{O_2}=\left(\begin{array}{cc}{\stackrel{\‾}{x_{11}}}^{\left(2\right)}& {\stackrel{\‾}{t_{11}}}^{\left(2\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {\stackrel{\‾}{x_{1h}}}^{\left(2\right)}& {\stackrel{\‾}{t_{1h}}}^{\left(2\right)}\end{array}\right),$ $\stackrel{\‾}{D_2}=\left(\begin{array}{cc}{\stackrel{\‾}{x_{21}}}^{\left(2\right)}& {\stackrel{\‾}{t_{21}}}^{\left(2\right)}\\ \·& \·\\ \·& \·\\ \·& \·\\ {\stackrel{\‾}{x_{2h}}}^{\left(2\right)}& {\stackrel{\‾}{t_{2h}}}^{\left(2\right)}\end{array}\right),$ Satisfy condition:

m≤n，h≤n

${\stackrel{\&OverBar;}{x_{1i}}}^{\left(1\right)}={\stackrel{\&OverBar;}{x_{2i}}}^{\left(1\right)}\&Element;\left\{{x_{1i}}^{\left(1\right)}\right|i=1,...m\}\&cap;\left\{{x_{2i}}^{\left(1\right)}\right|i=1,...m\},$ ${\stackrel{\&OverBar;}{t_{1i}}}^{\left(1\right)}<{\stackrel{\&OverBar;}{t_{2i}}}^{\left(1\right)};$ ${\stackrel{\&OverBar;}{x_{1i}}}^{\left(2\right)}={\stackrel{\&OverBar;}{x_{2i}}}^{\left(2\right)}\&Element;\left\{{x_{1i}}^{\left(2\right)}\right|i=1,...h\}\&cap;\left\{{x_{2i}}^{\left(2\right)}\right|i=1,...h\},$ ${\stackrel{\&OverBar;}{t_{1i}}}^{\left(2\right)}<{\stackrel{\&OverBar;}{t_{2i}}}^{\left(2\right)};$

2) establishing threshold value is p car, m≤h, and three kinds of situations of following branch:

As p〉during h, show that OD is to (O ₁, D ₁), (O ₂, D ₂) do not satisfy threshold condition; Withdraw from algorithm this moment.

When p ∈ (m, h] time, show and have only OD (O ₂, D ₂) satisfy threshold condition, this moment from

Middle random choose goes out p corresponding record, obtains $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{O_2}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{11}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{11}}}^{\left(2\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{1p}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1p}}}^{\left(2\right)}\end{array}\right),$ $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{D_2}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{21}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{21}}}^{\left(2\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{2p}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2p}}}^{\left(2\right)}\end{array}\right);$

When p≤m, show that two groups of OD are to (O ₁, D ₁), (O ₂, D ₂) all satisfy threshold condition, this moment from

Middle random choose goes out p corresponding record, and the matrix of consequence after obtaining intercepting is designated as $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{O_1}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{11}}}^{\left(1\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{11}}}^{\left(1\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{1p}}}^{\left(1\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1p}}}^{\left(1\right)}\end{array}\right),$ $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{D_1}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{21}}}^{\left(1\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{21}}}^{\left(1\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{2p}}}^{\left(1\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2p}}}^{\left(1\right)}\end{array}\right)$ With $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{O_2}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{11}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{11}}}^{\left(2\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{1p}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1p}}}^{\left(2\right)}\end{array}\right),$ $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{D_2}=\left(\begin{array}{cc}{\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{21}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{21}}}^{\left(2\right)}\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;\\ {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{x_{2p}}}^{\left(2\right)}& {\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2p}}}^{\left(2\right)}\end{array}\right)$

3) if p ∈ (m, h], obtain

With

Earliest time and time poor the latest, promptly establish

Earliest time is $t_{o_2}=\min \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1i}}}^{\left(2\right)}\right),$

Time is the latest $t_{D_2}=\max \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2i}}}^{\left(2\right)}\right)$ (i=1 ..., p), both earliest times and the mistiming is the latest $t=t_{D_2}\_t_{o_2}$ , this moment, timeslice was Δ t=t; When p≤m, then calculate above-mentioned

With

With

Earliest time and time poor the latest,

Earliest time is $t_{o_1}=\min \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1i}}}^{\left(1\right)}\right),$ Time is the latest $t_{D_1}=\max \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2i}}}^{\left(1\right)}\right),$

Earliest time is $t_{o_2}=\min \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{1i}}}^{\left(2\right)}\right),$

Time is the latest $t_{D_2}=\max \left({\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{t_{2i}}}^{\left(2\right)}\right)$ (i=1 ..., p), then both mistimings are respectively $t_1=t_{D_1}\_t_{o_1},$ $t_2=t_{D_2}\_t_{o_2};$ In order to satisfy threshold value for passing through the condition of p car at least, then the select time sheet is Δ t=max (t ₁, t ₂).

After obtaining the required timeslice of corresponding analysis, will capture the Dynamic OD Matrix data that identification equipment obtains by front end and divide with this timeslice, at this moment between in the section, the method that adopts static OD to analyze is analyzed it.The vehicle snapshot data that relevant crossing is obtained are added up ordering, utilize the correlativity at crossing to carry out finding the solution of journey time, and to the abnormal data screening and filtering; Call for subsequent module.

According to above institute obtain timeslice Δ t, the algorithm flow of trying to achieve Link Travel Time is as follows:

Definition: Link Travel Time processing array $C=\left(\begin{array}{ccc}{x}_1& \stackrel{\&OverBar;}{T_1}& s_i\&RightArrow;s_j\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_n& \stackrel{\&OverBar;}{T_n}& s_k\&RightArrow;s_n\end{array}\right),$ X wherein _i(i=1 ..., n) be vehicle characteristics information,

Be the candid photograph mistiming in corresponding two candid photograph places, s _i→ s _j(i=1 ..., n; J=1 ..., n) capture the place characteristic information for doing two of the mistiming, and s _iThe candid photograph time at place is prior to s _j

1) according to the initial time t that system provided, be the processing cycle with a timeslice Δ t, the Dynamic OD data are carried out data screening, the purpose in this step is to select field of definition to be processed.The for example a certain period by the Dynamic OD data that the crossing fiber optic sends over is $A=\left(\begin{array}{ccc}{x}_1& t_1& s_1\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_n& t_n& s_n\end{array}\right),$ Then the data after the screening are: $\stackrel{\&OverBar;}{R}=\left(\begin{array}{ccc}{x}_1& t_1& s_1\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_m& t_m& s_m\end{array}\right),$ (m≤n，t≤t _i≤t+Δt)

2) there is bigger redundancy according to the OD data that the time period filtered out, for example there is the branch road in two adjacent crossings or because other various factors, there is the bigger phenomenon that do not match in the information of vehicles of two crossings record, before entering next step processing, must earlier these data be carried out the vehicles identifications information matches, single or abnormal data removal with those, final acquisition is carried out journey time and is calculated required data.The algorithm that is adopted is as follows:

◆ establish x=x ₁

◆ with x traversal record collection R successively

◆ when $\&Exists;x_i=x,$ Then with x and x _iResult set R charged in corresponding record, and corresponding record value among the deletion R, if there is unique record in R, then forwards the v step to, otherwise record among the R is renumberd, and owner record is designated as x ₁, forward the i step to.

◆ when $\&ForAll;x_i\&NotEqual;x,$ The record of x correspondence among the deletion R if there is unique record in R, then forwards the V step to, otherwise record among the R is renumberd, and owner record is designated as x ₁, change the i step over to

◆ finish

The result set of last gained is: $R=\left(\begin{array}{ccc}{x}_1& t_1& s_1\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_h& t_h& s_h\end{array}\right)$ (h≤m, and vehicles identifications x wherein _iThere is a heavily value at least in (i ∈ [1, n]))

3) the OD data sample R that the 2nd step coupling is obtained carries out the judgement of journey time.Because the x among the R _iThere is heavily value, supposes x _iThe tuple of=u is k, and u corresponding k bar in R is designated as $R_i=\left(\begin{array}{ccc}u& t_i& s_i\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ u& t_{i+k-1}& s_{i+k-1}\end{array}\right),$ Its record is poor in twos by time domain, and then the result is total

The gained matrix of consequence is $C_i=\left(\begin{array}{ccc}u& T_i& s_i\&RightArrow;s_i\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ u& T_{i+C_k^2-1}& s_{i+k-1}\&RightArrow;s_{i+k-1}\end{array}\right)$

4) at this moment, need utilize the crossing syntople of road network right

Further screen, the form after the screening is $\stackrel{\&OverBar;}{C_i}=\left(\begin{array}{ccc}u& \stackrel{\&OverBar;}{T_1}& s_i\&RightArrow;s_j\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ u& \stackrel{\&OverBar;}{T_m}& s_k\&RightArrow;s_n\end{array}\right),$ Satisfy condition: $\stackrel{\&OverBar;}{T_i}>0$ (0＜i＜m), and s _iWith s _j, s _kWith s _nAdjacent

5) circulation the 3rd), 4) step, obtain the journey time matrix of consequence of respectively organizing data

(1≤i≤h), between the crossing of each vehicle that draws journey time as a result battle array be expressed as with partitioned matrix $\stackrel{\&OverBar;}{C}=\left(\begin{array}{c}\stackrel{\&OverBar;}{C_1}\\ \stackrel{\&OverBar;}{C_2}\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ \stackrel{\&OverBar;}{C_{h-1}}\\ \stackrel{\&OverBar;}{C_h}\end{array}\right).$

6),, still need the result set among the C is weighted average treatment according to the highway section to the time in order to obtain the journey time in whole highway section because the data among the C are the journey time results at the single car sample.Get that road section information is s among the C _i→ s _jRecord set be $D_i=\left(\begin{array}{ccc}{x}_m& \stackrel{\&OverBar;}{T_m}& s_i\&RightArrow;s_j\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_n& \stackrel{\&OverBar;}{T_n}& s_i\&RightArrow;s_j\end{array}\right),$ According to vehicles identifications type x _iDifference, give certain weight α to respective record _iAt this moment $D_i=\left(\begin{array}{cccc}{x}_m& \stackrel{\&OverBar;}{T_m}& s_i\&RightArrow;s_j& {\mathrm{\&alpha;}}_m\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;& \&CenterDot;\\ x_n& \stackrel{\&OverBar;}{T_n}& s_i\&RightArrow;s_j& {\mathrm{\&alpha;}}_n\end{array}\right),$ At this moment, be weighted the x of average gained by following formula _i→ s _jThe road-section average journey time be: $\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{T_{\mathrm{ij}}}=\underset{k=m}{\overset{n}{\mathrm{\&Sigma;}}}{\stackrel{\&OverBar;}{T}}_k{\mathrm{\&alpha;}}_k$ , the D of this moment _iOnly comprise road section information, be designated as $D_i=\left(\begin{array}{cc}\stackrel{\stackrel{\&OverBar;}{\&OverBar;}}{T_{\mathrm{ij}}}& s_i\&RightArrow;s_j\end{array}\right),$ Calculate the journey time in other each highway section with this, must result set be $C=\left(\begin{array}{c}{D}_1\\ D_2\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ D_{h-1}\\ D_h\end{array}\right).$

Beneficial effect of the present invention mainly shows: 1, owing to adopted candid photograph equipment to obtain the number plate information of vehicle, and utilize link table to deposit the topological structure of road network, as compared with the past merely based on the theory analysis method difference, this system can be applied to complicated city road network, and can dynamically increase circuit node on the backstage; 2, because based on capturing identification equipment, what this place was paid close attention to is the candid photograph information at crossing, no longer the situation of vehicle on road is done too much requirement, therefore can effectively avoid the requirement for restriction that proposes in above-mentioned two methods to vehicle flowrate; 3, easy and simple to handle, based on B S development mode, any on-line customer only needs browser promptly addressable; Expansibility is strong, is convenient to subsequent extension; 4, dynamic timeslice technology can on-the-fly modify the timeslice of processing according to historical traffic flow situation of change; 5, have the data export function, can provide the data necessary source interface the prediction of obtaining with busy route of follow-up typical traffic route.

(4) description of drawings

Fig. 1 is the functional block diagram of intelligent analysis system for municipal traffic journey time.

Fig. 2 is the processing flow chart of intelligent analysis system for municipal traffic journey time.

Fig. 3 is the process flow diagram that calculates the road-section average running time.

Fig. 4 is the process flow diagram of judging violating the regulations.

(5) embodiment

Below in conjunction with accompanying drawing the present invention is further described.

With reference to Fig. 1, Fig. 2, Fig. 3, Fig. 4, a kind of intelligent analysis system for municipal traffic journey time, the candid photograph identification equipment 1, the intelligent server 2 that comprise each crossing that is installed in urban traffic network, described each are captured identification equipment 1 and are connected intelligent server 2; Described intelligent server 2 comprises: urban traffic network topological structure module, be used for setting up the road network topology structure according to each crossing and the annexation thereof of urban traffic network, set the road speed limit in stroke between the interconnective crossing and this highway section, and set up the corresponding relation of road network topology structure and OD matrix; The vehicle snapshot identification module is used for when vehicle passes through the crossing, sends the candid photograph instruction to capturing identification equipment, and reads the data of capturing identification equipment, the license plate number and the candid photograph time of writing down this vehicle; Road-section average running time computing module, be used for according to the road network topology structure, by passing through the vehicle at this crossing in a period of time before the license plate number statistics current time, the vehicle coupling of passing through with interconnective crossing again, find the identical vehicle of license plate number, this vehicle was subtracted each other through the time at two different crossings, and these sample datas are carried out the running time that statistical study obtains two crossings; Discrimination module violating the regulations, be used for running time according to described two crossings, and according to the stroke and the road speed limit at two heavy crossings of net topology structure, calculate the average overall travel speed of vehicle in this highway section, comparison road speed limit, bigger as average overall travel speed than road speed limit, be judged to be violating the regulationsly, write down the license plate number of this vehicles peccancy, time violating the regulations and highway section violating the regulations.

Described intelligent server also comprises: query script violating the regulations, be used for record according to logging modle violating the regulations, and set up the corresponding sequence of time violating the regulations, highway section violating the regulations and the license plate number of vehicles peccancy, and set up the query pattern of this sequence.The flow rate calculation module is used for according to the vehicle snapshot identification module vehicle flowrate at each crossing being added up, and writes down the vehicle flowrate of statistics according to timeslice.The graph inquiring module is used for road network topology structure and electronic chart are set up corresponding relation, during the adjacent crossing of on clicking electronic chart two, is linked to two interconnective crossings of road network topology structure.

The flow inquiry of present embodiment:, show the number of vehicles of the process in the previous time period of carving between each crossing at a time by Query Database; Selected parameter is the crossing title, the zero-time section.

The demonstration of running time: Query Database shows the running time between current crossing.

Graph inquiring: on electronic chart, two crossings that click will be inquired about can show the running time between these two crossings; Selected parameter is an initial crossing title, the zero-time section.

Inquiry violating the regulations: the number plate and the time period of the required inquiry of input on interface, can judge whether current vehicle exeeds the regulation speed in this time period.

Claims (4)

1, a kind of intelligent analysis system for municipal traffic journey time, it is characterized in that: described intelligent analysis system comprises the candid photograph identification equipment at each crossing that is installed in urban traffic network, intelligent server, and described each captured identification equipment and connected intelligent server; Described intelligent server comprises: urban traffic network topological structure module, be used for setting up the road network topology structure according to each crossing and the annexation thereof of urban traffic network, set the road speed limit in stroke between the interconnective crossing and this highway section, and set up the corresponding relation of road network topology structure and OD matrix; The vehicle snapshot identification module is used for when vehicle passes through the crossing, sends the candid photograph instruction to capturing identification equipment, and reads the data of capturing identification equipment, the license plate number and the candid photograph time of writing down this vehicle; Road-section average running time computing module, be used for according to the road network topology structure, by passing through the vehicle at this crossing in a period of time before the license plate number statistics current time, the vehicle coupling of passing through with interconnective crossing again, find the identical vehicle of license plate number, this vehicle was subtracted each other through the time at two different crossings, obtain the running time at two crossings; Discrimination module violating the regulations, be used for running time according to described two crossings, and according to the stroke and the road speed limit at two crossings of road network topology structure, calculate the average overall travel speed of vehicle in this highway section, comparison road speed limit, bigger as average overall travel speed than road speed limit, be judged to be violating the regulationsly, and write down the license plate number of this vehicles peccancy, time violating the regulations and highway section violating the regulations.

2, intelligent analysis system for municipal traffic journey time as claimed in claim 1 is characterized in that: described intelligent server also comprises:

Enquiry module violating the regulations is used for the record according to logging modle violating the regulations, sets up the corresponding sequence of time violating the regulations, highway section violating the regulations and the license plate number of vehicles peccancy, and sets up the query pattern of this sequence.

3, intelligent analysis system for municipal traffic journey time as claimed in claim 2 is characterized in that: described intelligent server also comprises:

The flow rate calculation module is used for according to the vehicle snapshot identification module vehicle flowrate at each crossing being added up, and writes down the vehicle flowrate of statistics according to the time period.

4, intelligent analysis system for municipal traffic journey time as claimed in claim 3 is characterized in that: described intelligent server also comprises:

The graph inquiring module is used for road network topology structure and electronic chart are set up corresponding relation, during the adjacent crossing of on clicking electronic chart two, is linked to two interconnective crossings of road network topology structure.

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