CN105787858A - Situation deduction method for expressway network - Google Patents
Situation deduction method for expressway network Download PDFInfo
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Abstract
The invention provides a situation deduction method for expressway network. The method comprises the steps that 1) operation situation deduction model configuration of the expressway network is established; and 2) according to the operation situation deduction model configuration of the expressway-network, an online rapid expressway-network operation situation deduction model is established, and the online rapid expressway-network operation situation deduction model further includes a traffic demand estimation and prediction model, a road diversion rate based transmission model, and a three-detector based road situation deduction model.
Description
Technical field
The present invention relates to traffic analysis technical field, particularly relate to a kind of freeway net deducing manoeuver method.
Background technology
Along with the development of society, traffic has had become as people and has lived very important ingredient, and traffic administration directly influences the operation of entire society, therefore if able to network operation situation of satisfying the need carries out Accurate Prediction just can provide better reference for traffic administration.Main artery network operation situation is primarily referred to as the variation tendency of the Running State that satisfies the need and is predicted and assesses, and depicts the space-time development process of road network running status when considering accident.
Current most researchs are all based on historical data and utilize mathematics to calculate or traffic simulation mode satisfies the need Running State and development trend is predicted, after having data to update, needs re-start calculating or emulation, Current traffic data mode cannot reproduce, and arithmetic speed is slow, especially, microcosmic, middle sight traffic simulation method, in large-scale road network situation, simulation velocity is slow, it is impossible to meet the demand of dynamic road network Study on Trend anticipation in current road network management.Mathematics computational methods are built upon on substantial amounts of model hypothesis basis, and detector data more sufficient road network operational efficiency is higher, but are affected bigger by the reliability of road network running status, data sensitive.
The establishment of the mechanisms such as the provincial road network management of establishment and various places and the emergency disposal center along with Ministry of Communications's road network operational monitoring and emergency disposal center, the administration sections at different levels such as portion, province and district city satisfy the need Running State and future developing trend change is grasped in real time and the demand of anticipation is more and clearer and more definite.But the monitoring equipment on current road network is substantially on the low side, still can not reach monitoring point layout requirements, and estimate that also cannot complete monitoring within nearly a period of time covers the main artery network operational monitoring system in the whole nation.Monitor data due to needs in a large number and just can carry out the prediction of accurate road network operation situation, but monitoring infrastructure construction is definitely not and can complete between overnight, therefore under current road network and road network operational monitoring system condition, how to complete the analysis of road network operation situation, early warning is run for road network, especially the quick early warning under emergency circumstances lays the foundation, and is currently urgently solve the technical problem that.
Summary of the invention
For prior art is monitored the problem that cannot be carried out accurate road network operation situation in the incomplete situation of infrastructure, the purpose of the present invention is to propose to a kind of freeway net deducing manoeuver method.
To achieve these goals, embodiments provide a kind of freeway net situation and push away method, including:
Step 1, set up road network operation situation deduce model framework；
Step 2, according to described road network operation situation deduce model framework set up the online rapid deduction model of road network operation situation；The online rapid deduction model of wherein said road network operation situation includes: transport need is estimated and prediction, the TRANSFER MODEL based on section split ratio, the section deducing manoeuver model based on three detectors.
Wherein, step 1 specifically includes:
Step 11, determine that road network operation situation deduces the comprising modules of model, at least include transport need estimation and prediction module, consistency check module, the varying information module of the traffic capacity of road network structure, middle sight traffic simulation engine modules, road network operational factor output module
Step 12, set up road network operation situation and deduce mechanism, adopt and roll planar process and deduce, in time by analysis and Control phase rolls forward so that history is estimated that data are modified by new data, and continue the new running status of prediction based on new data.
Wherein, step 2 specifically includes:
Step 21, transport need are estimated and prediction: based on expressway tol lcollection data and section traffic flow data, adopt polynomial trend Forecasting Methodology to carry out Traffic Demand Forecasting, eliminate, by kalman filter method, the impact that measured data error is brought;
Step 22, foundation are without the traffic flow TRANSFER MODEL under situation: without, under situation, adopting the traffic flow TRANSFER MODEL based on section split ratio, carry out rapid deduction；
Step 23, the block status set up under situation deduce model: under situation, the node steering flow of section split ratio as input and is exported, the intermediateness in section, accident place is deduced by three detector models, to obtain the traffic behavior more accurately of optional position on this section.
Technical scheme has the advantage that
Embodiments provide a kind of freeway net deducing manoeuver Forecasting Methodology, it is possible to just can be realized by less data the operation situation of road network is predicted.
Accompanying drawing explanation
Be will become clearer from by description a preferred embodiment of the present invention carried out below in conjunction with accompanying drawing, technical scheme and technique effect thereof, and more easily understand.Wherein:
The road network operation situation that Fig. 1 is the embodiment of the present invention deduces model framework schematic diagram；
Fig. 2 is the principle schematic rolling planar process；
Fig. 3 deduces example based on the road network operation situation rolling planar process；
Fig. 4 is road network local annexation schematic diagram；
Fig. 5 is simple road network schematic diagram；
Fig. 6 is three detector theoretical principle schematic diagrams；
Fig. 7 utilizes three detector theories to estimate and predicts the traffic behavior schematic diagram of each section in section；
Fig. 8 is the flow shunt figure assuming each section.
Detailed description of the invention
Below with reference to appended accompanying drawing, a preferred embodiment of the present invention is described.The road network operation situation Forecasting Methodology of the embodiment of the present invention, it is proposed that the OD needs estimate in a kind of road network operation situation deduction process and the main contents such as prediction, section situation rapid deduction model.
To achieve these goals, embodiments provide a kind of freeway net deducing manoeuver method, including:
Embodiments provide a kind of freeway net deducing manoeuver method, including:
Step 1, set up road network operation situation deduce model framework；
Step 2, according to described road network operation situation deduce model framework set up the online rapid deduction model of road network operation situation；The online rapid deduction model of wherein said road network operation situation includes: transport need is estimated and prediction, the TRANSFER MODEL based on section split ratio, the section deducing manoeuver model based on three detectors.
Wherein, step 1 specifically includes:
Step 11, determine that road network operation situation deduces the comprising modules of model, at least include transport need estimation and prediction module, consistency check module, the varying information module of the traffic capacity of road network structure, middle sight traffic simulation engine modules, road network operational factor output module
Step 12, set up road network operation situation and deduce mechanism, adopt and roll planar process and deduce, in time by analysis and Control phase rolls forward so that history is estimated that data are modified by new data, and continue the new running status of prediction based on new data.
Wherein, step 2 specifically includes:
Step 21, transport need are estimated and prediction: based on expressway tol lcollection data and section traffic flow data, adopt polynomial trend Forecasting Methodology to carry out Traffic Demand Forecasting, eliminate, by kalman filter method, the impact that measured data error is brought;
Step 22, foundation are without the traffic flow TRANSFER MODEL under situation: without, under situation, adopting the traffic flow TRANSFER MODEL based on section split ratio, carry out rapid deduction；
Step 23, the block status set up under situation deduce model: under situation, the node steering flow of section split ratio as input and is exported, the intermediateness in section, accident place is deduced by three detector models, to obtain the traffic behavior more accurately of optional position on this section.
It is further described implementing algorithm below:
The road network operation situation deduction model framework of setting up of step 1 specifically includes:
Step 11, determine road network operation situation deduce model comprising modules, as it is shown in figure 1, it at least includes with lower module: transport need is estimated and prediction, consistency check, the varying information of the traffic capacity of road network structure, middle sight traffic simulation engine, road network operational factor output etc..Relation between each module and data flow are as shown in Figure 1.
Step 12, set up road network operation situation and deduce mechanism, adopt and roll planar process and deduce, in time by analysis and Control phase rolls forward so that history is estimated that data are modified by new data, and continue the new running status of prediction based on new data.
Roll planar process concept as in figure 2 it is shown,Represent the time span in backtracking stage, in figureRepresent the time span (i.e. short-term forecast phases-time length) in rolling cycle,Represent that the time span of analysis phase, analysis phase time span and the difference rolling length cycle time are long-term forecast phases-time length.Assume in the analysis phaseAfter traffic circulation status predication, if in the periodAfterwards, can obtain the trip requirements data of reality, then the trip requirements data of this reality available replace the data in short-term forecast stage, second half section correction analysis analysis phase (i.e. long-term forecast stage) traffic circulation status predication.
In rolling planar process, the relevant parameter of setting comprises six: backtracking stage length, analysis phase length, rolling cycle, short-term forecast stage length, long-term forecast stage length and general planning plane length.General planning plane length is the bulk analysis time span of whole simulation；Backtracking stage length representative prediction process utilizes the time span of historical data, analyzes process length and represent the time span of each analysis process in rolling planar process；The rolling cycle represents every how long once analyzing process, is namely analyzed the frequency of process；The time span of short-term forecast stage length fingering row short-term forecast, is numerically equal to roll Cycle Length；Long-term forecast stage length is the time span carrying out long-term forecast, is numerically equal to the difference analyzed process length with roll Cycle Length.
It is as follows that road network operation situation deduces flow process:
Step 121, history OD data and road network structure attribute information prediction is utilized to generate futureThe demand data of period；
Step 122, check whether there is event information input, if there being event information to input, call road network structure and traffic capacity time-varying module, carry out road network structure and the correction of each road section capacity, according to section split ratio, the demand traffic flow data of prediction is fitted on each section, and jumps to step 124；Step 123 is then jumped to without event input；
Step 123, to each section point of invocation queuing model or spatial queue model, deduceThe state in moment, the transfer between section and section considers node Capacity Constraints, and data are stored in the Running Status Table of section, jump to step 125；
Step 124, carry out, according to section or track, the Situation Assessment that section is affected by event to there being event section to call three detector models, formedThe state in moment, becomes more meticulous and deduces the block status data obtained and become a mandarin, go out flow data substitution step 123, carry out node Capacity Constraints judgement, and step terminates.
Section Running Status Table is exported the assessment of road network running status and early warning prototype system, and enters step 126 by step 125, middle macroscopic view rapid deduction module；
Step 126, determining whether that new data inputs, if had, entering step 127；If it is not, judge whether to terminate deducing, if not terminating deducing, then return step 1 and startPrediction；
Step 127, reading close onThe Traffic flow detecting device data in moment and charge data, utilize currentThe data in moment, update prediction in the Running Status Table of sectionThe data in moment, return step 121 afterwards and startForecast demand estimate.
Rolling planar process and providing in future anticipation information, different depending on its rolling cycle and analysis process length setting, backtracking stage length is 30 minutes by the embodiment of the present invention, and rolling cycle set is 15 minutes, and analysis phase length is set as 2 hours；The measured data then utilizing first 30 minutes of prediction time is predicted the traffic circulation state of following two hours in conjunction with history OD data, when 0-15 minute, system will carry out following road grid traffic running status short-term forecast, within the 15-120 minute, system will carry out following road grid traffic running status long-term forecast, now information updating frequency is the rolling cycle 15 minutes, to obtain new measured data and replace the short term predicted data of previous stage after spending 15 minutes, the embodiment of the present invention general planning cycle is one day.
Step 2, according to described road network operation situation deduce model framework set up the online rapid deduction model of road network operation situation.Specifically include
Step 21, to transport need estimate and prediction
Based on expressway tol lcollection data and section traffic flow data, adopt polynomial trend Forecasting Methodology to carry out Traffic Demand Forecasting, eliminate, by kalman filter method, the impact that measured data error is brought.Detailed process is as follows:
First provide some symbols and implication thereof of using in model:
J=road network charge station node ID,；
τ=departure time interval sequence number, τ=1,2 ...；
The stage of=rolling forecast,=1,2,3,…；
The departure time intervening sequence number that=each rolling stage includes；
=charge station node j is in the demand influx of interval τ；
=based on the charge station node j of historical data in the prior estimate of the demand influx of interval τ；
=charge station node j is at interval τWith prior estimateDemand disruption；
=demand disruptionThe single order of variable, second order, third order difference amount；
Estimate average；
Estimation average；
The state variable vector of=stage k；
The measurement vector of=stage k；
=measure vectorAnd state vectorMatching relationship matrix；
The process noise of=stage k；
The measurement error of=stage k；
=predict based on the observation by the end of stage k-1Namely；
=estimate based on the observation by the end of the k stageNamely；
=stage k-1 predictionState covariance battle array, namely；
=stage k, estimationState covariance battle array, namely；
The target of this step is to find charge station's nodeAt departure time intervalPrediction and the time-varying traffic demand of estimation.Under generic condition, for rule conditions of demand under, the prior estimate on historical experience value basisIt is able to reflect preferably current requirements and temporal behavior thereof.For better reflecting the practical situation of traffic circulation state on the same day, estimating by history and difference component is expressed, it is also conceivable to suitable random disturbances in real system, the embodiment of the present invention puts aside this random disturbances item.
Formula 1
Wherein,Represent history OD data to j at time departureError term with approximate real demand.
Introducing polynomial trend is predicted, carries out the prediction of the difference vector of τ+ζ, whereinFor preset value, can be by order to improve trend prediction precisionValue set smaller；M order polynomial is used to build this function as follows:
Wherein,For multinomial coefficient.
Anticipation functionCarry out Taylor expansion, can be converted intoMultistage difference combination:
；
Wherein p and m is default integer.
As can be seen here, the coefficient of the formula of original polynomial distribution can be expressed as:
The embodiment of the present invention takes three rank multinomial models carry outPrediction be shown below:
Definition is at forecast period,(if each stage forecast Period Length is that l includes 3 intervals, thenStage,Serial number be 9);
The wherein transposition symbol of T representing matrix；
State-transition matrix is defined as:
The predictor formula in k+1 stage is as follows:
It it is more than forecast period.
Introduce KalmanFiltering framework below and eliminate the impact that charge station's influx measured value error is brought；Owing to measured value direct in the embodiment of the present invention and estimated value are identical vector value, therefore H=I.
First the prediction of current generation and the transmission of covariance is carried out with the posterior estimate of previous stage:
Introduce afterwardsAfter stage measured value, carry out the calculating of gain:
, formula 2
The posterior estimate of formation stages k and the transmission of covariance matrix:
Formula 3
Formula 4
Call formula 2 forecast period's, can generate, based on this predictive value, the charge station deduced for rolling planar process and flow into value, and form the multistage difference matrix needed for subsequent prediction.
Step 22, foundation are without the traffic flow TRANSFER MODEL under situation: without, under situation, adopting the traffic flow TRANSFER MODEL based on section split ratio, carry out rapid deduction.
Without, under situation, adopting the traffic flow TRANSFER MODEL based on section split ratio, carry out rapid deduction.
For Fig. 4, the relation between link flow and section split ratio is described.
First given road network being modeled, interchange is set to node, and charge station is decomposed into node and possesses the dummy node of vehicle generation and suction function.
Symbol definition is as follows:
Node set, comprises N number of node；
Dummy node set, comprises M node, M≤N；
Represent section or virtual segment；
Represent by sectionWithThe road chain of section composition,；
Represent t, by sectionTo sectionSplit ratio；
Represent t, sectionTotal capacity, according to road structure characteristic and whether by event affect and determine；
Represent t, sectionTotal wheel traffic；
Represent t, flow into sectionThe volume of traffic；
Represent t, from sectionThe volume of traffic flowed out；
Represent t, it is desirable to enter sectionThe volume of traffic；
Represent t, it is desirable to from sectionThe volume of traffic flowed out；
Represent t, it is desirable to from sectionFlow out the volume of traffic being left on this section because of upstream occlusion；
To any sectionMeet:
SectionExpectation influx in t is that each has the section of annexation, deduces according to its expectation discharge and split ratio thereof, is shown below.
IfSo,
, it is about to expectation influx and is assigned to the influx in each section of t.
Otherwise,
Enter next one circulation afterwards,。
Below for Fig. 5, it is assumed that the flow shunt in each section compares as shown in Figure 8, with sectionFor example, above-mentioned calculating process is described.
Make each section expectation discharge, then, according to section shunt ratio table, total expectation flows into sectionDemand be
WhenTime,, then
WhenTime,, then the discharge distributed from each section is:
Step 23, the block status set up under situation deduce model: under situation, the node steering flow of section split ratio as input and is exported, the intermediateness in section, accident place is deduced by three detector models, to obtain the traffic behavior more accurately of optional position on this section.
Under situation, using the node steering flow of section split ratio as input and output, the intermediateness in section, accident place is deduced by three detector models, has obtained the traffic behavior more accurately of optional position on this section.
First three detectors are introduced theoretical.
Proposition directly can be estimated the simplification kinematic wave model of the system state amount of any one event in border by section upstream and downstream boundary condition, it is similarly to one virtual-sensor of the internal existence in section, therefore the simplification kinematic wave model of Newell is also referred to as three detectors theoretical (Three-DetectorTheory).
Fig. 6 describes the operation principle that Newell tri-detector is theoretical, figure middle and upper reachesWhat corresponding block arrow straight line represented is coboundary, downstreamCorresponding lower boundary.For time aerial arbitrfary point, its traffic behavior is by coboundary corresponding eventEvent corresponding with lower boundaryTraffic behavior together decide on, specific as follows.
In flow-density triangular relational model, there is the traffic circulation state of two kinds of strict differentiations, i.e. the congestion state on unimpeded running status on the left of Fig. 6 and right side.Unimpeded running status characteristic of correspondence ripple is referred to as prewave, i.e. the curve of Fig. 6 signal, this represents under unimpeded running status, the spread speed of prewave and the travel speed of vehicleIdentical.
Event inside sectionState when being determined by prewave, pointCorresponding accumulation vehicle flowrate should be equal to the accumulation vehicle flowrate of upstream boundary a point, namely
In formula,Express time is poor, and namely prewave propagates section certain point internal from upstream boundaryThe required time.Similar, crowded running status characteristic of correspondence ripple is referred to as rearmounted ripple, i.e. the curve of upper figure signal。
Event inside sectionState when being determined by rearmounted ripple, pointCorresponding accumulation vehicle flowrateThe accumulation vehicle flowrate corresponding with a bThere is following relation:
Therefore, inside section, the state of event is determined by rearmounted ripple, pointCorresponding accumulation vehicle flowrateComputing formula is:
For inside, section any event, its accumulation vehicle flowrate can be obtained by prewave and rearmounted ripple COMPREHENSIVE CALCULATING.The value that both calculations obtain is likely to different, and Newell proves that the accumulation vehicle flowrate of its event of P is that the smaller value calculated by both modes determines, formula is as follows:
Formula 5
In formula,Represent upstream boundary pointCorresponding accumulation vehicle flowrate, i.e. the some a shown in figure；Represent downstream boundary pointCorresponding accumulation vehicle flowrate, i.e. the some b of signal in figure.The physical meaning of formula 5 is that the accumulation vehicle flowrate passed through in any event is subject to the restriction of upstream and downstream vehicle number simultaneously, namely arrives the vehicle number of this point and downstream is able to receive that when congestion state maximum vehicle number from upstream boundary when unimpeded state.
Therefore in the step 23 in the embodiment of the present invention:
Traffic behavior is estimated and the target of prediction is predictionWithBetween the traffic behavior of any position.As it is shown in fig. 7, due to based on pointWithIntegrated flow, it is possible to use three detector theoretical predictionsWithBetween the traffic behavior of optional position, same, based on pointIntegrated flow, it is possible to predictionBetween the traffic behavior of optional position.Therefore, three key positions are paid close attention toThe estimation of integrated flow and prediction, then traffic status prediction value is existedBetween transmit, and the constraints that three meets is as follows:
(1) constraint of upstream boundary
(2) constraint of accident point
(3) constraint of downstream boundary
The traffic behavior introducing accident place section under event condition below is estimated and prediction.
Such as Fig. 7, event occurs, the moment, adopt the theoretical state to this section each position of three detectors estimate and predict.
Under event condition, the traffic behavior in section, section upstream, accident place is estimated and the step of prediction is as follows:
Step 231: obtain input boundary condition and the key parameter of traffic flow transmission；
Step 231.1: obtain three parameters in section before and after event location respectively；
Step 231.2: according to event impact analysis result, it was predicted that the time of event duration, and the traffic capacity value after potential reduction；
Step 231.3: obtain to outside space-time boundary value,
Step 232: after event occurs, the accumulative vehicle flowrate prediction of event place section；
Step 232.1: the section integrated flow prediction in incident duration section；
, formula 6
Wherein,
Step 232.2: terminating to dissipate in the complete time period to event impact from event, event place section integrated flow is predicted；
Wherein,。
Step 232.3: the event impact dissipation complete time is determined.
If,
Wherein,,It is a less value, can determine according to computational accuracy demand.Meeting formula 6 then, event impact is dissipated complete.That is, this moment sectionIntegrated flow can be byWithValue estimate obtain.
Step 233: the traffic behavior in section, section upstream, accident place is estimated and prediction.
Incident duration section and event affect dissipate complete before, the traffic behavior in section, upstream can be byCalculate according to three detector concept and obtain.
The free stream velocity used in above-mentioned model and backward-wave speed can pass through the GPS track data acquisition of vehicle, and detailed process has relevant documents and materials.
Along with the development of technology, present inventive concept can realize by different way.Embodiments of the present invention are not limited in embodiments described above, and can be changed within the scope of the claims.
Claims (7)
1. a freeway net deducing manoeuver method, it is characterised in that including:
Step 1, set up road network operation situation deduce model framework；
Step 2, according to described road network operation situation deduce model framework set up the online rapid deduction model of road network operation situation；The online rapid deduction model of wherein said road network operation situation includes: transport need is estimated and prediction, the TRANSFER MODEL based on section split ratio, the section deducing manoeuver model based on three detectors.
2. freeway net deducing manoeuver method according to claim 1, it is characterised in that step 1 specifically includes:
Step 11, determine that road network operation situation deduces the comprising modules of model, at least include transport need estimation and prediction module, consistency check module, the varying information module of the traffic capacity of road network structure, middle sight traffic simulation engine modules, road network operational factor output module
Step 12, set up road network operation situation and deduce mechanism, adopt and roll planar process and deduce, in time by analysis and Control phase rolls forward so that history is estimated that data are modified by new data, and continue the new running status of prediction based on new data.
3. freeway net deducing manoeuver method according to claim 1, it is characterised in that step 2 specifically includes:
Step 21, transport need are estimated and prediction: based on expressway tol lcollection data and section traffic flow data, adopt polynomial trend Forecasting Methodology to carry out Traffic Demand Forecasting, eliminate, by kalman filter method, the impact that measured data error is brought;
Step 22, foundation are without the traffic flow TRANSFER MODEL under situation: without, under situation, adopting the traffic flow TRANSFER MODEL based on section split ratio, carry out rapid deduction；
Step 23, the block status set up under situation deduce model: under situation, the node steering flow of section split ratio as input and is exported, the intermediateness in section, accident place is deduced by three detector models, to obtain the traffic behavior more accurately of optional position on this section.
4. freeway net deducing manoeuver method according to claim 2, it is characterised in that described step 12 specifically includes:
Step 121, history OD data and road network structure attribute information prediction is utilized to generate futureThe demand data of period；
Step 122, check whether there is event information input, if there being event information to input, call road network structure and traffic capacity time-varying module, carry out road network structure and the correction of each road section capacity, according to section split ratio, the demand traffic flow data of prediction is fitted on each section, and jumps to step 124；Step 123 is then jumped to without event input；
Step 123, to each section point of invocation queuing model or spatial queue model, deduceThe state in moment, the transfer between section and section considers node Capacity Constraints, and data are stored in the Running Status Table of section, jump to step 125；
Step 124, carry out, according to section or track, the Situation Assessment that section is affected by event to there being event section to call three detector models, formedThe state in moment, becomes more meticulous and deduces the block status data obtained and become a mandarin, go out flow data substitution step 123, carry out node Capacity Constraints judgement, and step terminates;
Section Running Status Table is exported the assessment of road network running status and early warning prototype system, and enters step 126 by step 125, middle macroscopic view rapid deduction module；
Step 126, determining whether that new data inputs, if had, entering step 127；If it is not, judge whether to terminate deducing, if not terminating deducing, then return step 1 and startPrediction；
Step 127, reading close onThe Traffic flow detecting device data in moment and charge data, utilize currentThe data in moment, update prediction in the Running Status Table of sectionThe data in moment, return step 121 afterwards and startForecast demand estimate.
5. freeway net deducing manoeuver method according to claim 3, it is characterised in that step 21 specifically includes:
Charge station's node is obtained by below equationAt departure time intervalPrediction and the time-varying traffic demand of estimation:
Formula 1
Wherein,Represent the charge station node j demand influx at interval τ,Represent the prior estimate in the demand influx of interval τ of the charge station node j based on historical data；Represent that charge station node j is at interval τWith prior estimateDemand disruption；Represent history OD data to j at time departureError term with approximate real demand；
Introducing polynomial trend is predicted, carries out the prediction of the difference vector of τ+ζ, whereinFor preset value；M order polynomial is used to build this function as follows:
Wherein,For multinomial coefficient；
Anticipation functionCarry out Taylor expansion, can be converted intoMultistage difference combination:
；
Wherein p and m is default integer；
Therefore the coefficient of the formula of original polynomial distribution can be expressed as:
；
Take three rank multinomial models to carry outPrediction be shown below:
Definition is at forecast period,(if each stage forecast Period Length is that l includes 3 intervals, thenStage,Serial number be 9);
The wherein transposition symbol of T representing matrix；
State-transition matrix is defined as:
The predictor formula in k+1 stage is as follows:
；
Introduce KalmanFiltering framework below and eliminate the impact that charge station's influx measured value error is brought；Owing to measured value direct in the embodiment of the present invention and estimated value are identical vector value, therefore H=I;
First the prediction of current generation and the transmission of covariance is carried out with the posterior estimate of previous stage:
After introducing stage measured value afterwards, carry out the calculating of gain:
, formula 2
The posterior estimate of formation stages k and the transmission of covariance matrix:
Formula 3
Formula 4
Call formula 2 forecast period's, can generate, based on this predictive value, the charge station deduced for rolling planar process and flow into value, and form the multistage difference matrix needed for subsequent prediction；
Wherein:
J is road network charge station node ID,；
τ is departure time interval sequence number, τ=1,2 ...；
For the stage of rolling forecast,=1,2,3,…；
For the departure time intervening sequence number that each rolling stage includes；
For the charge station node j demand influx at interval τ；
For the charge station node j based on historical data in the prior estimate of the demand influx of interval τ；
For charge station node j at interval τWith prior estimateDemand disruption；
, for demand disruptionThe single order of variable, second order, third order difference amount；
ForEstimate average；
ForEstimation average；
State variable vector for stage k；
Measurement vector for stage k；
For measuring vectorAnd state vectorMatching relationship matrix；
Process noise for stage k；
Measurement error for stage k；
For what predict based on the observation by the end of stage k-1Namely；
For what estimate based on the observation by the end of the k stageNamely；
For stage k-1 predictionState covariance battle array, namely；
For stage k, estimationState covariance battle array, namely。
6. freeway net deducing manoeuver method according to claim 5, it is characterised in that step 22 specifically includes:
First given road network being modeled, interchange is set to node, and charge station is decomposed into node and possesses the dummy node of vehicle generation and suction function；To any sectionMeet:
SectionExpectation influx in t is that each has the section of annexation, deduces according to its expectation discharge and split ratio thereof, is shown below；
IfSo,
, it is about to expectation influx and is assigned to the influx in each section of t；
Otherwise,
Enter next one circulation afterwards,；
Symbol definition is as follows:
Node set, comprises N number of node；
Dummy node set, comprises M node, M≤N；
Represent section or virtual segment；
Represent by sectionWith sectionThe road chain of composition,；
Represent t, by sectionTo sectionSplit ratio；
Represent t, sectionTotal capacity, according to road structure characteristic and whether by event affect and determine；
Represent t, sectionTotal wheel traffic；
Represent t, flow into sectionThe volume of traffic；
Represent t, from sectionThe volume of traffic flowed out；
Represent t, it is desirable to enter sectionThe volume of traffic；
Represent t, it is desirable to from sectionThe volume of traffic flowed out；
Represent t, it is desirable to from sectionFlow out the volume of traffic being left on this section because of upstream occlusion；
7. freeway net deducing manoeuver method according to claim 6, it is characterized in that, described step 23 specifically includes: under situation, using the node steering flow of section split ratio as input and output, the intermediateness in section, accident place is undertaken deducing to obtain the traffic behavior of optional position on this section by three detector models；Specifically include:
Traffic behavior is estimated and the target of prediction is predictionWithBetween the traffic behavior of any position；Due to based on pointWithIntegrated flow, it is possible to use three detector theoretical predictionsWithBetween the traffic behavior of optional position, same, based on pointWithIntegrated flow, it is possible to predictionWithBetween the traffic behavior of optional position；Step 23 needs obtain key positionThe estimation of integrated flow and prediction, then traffic status prediction value is existedWithBetween transmit, and the constraints that three meets is as follows:
(1) constraint of upstream boundary
(2) constraint of accident point
(3) constraint of downstream boundary
Event occurs, the moment, adopt the theoretical state to this section each position of three detectors estimate and predict；
Under event condition, the traffic behavior in section, section upstream, accident place is estimated and the step of prediction is as follows:
Step 231: obtain input boundary condition and the key parameter of traffic flow transmission；
Step 231.1: obtain three parameters in section before and after event location respectively；
Step 231.2: according to event impact analysis result, it was predicted that the time of event duration, and the traffic capacity value after potential reduction；
Step 231.3: obtain fromArriveOutside space-time boundary value,
Step 232: after event occurs, the accumulative vehicle flowrate prediction of event place section；
Step 232.1: the section integrated flow prediction in incident duration section；
Formula 6
Wherein,
Step 232.2: terminating to dissipate in the complete time period to event impact from event, event place section integrated flow is predicted；
Wherein；
Step 232.3: the event impact dissipation complete time is determined；If,
Wherein, it is a less value, can determine according to computational accuracy demand；Meet the impact of formula 6 then event and dissipate complete；That is, this moment sectionIntegrated flow can be byValue estimate obtain
Step 233: the traffic behavior in section, section upstream, accident place is estimated and prediction；
Incident duration section and event affect dissipate complete before, the traffic behavior in section, upstream can be byCalculate according to three detector concept and obtain.
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