CN107103142A - Comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology - Google Patents
Comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology Download PDFInfo
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Abstract
The present invention discloses a kind of comprehensive traffic network operation situation towards highway and the railway network and deduces emulation technology, it is characterised in that including：1）Regional complex transportation network is met an urgent need simulation framework, 2）The needs estimate and forecast model combined based on trend prediction with neutral net, 3）Multi-mode comprehensive transport capability simulation model based on intelligent body；This is deduced emulation technology towards the comprehensive traffic network operation situation of highway and the railway network and utilizes existing network of highways, railway network traffic data, emphasis constructs the general frame of the emergent emulation platform of regional complex transportation network, with time-space network modeling technique, the multi-mode comprehensive traffic simulation model based on intelligent body is constructed.
Description
Technical field
The invention belongs to intelligent transportation field, specifically a kind of comprehensive traffic network towards highway and the railway network is transported
Row deducing manoeuver emulation technology.
Background technology
In regional response process of emergency system, how to formulate the emergent Traffic Organization of comprehensive traffic road network ensures emergent friendship
Lead to safe efficient, orderly function, for improving region emergency managerial ability, casualties and property caused by reduction disaster
Loss, has important practical significance.For transportation network, particularly composite communications transport network, it is difficult to pass through mathematics
Its running status changing trend under event condition of mode quick obtaining such as calculate and to different management and control measures or emergent ring
Answer the visual effect of measure.Therefore, set up regional complex transportation network operation situation and deduce emulation, realize correspondence network of highways
The quick loading on composite communications transport network of anxious management and control measures is emulated with deduction, is to evaluate and the emergent traffic of optimization road network
The important means of organization scheme.
Single travel pattern being intended for existing traffic simulation platform, less research is more in composite communications transport network more
Plant under conditions of travel pattern exists jointly and how to build emulation platform.And existing analogue system, mostly single-threaded system,
Simulation velocity is partially slow, it is impossible to which adaptation is docked with the real-time of operation system.
The content of the invention
It is an object of the invention to provide one kind using existing network of highways, railway network traffic data, emphasis constructs region
The general frame of the emergent emulation platform of composite communications transport network, with time-space network modeling technique, is constructed based on intelligent body
Multi-mode comprehensive traffic simulation model towards highway and the railway network comprehensive traffic network operation situation deduce emulation technology.
In order to solve the above-mentioned technical problem, the technical scheme is that：
A kind of comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology, including：
1) the emergent simulation framework of regional complex transportation network,
Including comprehensive travel needs estimate and prediction module, road-net node/section dynamic function Attribute tuning module, multimode
Formula dynamic traffic flow, traffic flow simulation, dynamic shortest path are calculated, operation is assessed, exported and interface display module, emulation system
The key data input of system includes OD demand datas, road net traffic state service data real-time with history, incident data,
Network foundation data and road management and control and information distribution scheme；
2) needs estimate and forecast model combined based on trend prediction with neutral net,
Comprehensive travel needs estimate is with prediction module mainly by real-time and history OD demand datas, road grid traffic shape
State service data etc. carries out pattern analysis and OD trend estimations, and Mobile state tune is entered in influence of the combination accident to transport need
It is whole, generate road network Dynamic OD requirement matrix；
3) the multi-mode comprehensive transport capability simulation model based on intelligent body,
Multi-mode dynamic traffic mainly solves the Traffic Assignment Problem between a variety of travel patterns with flow module, by institute
Generation, the calculating for the selecting module for going out row mode for having path set, by vehicles distribution corresponding with people, realize intelligent body
Travel schedule, to each path of the intelligent body stroke one comprising timestamp sequence and sequence node.
Further, road-net node/section dynamic function attribute module is the basic data by network, with reference to dynamic
Incident data and road management and control data and information distribution scheme, generation emulation road-net node/section dynamic function attribute.
Further, multi-mode dynamic traffic passes through to all generations for having path set, the choosing for going out row mode with flow module
The calculating of module is selected, by vehicles distribution corresponding with people, the travel schedule of intelligent body is realized, to each intelligent body stroke one
Path comprising timestamp sequence and sequence node.
Further, traffic flow simulation and dynamic shortest path module constitute the nucleus module of middle sight traffic simulation, hand over
Through-flow analog module is using section transmission, node-node transmission model, on the premise of relevant constraint is met, and realization exists to vehicle
The simulation shifted between section internal operation and section, section mode is related to the magnitude of traffic flow-density relations, and theory is current
Ability is main by road segment classification and track quantity；Node metastasis model is related to left-hand rotation and straight trip Capacity Constraints, dynamic point
Hourage of the traffic flow simulated with platform on section will be fed back in the dynamic shortest path model of next iteration, and then
Decision-making foundation is provided for intelligent body selection specially designated path, meanwhile, new pathway can be fed back to next iteration.
Further, operation evaluation module is estimated to operation result from section, passage, OD to, path, region, is wrapped
In terms of including efficiency, speed, reliability, energy consumption.
Further, predicted using polynomial trend and combine vehicle flowrate or trip of the kalman filter method to Ingress node
The volume of the flow of passengers estimated and predicted respectively, and method is as follows：
J=road-net node sequence numbers, j=1,2 ..., N；
τ=departure time interval sequence number, τ=1,2 ...；
The stage of k=rolling forecasts, k=1,2,3 ...；
L=each rolls the departure time intervening sequence number included in the stage；
D(j,τ)The demand influxs of=node j in time interval τ；
Prior estimates of=node the j based on historical data in time interval τ demand influx；
μ(j,τ)=node j is in time interval τ D(j,τ)With prior estimateDemand disruption；
μ′(j,τ),μ″(j,τ),μ″′(j,τ)=demand disruption μ(j,τ)The single order of variable, second order, third order difference amount；
=D(j,τ)Estimate average；
=μ(p) (j,τ)Estimation average；
Zk=stage k state variable vector；
Yk=stage k measurement vector；
Hk=measurement vector YkWith state vector ZkMatching relationship matrix；
wk=stage k processing noise；
Rk=stage k measurement error；
=the Z based on the observation prediction by the end of stage k-1kThat is E (Zk|Y1,Y2,…,Yk-1)；
=the Z based on the observation estimation by the end of the k stageskThat is E (Zk|Y1,Y2,…,Yk)；
Pk,k-1The Z of=stage k-1 predictionskState covariance battle array, i.e.,
Pk,k=stage k, the Z of estimationkState covariance battle array, i.e.,
Polynomial trend prediction is introduced, the prediction of τ+ζ difference vector is carried out, the function is built such as using m order polynomials
Under：
μ(j,τ+ζ)=b0+b1ζ+b2ζ2+…bpζp+…+bmζm；
According to Taylor expansion anticipation function μ(j,τ+ζ)It is extended to μ(j,τ)Multistage difference combination：
The coefficient of the formula of original polynomial distribution can be expressed as：
Three rank multinomial models are taken to carry out μ(j,τ+ζ)Prediction be shown below：
We are defined on forecast period k, τ=kl, and such as each stage forecast Period Length is that l includes 3 time intervals, that
In the k=3 stages, τ sequence number is 9；
State-transition matrix is defined as：
The predictor formula in k+1 stages is as follows：
Zk+1=AkZk；
Introduce Kalman filtering algorithm to eliminate the influence that charge station's influx measured value error band comes, because this part is straight
It is identical vector value that measured value, which is connect, with estimate, therefore H=I；
Carried out first with the posterior estimate of previous stage, the prediction of current generation and the transmission of covariance：
Introduce afterwards after stage k measured value, carry out the calculating of gain：
The transmission of formation stages k posterior estimate and covariance matrix：
Pk,k=(I-KkHk)Pk,k-1
Call above-mentioned formula forecast period k+1 μ(j,τ+ζ), the node-flow for deduction can be generated based on the predicted value
Enter value D(j,τ), and the multistage difference matrix formed needed for subsequent prediction.
Further, by BP neural network model can respectively in freeway net vehicle OD distribution be predicted and
The OD for going out administrative staff in the railway network is distributed and is predicted, illustrates to calculate by taking the OD forecast of distribution of vehicle in freeway net as an example
Method flow, algorithm flow is as follows：
Step 1：Data decimation and standardization, for n charge tiny node in road network, choose before predicted time section
The OD data of m days same periods are simultaneously standardized according to formula 4.1；
Step 2：BP neural network is built, i=1 is made, for i-th of charge tiny node, the OD distributions for choosing first day are compared
Example chooses the data of second day as desired output vector as input vector, the like constitute m-1 group training samples；
Step 3：Neutral net is trained, by repeatedly training, network is reached the training objective of setting；
Step 4：OD distribution proportions are predicted, for the neutral net trained, the number of input prediction objective time interval the previous day
According to obtaining prediction distribution ratio, if going to step 5, otherwise go to step 2, adjust network parameter, re -training network；
Step 5：Output prediction OD distribution proportion matrixes, the output matrix if i=n, otherwise i+1 go to step 2.
Further, predict obtain road grid traffic respectively with BP neural network algorithm and polynomial trend prediction algorithm
After distribution proportion and each entrance magnitude of traffic flow, two kinds are predicted the outcome and is merged, obtained under the conditions of no event, predicted on network
The passenger of period or the OD matrixes of vehicle, by vehicle or passenger by node i to node j flow qijWebsite i entrances can be passed through
Flow OiWith with the distribution proportion r for going to j websitesijIt is determined that, under the conditions of without event, road network predicts OD matrixes by equation below
Try to achieve：
By the OD matrixes OD of above-mentioned formula respectively to vehiclevWith the OD matrixes OD of passengerpCalculated, so as to obtain pre-
Survey the OD matrixes without the vehicle under the conditions of event and passenger on the period.
Further, Travel Time Budget TTB (p), starting point o are givenp, intelligent body p departure time τp, considering road
Under the constraints of appearance of a street amount and Passenger Flow service ability, maximize the space-time accessibility in system scope, based on intelligence
The object function of the optimization problem of the composite communications transport network dynamic distribution model of body, is shown below：
That is, the optimization aim of model is to minimize the unreachable generalized cost of user.If intelligent body p can be pre- in the given time
Arrived in calculating, then inaccessible generalized cost is 0, otherwise, C (p) is by overall travel time of the intelligent body using path.
The technology of the present invention effect major embodiment is in the following areas：In regional response process of emergency system, how synthesis is formulated
The emergent Traffic Organization of traffic network ensures emergent traffic safety, efficient, orderly function, for improving region contingency management energy
Power, reduces the casualties and property loss caused by disaster, has important practical significance, for transportation network, special
It is not composite communications transport network, becomes it is difficult to mode quick obtaining its running status under event condition such as calculate by mathematics
Change situation and to different management and control measures or the visual effect of emergency response measure, therefore, set up regional complex transportation network
Network emulation platform, realizes that the quick loading on composite communications transport network for management and control measures of being met an urgent need to network of highways is emulated, is to comment
Valency and the important means of the emergent Traffic Organization of optimization road network.
Brief description of the drawings
Fig. 1 is the emergent simulation framework figure of regional complex transportation network of the invention；
Fig. 2 is comprehensive travel needs estimate of the invention and prediction Organization Chart；
Fig. 3 is the trip distribution modeling algorithm flow chart based on BP neural network of the invention；
Fig. 4 is the algorithm flow chart under the finite path collection based on column-generation of the present invention；
Fig. 5 is input and output design drawing of the invention.
Embodiment
A kind of comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology, including
1) the emergent simulation framework of regional complex transportation network
Regional complex transportation network meets an urgent need emulation platform mainly including comprehensive travel needs estimate and prediction module, road
Net node/section dynamic function Attribute tuning module, multi-mode dynamic traffic flow, traffic flow simulation, dynamic shortest path meter
Calculate, run several modules such as assessment, and it is pre- with the transportation network security risk based on G I S by way of interface interchange
Alert platform and Decision System of Emergency connect relatively, architecture design such as Fig. 1 institutes of the emergent emulation platform of regional complex transportation network
Show.
The key data input of analogue system includes OD demand datas in real time with history, road net traffic state operation number
According to incident data, network foundation data and road management and control data and information distribution scheme.
Comprehensive travel needs estimate is with prediction module mainly by real-time and history OD demand datas, road grid traffic shape
State service data etc. carries out pattern analysis and OD trend estimations, and Mobile state tune is entered in influence of the combination accident to transport need
It is whole, generate road network Dynamic OD requirement matrix.
Road-net node/section dynamic function attribute module is mainly the basic data by network, with reference to dynamic burst
Event data and road management and control data and information distribution scheme, generation emulation road-net node/section dynamic function attribute.
Multi-mode dynamic traffic mainly solves the Traffic Assignment Problem between a variety of travel patterns with flow module, by institute
Generation, the calculating for the selecting module for going out row mode for having path set, by vehicles distribution corresponding with people, realize intelligent body
Travel schedule, to each path of the intelligent body stroke one comprising timestamp sequence and sequence node.
Traffic flow simulation and dynamic shortest path module constitute the nucleus module of middle sight traffic simulation, traffic flow simulation mould
Block is using section transmission, node-node transmission model, on the premise of relevant constraint is met, and realizes to vehicle inside section
The simulation shifted between operation and section.Section mode is related to the magnitude of traffic flow-density relations, and basic capacity is main
By road segment classification and track quantity；Node metastasis model is related to left-hand rotation and straight trip Capacity Constraints.Dynamically distributes platform institute
Hourage of the traffic flow of simulation on section will be fed back in the dynamic shortest path model of next iteration, and then is intelligent body
Specially designated path is selected to provide decision-making foundation.Meanwhile, new pathway can be fed back to next iteration.
Operation evaluation module can be commented multiple dimensions such as, path, regions operation result from section, passage, OD
Estimate, including many aspects such as efficiency, speed, reliability, energy consumption.
The input and output of the emergent emulation platform of regional complex transportation network enter shown in Fig. 5.
2) needs estimate and forecast model combined based on trend prediction with neutral net；
Needs estimate and prediction Organization Chart such as Fig. 2.
1. entrance needs estimate
Predicted using polynomial trend and combine vehicle flowrate or passenger traffic volume point of the kalman filter method to Ingress node
Do not estimated and predicted.
J=road-net node sequence numbers, j=1,2 ..., N；
τ=departure time interval sequence number, τ=1,2 ...；
The stage of k=rolling forecasts, k=1,2,3 ...；
L=each rolls the departure time intervening sequence number included in the stage；
D(j,τ)The demand influxs of=node j in time interval τ；
Prior estimates of=node the j based on historical data in time interval τ demand influx；
μ(j,τ)=node j is in time interval τ D(j,τ)With prior estimateDemand disruption；
μ′(j,τ),μ″(j,τ),μ″′(j,τ)=demand disruption μ(j,τ)The single order of variable, second order, third order difference amount；
=D(j,τ)Estimate average；
=μ(p) (j,τ)Estimation average；
Zk=stage k state variable vector；
Yk=stage k measurement vector；
Hk=measurement vector YkWith state vector ZkMatching relationship matrix；
Wk=stages k processing noise；
Rk=stage k measurement error；
=the Z based on the observation prediction by the end of stage k-1kThat is E (Zk|Y1,Y2,…,Yk-1)；
=the Z based on the observation estimation by the end of the k stageskThat is E (Zk|Y1,Y2,…,Yk)；
Pk,k-1The Z of=stage k-1 predictionskState covariance battle array, i.e.,
Pk,k=stage k, the Z of estimationkState covariance battle array, i.e.,
Polynomial trend prediction is introduced, the prediction of τ+ζ difference vector is carried out, the function is built such as using m order polynomials
Under：
μ(j,τ+ζ)=b0+b1ζ+b2ζ2+…bpζp+…+bmζm；
According to Taylor expansion anticipation function μ(j,τ+ζ)It is extended to μ(j,τ)Multistage difference combination：
The coefficient of the formula of original polynomial distribution can be expressed as：
Three rank multinomial models are taken to carry out μ(j,τ+ζ)Prediction be shown below：
We are defined on forecast period k, τ=kl, and such as each stage forecast Period Length is that l includes 3 time intervals, that
In the k=3 stages, τ sequence number is 9；
State-transition matrix is defined as：
The predictor formula in k+1 stages is as follows：
Zk+1=AkZk；
Introduce Kalman filtering algorithm to eliminate the influence that charge station's influx measured value error band comes, because this part is straight
It is identical vector value that measured value, which is connect, with estimate, therefore H=I；
Carried out first with the posterior estimate of previous stage, the prediction of current generation and the transmission of covariance：
Introduce afterwards after stage k measured value, carry out the calculating of gain：
The transmission of formation stages k posterior estimate and covariance matrix：
Pk,k=(I-KkHk)Pk,k-1
Call above-mentioned formula forecast period k+1 μ(j,τ+ζ), the node-flow for deduction can be generated based on the predicted value
Enter value D(j,τ), and the multistage difference matrix formed needed for subsequent prediction.
2. network OD distribution proportions are predicted
OD distributions that can be respectively to vehicle in freeway net by BP neural network model are predicted and to the railway network
In go out the OD distributions of administrative staff and be predicted, pre- flow gauge is similar, as shown in figure 3, therefore, only with vehicle in freeway net
Illustrate algorithm flow exemplified by OD forecast of distribution, algorithm flow is as follows.
Step 1：Data decimation and standardization, for n charge tiny node in road network, choose before predicted time section
The OD data of m days same periods are simultaneously standardized according to formula 4.1；
Step 2：BP neural network is built, i=1 is made, for i-th of charge tiny node, the OD distributions for choosing first day are compared
Example chooses the data of second day as desired output vector as input vector, the like constitute m-1 group training samples；
Step 3：Neutral net is trained, by repeatedly training, network is reached the training objective of setting；
Step 4：OD distribution proportions are predicted, for the neutral net trained, the number of input prediction objective time interval the previous day
According to obtaining prediction distribution ratio rij(j=1,2,3 ..., n), ifAnd rij>=0, step 5 is gone to, step is otherwise gone to
Rapid 2, adjust network parameter, re -training network；
Step 5：Output prediction OD distribution proportion matrixes, the output matrix if i=n, otherwise i+1 go to step 2.
3. comprehensive travel demand OD Matrix predictions
Predict obtain road grid traffic distribution proportion respectively with BP neural network algorithm and polynomial trend prediction algorithm
Merged with after each entrance magnitude of traffic flow, it is necessary to be predicted the outcome to two kinds, under the conditions of obtaining no event, prediction period on network
Passenger or vehicle OD matrixes.By vehicle or passenger by node i to node j flow qijCan be by the stream of website i entrances
Measure OiWith with the distribution proportion r for going to j websitesijIt is determined that, then under the conditions of without event, road network prediction OD matrixes can be by as follows
Formula is tried to achieve：
OD matrixes OD that can respectively to vehicle by above-mentioned formulavWith the OD matrixes OD of passengerpCalculated, so as to obtain
The OD matrixes of vehicle and passenger under the conditions of predicted time Duan Shangwu events.
3) the multi-mode comprehensive transport capability simulation model based on intelligent body
In composite communications transport network, traveler is arrived at by different trip modes, such as railway, self-driving and group
Conjunction mode (such as highway, railway transfer).The network of this multi-mode, which can be modeled as one, can carry out the multilayer of Used in Dynamic Traffic Assignment
Secondary network, wherein, constraints is the crucial ginseng in the service of different aspects or the constraint of road passage capability, modeling process
Number, variable symbol and implication are as shown in the table.
Given Travel Time Budget TTB (p), starting point op, intelligent body p departure time τp, considering some road capacities
Under the constraints of Passenger Flow service ability, maximize the space-time accessibility in system scope, i.e., inaccessible degree
Reach minimum.The object function of the optimization problem of composite communications transport network dynamic distribution model based on intelligent body, it is as follows
Shown in formula.
That is, the optimization aim of model is to minimize the unreachable generalized cost of user.If intelligent body p can be pre- in the given time
Arrived in calculating, then inaccessible generalized cost is 0, otherwise, C (p) is by overall travel time of the intelligent body using path.
That is, object function is intended to make the quantity of destination unreachable in all travelers minimum.
There are multiple constraintss, i.e. space-time traffic flow Constraints of Equilibrium, traveler to go out row constraint, traffic flow and space in model
Capacity-constrained and the constraint of railway transport of passengers service ability etc..
1. space-time traffic flow equilibrium constraint
For all p ∈ P
Each intelligent body p only one of which starting point and a terminal are represented, on spatio-temporal state summit in addition to the start and the end points only
In accordance with flow equilibrium constraint, i.e., for intelligent body p, each spatio-temporal state summit only once reaches and once left.
2. intelligent body trip constraints
All p ∈ P
Intelligent body p, which is arrived at, either to be gone on a journey by spatio-temporal activity arc or is gone on a journey by virtual spacetime arc of motion.
3. traffic flow and path space capacity constraints
Traffic flow constraints, i.e., overall influx r constraints are described by seeing traffic flow model in spatial queue
For：
To arbitrary (i, j) ∈ Ep, t ∈ T wherein, Ai,j(t) represent on section (i, j)
Accumulative amount of reach, Di,j(t) the accumulative amount of setting out is represented, its calculation formula is respectively：
For section (i, j),The free flow travel time is represented,Represent road section length,Represent that section is blocked
Density under state.
4) Passenger Flow service ability is constrained
To owning (i, j, t, s) ∈ AT
Passenger Flow service constraints：Train can only be travelled according to the service chart of formulation on specific space-time arc, in this feelings
Under condition, its intelligent body quantity serviced is no more than vehicle bearing capacity.
5) rail-road transfer (arc) stand service ability constrain
To owning (i, j, t, s) ∈ AT
The service ability constraint of rail-road transfer stop：The transfer of different grades of junction is limited in one's ability, by hinge parking facility,
The facilities services such as facility, safety devices ability of waiting is determined, between this part is also included from road forking node to junction
Section relevant capacity consistency.
The derivation algorithm of model is the algorithm based on column-generation, and the method based on column-generation is as shown in Figure 4.
The problem of outer circulation and circulation solution internally are under finite path collection is generated based on col-generating arithmetic required
The minimum user cost path of time correlation.In each outer circulation iteration m, the cost minimization routing algorithm of time correlation
Applied to the path for marginal user cost's cost minimization that time correlation is found for every one O-D pairs and the time interval each left,
New route (if any) is added in the subset Pm in present feasible path in m iteration, and one based on two-dimensional projection
Gradient descent direction method be used to solving to define on Pm under finite path collection the problem of, if do not find new path or
Default convergence standard is reached, algorithm terminates and exports the path flow of the time correlation obtained in current iteration.
The gradient descent direction method based on two-dimensional projection obtained by iteration, is formed based on being followed in col-generating arithmetic framework
Ring, in each interior circulation iteration n, with the path flow rn of DNLE (d) model evaluations renewal, corresponding system user totle drilling cost
TE (rn) and user cost, if the difference in two continuous iteration (i.e. TE (rn)-TE (rn-1)) between desired value
Value is less than predetermined threshold value or reaches default convergence (such as n=Nmax), and interior circulation is terminated, and algorithm returns to outer circulation.
The technology of the present invention effect major embodiment is in the following areas：In regional response process of emergency system, how synthesis is formulated
The emergent Traffic Organization of traffic network ensures emergent traffic safety, efficient, orderly function, for improving region contingency management energy
Power, reduces the casualties and property loss caused by disaster, has important practical significance, for transportation network, special
It is not composite communications transport network, becomes it is difficult to mode quick obtaining its running status under event condition such as calculate by mathematics
Change situation and to different management and control measures or the visual effect of emergency response measure, therefore, set up regional complex transportation network
Network emulation platform, realizes that the quick loading on composite communications transport network for management and control measures of being met an urgent need to network of highways is emulated, is to comment
Valency and the important means of the emergent Traffic Organization of optimization road network.
The foregoing is only a specific embodiment of the invention, but protection scope of the present invention is not limited thereto, any
The change or replacement expected without creative work, should all be included within the scope of the present invention.
Claims (9)
1. a kind of comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology, it is characterised in that including：
1) the emergent simulation framework of regional complex transportation network,
Moved including comprehensive travel needs estimate and prediction module, road-net node/section dynamic function Attribute tuning module, multi-mode
State traffic flow distribution, traffic flow simulation, dynamic shortest path are calculated, operation is assessed, exported and interface display module, analogue system
Key data input includes OD demand datas, road net traffic state service data in real time with history, incident data, network
Basic data and road management and control and information distribution scheme；
2) needs estimate and forecast model combined based on trend prediction with neutral net,
Comprehensive travel needs estimate is with prediction module mainly by being transported to real-time and history OD demand datas, road net traffic state
Row data etc. carry out pattern analysis and OD trend estimations, and Mobile state adjustment is entered in influence of the combination accident to transport need,
Generate road network Dynamic OD requirement matrix；
3) the multi-mode comprehensive transport capability simulation model based on intelligent body,
Multi-mode dynamic traffic mainly solves the Traffic Assignment Problem between a variety of travel patterns with flow module, by having to all
The generation of path set, go out row mode selecting module calculating, by the vehicles it is corresponding with people distribution, realize the stroke of intelligent body
Scheme, to each path of the intelligent body stroke one comprising timestamp sequence and sequence node.
2. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 1, its
It is characterised by：Road-net node/section dynamic function attribute module is the basic data by network, with reference to dynamic accident
Data and road management and control data and information distribution scheme, generation emulation road-net node/section dynamic function attribute.
3. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 2, its
It is characterised by：Multi-mode dynamic traffic passes through to all selecting modules for having the generation of path set, going out row mode with flow module
Calculate, by vehicles distribution corresponding with people, realize the travel schedule of intelligent body, the time is included to each intelligent body stroke one
Stab the path of sequence and sequence node.
4. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 3, its
It is characterised by：Traffic flow simulation and dynamic shortest path module constitute the nucleus module of middle sight traffic simulation, traffic flow simulation
Module is using section transmission, node-node transmission model, on the premise of relevant constraint is met, and realizes to vehicle inside section
The simulation shifted between operation and section, section mode is related to the magnitude of traffic flow-density relations, and basic capacity is main
By road segment classification and track quantity；Node metastasis model is related to left-hand rotation and straight trip Capacity Constraints, dynamically distributes platform institute
Hourage of the traffic flow of simulation on section will be fed back in the dynamic shortest path model of next iteration, and then is intelligent body
Specially designated path is selected to provide decision-making foundation, meanwhile, new pathway can be fed back to next iteration.
5. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 4, its
It is characterised by：Operation evaluation module is estimated to operation result from section, passage, OD to, path, region, including efficiency, speed
In terms of degree, reliability, energy consumption.
6. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 1, its
It is characterised by, is predicted using polynomial trend and combine vehicle flowrate or passenger traffic volume point of the kalman filter method to Ingress node
Do not estimated and predicted, method is as follows：
J=road-net node sequence numbers, j=1,2 ..., N；
τ=departure time interval sequence number, τ=1,2 ...；
The stage of k=rolling forecasts, k=1,2,3 ...；
L=each rolls the departure time intervening sequence number included in the stage；
D(j,τ)The demand influxs of=node j in time interval τ；
μ′(j,τ),μ″(j,τ),μ″′(j,τ)=demand disruption μ(j,τ)The single order of variable, second order, third order difference amount；
Estimate average；
Estimation average；
Zk=stage k state variable vector；
Yk=stage k measurement vector；
Hk=measurement vector YkWith state vector ZkMatching relationship matrix；
wk=stage k processing noise；
Rk=stage k measurement error；
Pk,k-1The Z of=stage k-1 predictionskState covariance battle array, i.e.,
Pk,k=stage k, the Z of estimationkState covariance battle array, i.e.,
Polynomial trend prediction is introduced, the prediction of τ+ζ difference vector is carried out, builds the function using m order polynomials as follows：
μ(j,τ+ζ)=b0+b1ζ+b2ζ2+…bpζp+…+bmζm；
According to Taylor expansion anticipation function μ(j,τ+ζ)It is extended to μ(j,τ)Multistage difference combination：
The coefficient of the formula of original polynomial distribution can be expressed as：
Three rank multinomial models are taken to carry out μ(j,τ+ζ)Prediction be shown below：
We are defined on forecast period k, τ=kl, and such as each stage forecast Period Length is that l includes 3 time intervals, then
In the k=3 stages, τ sequence number is 9；
State-transition matrix is defined as：
The predictor formula in k+1 stages is as follows：
Zk+1=AkZk；
Introduce Kalman filtering algorithm to eliminate the influence that charge station's influx measured value error band comes, because this part is directly surveyed
Value is identical vector value with estimate, therefore H=I；
Carried out first with the posterior estimate of previous stage, the prediction of current generation and the transmission of covariance：
Introduce afterwards after stage k measured value, carry out the calculating of gain：
The transmission of formation stages k posterior estimate and covariance matrix：
Pk,k=(I-KkHk)Pk,k-1
Call above-mentioned formula forecast period k+1 μ(j,τ+ζ), can be generated based on the predicted value and flow into value for the node of deduction
D(j,τ), and the multistage difference matrix formed needed for subsequent prediction.
7. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 6, its
It is characterised by, OD distributions that can be respectively to vehicle in freeway net by BP neural network model are predicted and to the railway network
In go out administrative staff OD distribution be predicted, illustrate algorithm flow by taking the OD forecast of distribution of vehicle in freeway net as an example, calculate
Method flow is as follows：
Step 1：Data decimation and standardization, for n charge tiny node in road network, choose before predicted time section m days
The OD data of same period are simultaneously standardized according to formula 4.1；
Step 2：BP neural network is built, i=1 is made, for i-th of charge tiny node, the OD distribution proportions for choosing first day are made
For input vector, choose the data of second day as desired output vector, the like constitute m-1 group training samples；
Step 3：Neutral net is trained, by repeatedly training, network is reached the training objective of setting；
Step 4：OD distribution proportions predict that, for the neutral net trained, the data of input prediction objective time interval the previous day are obtained
To prediction distribution ratio, if going to step 5, step 2 is otherwise gone to, network parameter, re -training network is adjusted；
Step 5：Output prediction OD distribution proportion matrixes, the output matrix if i=n, otherwise i+1 go to step 2.
8. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 7, its
It is characterised by, predicts obtain road grid traffic distribution proportion respectively with BP neural network algorithm and polynomial trend prediction algorithm
After each entrance magnitude of traffic flow, two kinds are predicted the outcome and is merged, under the conditions of obtaining no event, the trip of prediction period on network
Visitor or the OD matrixes of vehicle, by vehicle or passenger by node i to node j flow qijCan be by the flow O of website i entrancesi
With with the distribution proportion r for going to j websitesijIt is determined that, under the conditions of without event, road network prediction OD matrixes are tried to achieve by equation below：
By the OD matrixes OD of above-mentioned formula respectively to vehiclevWith the OD matrixes OD of passengerpCalculated, so as to obtain in prediction
Between vehicle under the conditions of Duan Shangwu events and the OD matrixes of passenger.
9. the comprehensive traffic network operation situation towards highway and the railway network deduces emulation technology according to claim 1, its
It is characterised by, gives Travel Time Budget TTB (p), starting point Op, intelligent body p departure time τp, consider road capacity and
Under the constraints of Passenger Flow service ability, maximize the space-time accessibility in system scope, the synthesis based on intelligent body
The object function of the optimization problem of transportation network dynamically distributes model, is shown below：
That is, the optimization aim of model is to minimize the unreachable generalized cost of user.If intelligent body p can be in given time budget
Arrive at, then inaccessible generalized cost is 0, otherwise, C (p) is by overall travel time of the intelligent body using path.
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