CN106828546A - A kind of subway transportation method of flow control - Google Patents

A kind of subway transportation method of flow control Download PDF

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Publication number
CN106828546A
CN106828546A CN201710078413.5A CN201710078413A CN106828546A CN 106828546 A CN106828546 A CN 106828546A CN 201710078413 A CN201710078413 A CN 201710078413A CN 106828546 A CN106828546 A CN 106828546A
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train
conflict
discrete
track
traffic
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韩云祥
黄晓琼
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Jiangsu University of Technology
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Jiangsu University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/10—Operations, e.g. scheduling or time tables
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/10—Operations, e.g. scheduling or time tables
    • B61L27/16—Trackside optimisation of vehicle or vehicle train operation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/30—Circuit design
    • G06F30/36—Circuit design at the analogue level
    • G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods

Abstract

The present invention relates to a kind of subway transportation method of flow control, comprise the following steps：First according to the plan operational factor of each train, the topology diagram of Rail traffic network is generated；Topology diagram is based on again, analyzes the controllability and sensitiveness of train flow；Further according to the plan operational factor of each train, many train Lothrus apterus running orbits are generated；Again in each sampling instant, based on the current running status of train and historical position observation sequence, advanced positions to train certain moment in future are predicted, then set up from the continuous dynamic of train to the observer of discrete conflict logic, by the conflict situation that continuous dynamic mapping is the expression of discrete observation value；When system is possible to violate traffic control rule, to the Hybrid dynamics behavior implementing monitoring of subway transportation hybrid system, for control centre provides warning information；Finally when warning information occurs, robust dual layer resist is carried out to train operation track using Adaptive Control Theory method, and program results is transferred to each train.

Description

A kind of subway transportation method of flow control
The application is Application No.：201510150696.0, invention and created name is《A kind of flow-optimized control of subway transportation Method》, the applying date is：The divisional application of the application for a patent for invention on March 31 in 2015.
Technical field
The present invention relates to a kind of flow-optimized control method of subway transportation, more particularly to a kind of double-deck ground based on Robust Strategies Iron traffic optimization control method.
Background technology
With expanding day by day for China's big and medium-sized cities scale, Traffic Systems are faced with the increasing pressure, energetically Feasibility of developing track transportation system turns into the important means for solving urban traffic congestion.National Eleventh Five-Year Plan guiding principle is it is to be noted, that there is bar The big city and group of cities area of part are using track traffic as Priority setting.China's just one unprecedented rail of experience Road transport development peak period, some cities have been turned to the construction of net by the construction of line, urban mass transit network progressively shape Into.In the complex region that Rail traffic network and train flow are intensive, still combined using train operation plan and be based on subjective experience Train interval dispensing mode gradually show its lag, be in particular in：(1) formulation of train operation plan timetable is simultaneously Not in view of the influence of various enchancement factors, easily cause traffic flow tactics and manage crowded, reduce the safety of traffic system operation Property；(2) train scheduling work lays particular emphasis on the personal distance kept between single train, and not yet rise to carries out strategic pipe to train flow The macroscopic aspect of reason；(3) subjective experience of a line dispatcher is depended on train allocation process, the selection for allocating opportunity is random more Property it is larger, lack scientific theory support；(4) the less shadow in view of external interference factor of allotment means that dispatcher is used Ring, the robustness and availability of train programs are poor.
It is directed to long-distance railway transportation more the discussion object of existing documents and materials, and is directed to big flow, high density and closely-spaced The Scientific Regulation scheme of the city underground traffic system under service condition still lacks system design.Under complicated road network service condition Train Coordinated Control Scheme need to carry out the running status of single vehicles in transportation network in region on strategic level to calculate and Optimization, and the traffic flow implementation collaborative planning to being made up of multiple trains；Pass through effective monitoring mechanism on pre- tactical level Adjust the subregional critical operational parameters in transportation network top to solve congestion problems, and ensure the fortune of all trains in the region Line efficiency；The running status of related train is then adjusted according to critical operational parameters on tactical level, single-row wheel paths are obtained Prioritization scheme, consideration train performance, scheduling rule and extraneous ring are changed into by the headway management of train from fixed manual type The factors such as border are in interior variable " microcosmic-macroscopic view-middle sight-microcosmic " Separation control mode.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of robustness and the preferable subway transportation flow control side of availability Method, the method can strengthen the subject of programs formulation and can effectively prevent subway train from running conflict.
Realize that the technical scheme of the object of the invention is to provide a kind of subway transportation method of flow control, comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze train flow controllability and The class feature of sensitiveness two；
Step C, the plan operational factor according to each train, on the basis of Modeling Method for Train Dynamics is built, according to row Car operation conflict Coupling point sets up train running conflict and allocates model in advance, generates many train Lothrus apterus running orbits；
Step D, in each sampling instant t, based on the current running status of train and historical position observation sequence, to train The advanced positions at following certain moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, with train initiating station stop position as the origin of coordinates, adopted each Sample moment, the original discrete two-dimensional position sequence x=[x of train acquired in1,x2,…,xn] and y=[y1,y2,…,yn], Treatment is carried out to it using first-order difference method and obtains new train discrete location sequence △ x=[△ x1,△x2,…,△xn-1] With △ y=[△ y1,△y2,…,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, to train track data cluster, to train discrete two-dimensional position sequence △ x and △ y new after treatment, lead to Setting cluster number M' is crossed, it is clustered respectively using K-means clustering algorithms；
Step D3, parameter training is carried out using HMM to the train track data after cluster, by will place Train operation track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number N' and parameter update period τ ', roll and obtain newest hidden Ma Erke according to T' nearest position detection value and use B-W algorithms Husband's model parameter λ '；Specifically：By the train track sets data length for being obtained is dynamic change, in order in real time with The state change of track train track, it is necessary to initial track HMM parameter lambda '=(π, A, B) on the basis of it is right It is readjusted, more accurately to speculate train in the position at following certain moment；Every period τ ', according to the T' of newest acquisition Individual observation (o1,o2,…,oT') to track HMM parameter lambda '=(π, A, B) reevaluated；
Step D4, foundation HMM parameter, are obtained corresponding to current time observation using Viterbi algorithm Hidden state q；
Step D5, every the periodHMM parameter lambda according to newest acquisition '=(π, A, B) and nearest H History observation (o1,o2,…,oH), the hidden state q based on train current time, in moment t, by setting prediction time domain h', Obtain the position prediction value O of future time period train；
Step E, set up from the continuous dynamic of train to the observer of discrete conflict logic, by the continuous of subway transportation system Dynamic mapping is the conflict situation of discrete observation value expression；When system is possible to violate traffic control rule, to subway transportation The Hybrid dynamics behavior implementing monitoring of hybrid system, for control centre provides timely warning information；
Step F, when warning information occurs, meeting train physical property, region hold stream constraint and track traffic scheduling On the premise of rule, by setting optimizing index function, Shandong is carried out to train operation track using Adaptive Control Theory method Rod dual layer resist, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each train is equal Reach it and free terminal.
Further, the detailed process of step A is as follows：
Step A1, the database from subway transportation control centre extract the website letter stopped in each train travelling process Breath；
Step A2, the site information that each train is stopped is classified according to positive and negative two traffic directions, and will be same Same site on one traffic direction is merged；
Step A3, according to website amalgamation result, multiple websites before and after being connected with straight line according to the space layout form of website.
Further, the detailed process of step B is as follows：
Step Bl, the Traffic flux detection model built in single subsegment；Its detailed process is as follows：
Step Bl.1, introduce state variable Ψ, input variable u and output variable Ω, wherein Ψ represent website between phase link The train quantity that certain moment is present in section, it includes single channel section and Multiple Sections two types, and u represents that track traffic dispatcher is directed to The Operation Measures that certain section is implemented, such as adjust train speed or change train in the station time, Ω represents certain section period On the train quantity left；
Step B1.2, by by time discretization, setting up shape such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1 Ψ(t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t represent sampling interval, Ψ (t) tables Show the state vector of t, A1、B1、C1And D1State-transition matrix, input matrix, the output measurement square of t are represented respectively Battle array and direct transmission matrix；
Step B2, the Traffic flux detection model built in many subsegments；Its detailed process is as follows：
Step B2.1, according to circuit space layout form and train flow historical statistical data, obtain each son of cross link Flow proportional parameter beta in section；
Step B2.2, according to the discrete time Traffic flux detection model in flow proportional parameter and single subsegment, build shape Such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ(t)+D1Discrete time traffic flow control in many subsegments of u (t) Simulation；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,…,A1 n-1B1] order and numerical value n relation, Qualitative analysis its controllability, the sensitivity coefficient matrix [C according to Controlling model1(zI-A1)-1B1+D1], its input of quantitative analysis is defeated Go out sensitiveness, wherein n represents the dimension of state vector, and I represents unit matrix, and z is represented to original discrete time Traffic flux detection The element factor that model is changed.
Further, the detailed process of step C is as follows：
Step C1, train status transfer modeling, train are shown as between website along the process that track traffic road network runs Switching at runtime process, the website in train operation plan is set, and sets up single train switched and transferred between different websites Petri net model：(g, G, Pre, Post m) are train section metastasis model, wherein g each sub- section, G tables between representing website to E= Show the transfer point of train running speed state parameter, Pre and Post represents front and rear to connection between each sub- section and website respectively Relation,The operation section residing for train is represented, wherein m represents model identification, Z+Represent Positive Integer Set；
The full operation profile hybrid system modeling of step C2, train, the operation by train between website is considered as continuous process, from The stress situation of train is set out, and kinetics equation of the train in the different operation phase is derived according to energy model, dry with reference to the external world Factor is disturbed, is set up on train in a certain operation phase speed vGMapping function vG=λ (T1,T2, H, R, α), wherein T1、T2、 H, R and α represent tractive force of train, braking force of train, train resistance, train gravity and train status random fluctuation parameter respectively；
Step C3, solution train track is speculated by the way of emulation is mixed, by the way that by time subdivision, utilization state is continuous Distance of the characteristic Recursive Solution any time train of change in a certain operation phase away from initial rest position point,Wherein J0Voyage for initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is distance of the τ moment train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are modeled in station time probability distribution function, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of different circuit difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, according to each train in advance up to the time of conflict point, pass through Time segments division, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being discontented near conflict point Implement robust secondary planning in the train track of sufficient personal distance requirement.
Further, in step D, the value of cluster number M' is 4, and the value of hidden state number N' is 3, and parameter updates period τ ' It is 30 seconds, T' is 10,It it is 30 seconds, H is 10, prediction time domain h' is 300 seconds.
Further, the specific implementation process of step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Set up hypersurface collection of functions and be used to reflect and be The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two row The related continuous function h of carIIIt is Type-II hypersurface；
Step E2, set up by train continuous state to discrete conflict situation observer, build train in traffic network The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dijT () represents train i and train j between the reality of t Every dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build Train monitor in real time mechanism of the people in loop, it is ensured that the operation of system is in safe reachable set, design solution from conflicting to conflicting The discrete watch-dog of section of slipping out of the hand, when the discrete observation vector of observer shows that safety regulation rally is breached, sends phase at once The warning information answered.
Further, the detailed process of step F is as follows：
Step F1, the analysis result based on step B and step E, it is determined that the traffic flow regulation measure specifically taken, including The speed of service and/or adjustment train of train are adjusted in the station class measure of time two, and using above regulation measure specifically Point and opportunity；
Step F2, termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions of the collision avoidance planning of setting train Time domain Υ；
Step F3, operation conflict Resolution process model building, the operation conflict Resolution in the above-listed workshop of Rail traffic network is considered as Inside and outside dual planning problem based on both macro and micro aspect, whereinRepresent outer layer plan model, i.e. rail Train flow flow-Density and distribution problem on road traffic network,Represent internal layer plan model, i.e. track traffic The state adjustment problem of single vehicles on section；F、x1And u1It is respectively object function, state vector and the decision-making of outer layer planning problem Vector, G (x1,u1)≤0 is the constraints of outer layer planning, f, x2And u2It is respectively object function, the state of internal layer planning problem Vector sum decision vector, g (x2,u2)≤0 is the constraints of internal layer planning, using the outer layer program results of macroscopic aspect as micro- The reference input of sight aspect internal layer planning；
Step F4, the variable bound modeling of operation conflict Resolution, build and include adjustable train quantity a, train speed ω and row Car is in variables such as station times γ in interior both macro and micro constraints：Wherein t need to implement the change of the section k of conflict Resolution Amount constraint can be described as：ak(t)≤aM、ωk(t)≤ωM、γk(t)≤γM, aM、ωM、γMRespectively maximum adjustable train number , in the station time, such variable of freeing can be subject to traffic flow distribution, train thing for amount, maximum train running speed and train most long The constraint of the aspect such as rationality energy and personal distance；
The optimal road network flow allocation plan of step F5, Multi-objective Robust is solved：Based on cooperative collision avoidance trajectory planning thought, For different performance indications, by selecting different conflict Resolution object functions, base is solved in traffic flow operation macroscopic aspect In Euler's network model multiple target traffic flow optimum flow allocation plan and each control section is only real in Rolling Planning interval Apply its first Optimal Control Strategy；
The optimal section train operation state adjustment of step F6, Multi-objective Robust：According to each section or zone flow configuration knot Really, evolutionary model is mixed based on train operation and Lagrangian plan model obtains optimal single vehicles controlled quentity controlled variable, generated optimal Single vehicles running orbit and each regulation and control train only implements its first Optimal Control Strategy in Rolling Planning interval；
Step F7, each train are received and perform train collision avoidance instruction；
Step F8, in next sampling instant, repeat step F5 to F7 is until each train reaches it and frees terminal.
Further, in step F2, it is next website that stops of train to terminate reference point locations P, and the value of parameter Θ is 300 seconds, the value of Υ was 300 seconds.
Further, the detailed process of step F5 is as follows：Order
WhereinRepresent distance between t train i present positions and next website square, Pi(t)=(xit, yit) two-dimensional coordinate value of t train i is represented,The next two-dimensional coordinate values for stopping website of train i are represented, The priority index of so t train i may be set to：
Wherein ntRepresent there is the train number of conflict on t section, from the implication of priority index, train away from From next website more close to, its priority is higher；
Setting optimizing index
Wherein i ∈ I (t) represent train code and I (t)=1,2 ..., nt, Pi(t+s △ t) represents train in moment (t+ S △ t) position vector, Π represents control time, i.e., the time span of Future Trajectory planning, u from current timeiExpression is treated The optimal control sequence of the train i of optimization, QitIt is positive definite diagonal matrix, its diagonal element refers to for train i in the priority of t Number λit, and
The present invention has positive effect：(1) subway transportation method of flow control of the invention is meeting track traffic control On the premise of personal distance, based on the real-time position information of train, maintenance data excavates means and dynamically speculates train track； According to track traffic regulation rule, alarm is implemented in the conflict to being likely to occur, according to train performance data and relevant constraint Give each train planning conflict Resolution track；When being configured to train schedule, it is contemplated that influence all kinds of of train The probability distribution of random factor and the robustness of train schedule, strengthen the availability of configuration result.
(2) controllability and sensitivity analysis result of the present invention based on Rail traffic network topological structure, can hand over for subway Through-flow allotment time, the selection in allotment place and allotment means provide scientific basis, it is to avoid the randomness that regulation and control scheme is chosen.
(3) scene monitoring mechanism of the present invention based on constructed " people is in loop ", can be to train inside continuous variable Frequent interaction with external discrete event makes effecting reaction in time, overcomes the shortcoming of conventional open loop monitored off-line scheme.
(4) the dual layer resist scheme of train flow of the invention can not only reduce the solution dimension of Optimal Control Problem, also The practicality of regulation and control scheme can be strengthened, overcome model and algorithm in existing document only focus on train AT STATION to hair when Between, and lack the defect of control when being run on specific railroad section to train and prediction.
(5) present invention can in time be incorporated train and be transported in real time based on constructed train operation track rolling forecast scheme All kinds of disturbing factors in row, improve the accuracy of train trajectory predictions, overcome Conventional Off-line prediction scheme accuracy not high Shortcoming.
Brief description of the drawings
Fig. 1 is train flow analysis on Operating figure；
Fig. 2 is that Lothrus apterus 3D robusts track speculates figure；
Fig. 3 mixes monitoring figure for train operation state；
Fig. 4 frees figure for train running conflict is optimal；
Fig. 5 is the schematic diagram of traffic flow bilayer allocation plan.
Specific embodiment
(embodiment 1)
A kind of flow-optimized control system of subway transportation, including it is wire topologies generation module, data transmission module, vehicle-mounted Terminal module, control terminal module and track monitoring module, track monitoring module are collected the status information of train and are supplied to Control terminal module.
The control terminal module includes following submodule：
Lothrus apterus Track Pick-up module before train operation：According to Train operation plan time of running table, train dynamicses are initially set up Model is learned, then train running conflict is set up according to train running conflict Coupling point and is allocated model in advance, ultimately produce Lothrus apterus row Car running orbit.
Train operation middle or short term Track Pick-up module：According to the train real time status information that track monitoring module is provided, profit With data mining model, thus it is speculated that the running orbit of train in future time period.
Train operation situation monitoring module：In each sampling instant t, the track estimation result based on train, when between train When being possible to occur violating the situation of safety regulation, to its dynamic behaviour implementing monitoring and for control terminal provides warning information.
Train collision avoidance track optimizing module：When train operation situation monitoring module sends warning information, train is being met On the premise of physical property, region hold stream constraint and track traffic scheduling rule, by setting optimizing index function, using adaptive Answer control theory method carries out robust dual layer resist by control terminal module to train operation track, and by data transmission module Program results is transferred into car-mounted terminal module to perform.Train collision avoidance track optimizing module includes internal layer planning and outer layer planning two Class planning process.
Using the subway transportation method of flow control of the flow-optimized control system of above-mentioned subway transportation, comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；Its is specific Process is as follows：
Step A1, the database from subway transportation control centre extract the website letter stopped in each train travelling process Breath；
Step A2, the site information that each train is stopped is classified according to positive and negative two traffic directions, and will be same Same site on one traffic direction is merged；
Step A3, according to website amalgamation result, multiple websites before and after being connected with straight line according to the space layout form of website.
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze train flow controllability and The class feature of sensitiveness two；Its detailed process is as follows：
Step Bl, see Fig. 1, build the Traffic flux detection model in single subsegment；Its detailed process is as follows：
Step Bl.1, introduce state variable Ψ, input variable u and output variable Ω, wherein Ψ represent website between phase link The train quantity that certain moment is present in section, it includes single channel section and Multiple Sections two types, and u represents that track traffic dispatcher is directed to The Operation Measures that certain section is implemented, such as adjust train speed or change train in the station time, Ω represents certain section period On the train quantity left；
Step B1.2, by by time discretization, setting up shape such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1 Ψ(t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t represent sampling interval, Ψ (t) tables Show the state vector of t, A1、B1、C1And D1State-transition matrix, input matrix, the output measurement square of t are represented respectively Battle array and direct transmission matrix；
Step B2, the Traffic flux detection model built in many subsegments；Its detailed process is as follows：
Step B2.1, according to circuit space layout form and train flow historical statistical data, obtain each son of cross link Flow proportional parameter beta in section；
Step B2.2, according to the discrete time Traffic flux detection model in flow proportional parameter and single subsegment, build shape Such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ(t)+D1Discrete time traffic flow control in many subsegments of u (t) Simulation；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,…,A1 n-1B1] order and numerical value n relation, Qualitative analysis its controllability, the sensitivity coefficient matrix [C according to Controlling model1(zI-A1)-1B1+D1], its input of quantitative analysis is defeated Go out sensitiveness, wherein n represents the dimension of state vector, and I represents unit matrix, and z is represented to original discrete time Traffic flux detection The element factor that model is changed；
Step C, see Fig. 2, the plan operational factor according to each train, on the basis of Modeling Method for Train Dynamics is built, Train running conflict is set up according to train running conflict Coupling point and allocate model in advance, generate many train Lothrus apterus running orbits；Its Detailed process is as follows：
Step C1, train status transfer modeling, train are shown as between website along the process that track traffic road network runs Switching at runtime process, the website in train operation plan is set, and sets up single train switched and transferred between different websites Petri net model：(g, G, Pre, Post m) are train section metastasis model, wherein g each sub- section, G tables between representing website to E= Show the transfer point of train running speed state parameter, Pre and Post represents front and rear to connection between each sub- section and website respectively Relation,The operation section residing for train is represented, wherein m represents model identification, Z+Represent Positive Integer Set；
The full operation profile hybrid system modeling of step C2, train, the operation by train between website is considered as continuous process, from The stress situation of train is set out, and kinetics equation of the train in the different operation phase is derived according to energy model, dry with reference to the external world Factor is disturbed, is set up on train in a certain operation phase speed vGMapping function vG=λ (T1,T2, H, R, α), wherein T1、T2、 H, R and α represent tractive force of train, braking force of train, train resistance, train gravity and train status random fluctuation parameter respectively；
Step C3, solution train track is speculated by the way of emulation is mixed, by the way that by time subdivision, utilization state is continuous Distance of the characteristic Recursive Solution any time train of change in a certain operation phase away from initial rest position point,Wherein J0Voyage for initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is distance of the τ moment train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are modeled in station time probability distribution function, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of different circuit difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, according to each train in advance up to the time of conflict point, pass through Time segments division, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being discontented near conflict point Implement robust secondary planning in the train track of sufficient personal distance requirement.
Step D, in each sampling instant t, based on the current running status of train and historical position observation sequence, to train The advanced positions at following certain moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, with train initiating station stop position as the origin of coordinates, adopted each Sample moment, the original discrete two-dimensional position sequence x=[x of train acquired in1,x2,…,xn] and y=[y1,y2,…,yn], Treatment is carried out to it using first-order difference method and obtains new train discrete location sequence △ x=[△ x1,△x2,…,△xn-1] With △ y=[△ y1,△y2,…,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, to train track data cluster, to train discrete two-dimensional position sequence △ x and △ y new after treatment, lead to Setting cluster number M' is crossed, it is clustered respectively using K-means clustering algorithms；
Step D3, parameter training is carried out using HMM to the train track data after cluster, by will place Train operation track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number N' and parameter update period τ ', roll and obtain newest hidden Ma Erke according to T' nearest position detection value and use B-W algorithms Husband's model parameter λ '；Specifically：By the train track sets data length for being obtained is dynamic change, in order in real time with The state change of track train track, it is necessary to initial track HMM parameter lambda '=(π, A, B) on the basis of it is right It is readjusted, more accurately to speculate train in the position at following certain moment；Every period τ ', according to the T' of newest acquisition Individual observation (o1,o2,…,oT') to track HMM parameter lambda '=(π, A, B) reevaluated；
Step D4, foundation HMM parameter, are obtained corresponding to current time observation using Viterbi algorithm Hidden state q；
Step D5, every the periodHMM parameter lambda according to newest acquisition '=(π, A, B) and nearest H Individual history observation (o1,o2,…,oH), the hidden state q based on train current time, in moment t, by setting prediction time domain H', obtains the position prediction value O of future time period train；
The value of above-mentioned cluster number M' is 4, and the value of hidden state number N' is 3, and parameter renewal period τ ' is 30 seconds, and T' is 10,It it is 30 seconds, H is 10, prediction time domain h' is 300 seconds.
Step E, see Fig. 3, set up from the continuous dynamic of train to the observer of discrete conflict logic, by subway transportation system Continuous dynamic mapping for discrete observation value expression conflict situation；When system is possible to violate traffic control rule, over the ground The Hybrid dynamics behavior implementing monitoring of iron traffic hybrid system, for control centre provides timely warning information；
The specific implementation process of the step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Set up hypersurface collection of functions and be used to reflect and be The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two row The related continuous function h of carIIIt is Type-II hypersurface；
Step E2, set up by train continuous state to discrete conflict situation observer, build train in traffic network The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dijT () represents train i and train j between the reality of t Every dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build Train monitor in real time mechanism of the people in loop, it is ensured that the operation of system is in safe reachable set, design solution from conflicting to conflicting The discrete watch-dog of section of slipping out of the hand, when the discrete observation vector of observer shows that safety regulation rally is breached, sends phase at once The warning information answered.
Step F, see Fig. 4, when warning information occurs, train physical property, region hold stream constraint and track is handed over meeting On the premise of logical scheduling rule, by setting optimizing index function, using Adaptive Control Theory method to train operation track Robust dual layer resist is carried out, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each Train reaches it and frees terminal；Its detailed process is as follows：
Step F1, the analysis result based on step B3 and step E3, it is determined that the traffic flow regulation measure specifically taken, bag The speed of service and/or adjustment train of adjustment train are included in the class measure of station time two, and using the specific of above regulation measure Place and opportunity；
Step F2, termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions of the collision avoidance planning of setting train Time domain Υ；
It is next website that stops of train to terminate reference point locations P, and the value of parameter Θ is 300 seconds, and the value of Υ is 300 Second；
Step F3, operation conflict Resolution process model building, the operation conflict Resolution in the above-listed workshop of Rail traffic network is considered as Inside and outside dual planning problem based on both macro and micro aspect, is shown in Fig. 5, whereinRepresent outer layer planning mould Train flow flow-Density and distribution problem in type, i.e. track traffic road network,Internal layer plan model is represented, i.e., The state adjustment problem of single vehicles on track traffic section；F、x1And u1Be respectively the object function of outer layer planning problem, state to Amount and decision vector, G (x1,u1)≤0 is the constraints of outer layer planning, f, x2And u2It is respectively the target of internal layer planning problem Function, state vector and decision vector, g (x2,u2)≤0 is the constraints of internal layer planning, and the outer layer of macroscopic aspect is planned into knot The reference input that fruit is planned as microcosmic point internal layer；
Step F4, the variable bound modeling of operation conflict Resolution, build and include adjustable train quantity a, train speed ω and row Car is in variables such as station times γ in interior both macro and micro constraints：Wherein t need to implement the change of the section k of conflict Resolution Amount constraint can be described as：ak(t)≤aM、ωk(t)≤ωM、γk(t)≤γM, aM、ωM、γMRespectively maximum adjustable train number , in the station time, such variable of freeing can be subject to traffic flow distribution, train thing for amount, maximum train running speed and train most long The constraint of the aspect such as rationality energy and personal distance；
The optimal road network flow allocation plan of step F5, Multi-objective Robust is solved：Based on cooperative collision avoidance trajectory planning thought, For different performance indications, by selecting different conflict Resolution object functions, base is solved in traffic flow operation macroscopic aspect In Euler's network model multiple target traffic flow optimum flow allocation plan and each control section is only real in Rolling Planning interval Apply its first Optimal Control Strategy；Its detailed process is as follows：Order
WhereinRepresent distance between t train i present positions and next website square, Pi(t)=(xit, yit) two-dimensional coordinate value of t train i is represented,The next two-dimensional coordinate values for stopping website of train i are represented, The priority index of that so t train i may be set to：
Wherein ntRepresent there is the train number of conflict on t section, from the implication of priority index, train away from From next website more close to, its priority is higher；
Setting optimizing index
Wherein i ∈ I (t) represent train code and I (t)=1,2 ..., nt, Pi(t+s △ t) represents train in moment (t+ S △ t) position vector, Π represents control time, i.e., the time span of Future Trajectory planning, u from current timeiExpression is treated The optimal control sequence of the train i of optimization, QitIt is positive definite diagonal matrix, its diagonal element refers to for train i in the priority of t Number λit, and
The optimal section train operation state adjustment of step F6, Multi-objective Robust：According to each section or zone flow configuration knot Really, evolutionary model is mixed based on train operation and Lagrangian plan model obtains optimal single vehicles controlled quentity controlled variable, generated optimal Single vehicles running orbit and each regulation and control train only implements its first Optimal Control Strategy in Rolling Planning interval；
Step F7, each train are received and perform train collision avoidance instruction；
Step F8, in next sampling instant, repeat step F5 to F7 is until each train reaches it and frees terminal.
Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to of the invention The restriction of implementation method.For those of ordinary skill in the field, it can also be made on the basis of the above description The change or variation of its multi-form.There is no need and unable to be exhaustive to all of implementation method.And these belong to this hair Obvious change that bright spirit is extended out or among changing still in protection scope of the present invention.

Claims (1)

1. a kind of subway transportation method of flow control, it is characterised in that comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze the controllability and sensitivity of train flow Two class features of property；
Step C, the plan operational factor according to each train, on the basis of Modeling Method for Train Dynamics is built, according to train fortune Row conflict Coupling point sets up train running conflict and allocates model in advance, generates many train Lothrus apterus running orbits；
Step D, in each sampling instant t, based on the current running status of train and historical position observation sequence, to train future The advanced positions at certain moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, with train initiating station stop position as the origin of coordinates, in each sampling Carve, the original discrete two-dimensional position sequence x=[x of train acquired in1,x2,…,xn] and y=[y1,y2,…,yn], use First-order difference method carries out treatment to it and obtains new train discrete location sequence △ x=[△ x1,△x2,…,△xn-1] and △ y =[△ y1,△y2,…,△yn-1], wherein △ xi=xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, to train track data cluster, to train discrete two-dimensional position sequence △ x and △ y new after treatment, by setting Surely number M' is clustered, it is clustered respectively using K-means clustering algorithms；
Step D3, parameter training is carried out using HMM to the train track data after cluster, by will be after treatment Train operation track data △ x and △ y be considered as the aobvious observation of hidden Markov models, by set hidden state number N' and Parameter updates period τ ', rolls and obtains newest Hidden Markov mould according to T' nearest position detection value and use B-W algorithms Shape parameter λ '；Specifically：By the train track sets data length for being obtained is dynamic change, in order to real-time tracking is arranged The state change of wheel paths, it is necessary to initial track HMM parameter lambda '=(π, A, B) on the basis of it is heavy to its New adjustment, more accurately to speculate train in the position at following certain moment；Every period τ ', according to T' sight of newest acquisition Measured value (o1,o2,…,oT') to track HMM parameter lambda '=(π, A, B) reevaluated；
Step D4, foundation HMM parameter, obtain hidden corresponding to current time observation using Viterbi algorithm State q；
Step D5, every the periodHMM parameter lambda according to newest acquisition '=(π, A, B) and nearest H history Observation (o1,o2,…,oH), the hidden state q based on train current time, in moment t, by setting prediction time domain h', is obtained The position prediction value O of future time period train；
Step E, set up from the continuous dynamic of train to the observer of discrete conflict logic, by the continuous dynamic of subway transportation system It is mapped as the conflict situation of discrete observation value expression；When system is possible to violate traffic control rule, subway transportation is mixed The Hybrid dynamics behavior implementing monitoring of system, for control centre provides timely warning information；
The specific implementation process of step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Hypersurface collection of functions is set up to be used to reflect system Contention situation, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two train phases The continuous function h of passIIIt is Type-II hypersurface；
Step E2, foundation are built train and are run in traffic network by the observer of train continuous state to discrete conflict situation When the safety regulation collection d that need to meetij(t)≥dmin, wherein dijT () represents actual intervals of the train i and train j in t, dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build people and exist The train monitor in real time mechanism of loop, it is ensured that the operation of system is in safe reachable set, design is from conflict to conflict Resolution hand The discrete watch-dog of section, when the discrete observation vector of observer shows that safety regulation rally is breached, sends corresponding at once Warning information；
Step F, when warning information occurs, meeting train physical property, region hold stream constraint and track traffic scheduling rule On the premise of, by setting optimizing index function, robust is carried out to train operation track using Adaptive Control Theory method double Layer planning, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each train is reached It frees terminal.
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Application publication date: 20170613