CN106864482A - A kind of subway conflict method for early warning - Google Patents

A kind of subway conflict method for early warning Download PDF

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Publication number
CN106864482A
CN106864482A CN201710078598.XA CN201710078598A CN106864482A CN 106864482 A CN106864482 A CN 106864482A CN 201710078598 A CN201710078598 A CN 201710078598A CN 106864482 A CN106864482 A CN 106864482A
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train
conflict
discrete
track
subway
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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
    • B61L23/00—Control, warning, or like safety means along the route or between vehicles or vehicle trains
    • 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
    • B61L27/20—Trackside control of safe travel of vehicle or vehicle train, e.g. braking curve calculation
    • 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
    • B61L27/40—Handling position reports or trackside vehicle data
    • 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
    • B61L27/60—Testing 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 conflict method for early warning, 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.The present invention rolls subway train track is predicted in real time, effective early warning train conflict, improves the security of subway transportation.

Description

A kind of subway conflict method for early warning
The application is Application No.：201510150113.4, invention and created name is《A kind of pre- police of subway train conflict 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 subway train conflict method for early warning, more particularly to a kind of subway train based on Robust Strategies Conflict method for early warning.
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.To ensure the safe operation of subway transportation, implement effective conflict pre- The alert emphasis for just turning into subway transportation control work.Implementing effective subway conflict early warning just turns into subway transportation control work Emphasis.
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 The subregional critical operational parameters in transportation network top are adjusted to solve congestion problems, but the prediction to subway train track at present And see train conflict early warning without accurate scheme.
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 conflict method for early warning of availability, The method is higher to the precision of prediction of subway train, the accuracy of subway train conflict early warning and ageing preferable.
Realize that the technical scheme of the object of the invention is to provide a kind of subway conflict method for early warning, 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] 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, 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；
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 subway transportation control centre provides timely warning information.
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 continuously becomes 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 numerical value of time window, and J (τ) is τ moment trains Away from the distance of initial rest position point, thereby it is assumed that and obtain 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, at once to subway Traffic control center sends corresponding warning information.
The present invention has positive effect：(1) subway conflict method for early warning of the invention is meeting track traffic control peace On the premise of full interval, based on the real-time position information of train, maintenance data excavates means and dynamically speculates train track；According to According to track traffic regulation rule, alarm is implemented in the conflict to being likely to occur, to the early warning effect that conflicts preferably, can effectively, accurately, The track of train is predicted in real time and train conflict is predicted, effectively improves the security of subway transportation.
(2) controllability and sensitivity analysis result of the present invention based on Rail traffic network topological structure, can hand over for subway Through-flow early warning provides scientific basis, overcomes the randomness of conventional early warning scheme selection.
(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) 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.
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 conflict method for early warning 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] 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, 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 subway transportation 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, at once to subway Traffic control center sends corresponding warning information.
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 conflict method for early warning, 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；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, introducing state variable Ψ, input variable u and output variable Ω, wherein Ψ are connected on section between representing website The train quantity that certain moment is present, it includes single channel section and Multiple Sections two types, and u represents that track traffic dispatcher is directed to certain road The Operation Measures implemented of section, such as adjust train speed or change train in the station time, Ω represent on certain section period from The train quantity opened；
Step B1.2, by by time discretization, setting up shape such as Ψ (t+ △ t)=A1Ψ(t)+B1U (t) and Ω (t)=C1Ψ (t)+D1Discrete time Traffic flux detection model in the single subsegment of u (t), wherein △ t represent the sampling interval, and Ψ (t) represents t The state vector at moment, A1、B1、C1And D1Represent respectively the state-transition matrix of t, input matrix, output calculation matrix 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 in each subsegment of cross link Flow proportional parameter beta；
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 flux detection mould in many subsegments of u (t) Type；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relation, it is qualitative Its controllability is analyzed, the sensitivity coefficient matrix [C according to Controlling model1(zI-A1)-1B1+D1], its input and output of quantitative analysis are quick Perception, wherein n represent the dimension of state vector, and I represents unit matrix, and z is represented to original discrete time Traffic flux detection model The element factor changed；
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], adopt Treatment is carried out to it with first-order difference method 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 subway transportation control centre provides timely warning information.
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