CN106938655A - Subway transportation conflict method for early warning - Google Patents

Subway transportation conflict method for early warning Download PDF

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Publication number
CN106938655A
CN106938655A CN201710208658.5A CN201710208658A CN106938655A CN 106938655 A CN106938655 A CN 106938655A CN 201710208658 A CN201710208658 A CN 201710208658A CN 106938655 A CN106938655 A CN 106938655A
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China
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train
track
conflict
time
website
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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
    • 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
    • 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 transportation 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 certain following moment of train are predicted, then set up from the continuous dynamic of train to the observer of discrete conflict logic, be the conflict situation that discrete observation value is expressed by continuous dynamic mapping；When system is possible to violate traffic control rule, to the Hybrid dynamics behavior implementing monitoring of subway transportation hybrid system, warning information is provided for control centre.The present invention rolls and subway train track is predicted in real time, effective early warning train conflict, improves the security of subway transportation.

Description

Subway transportation conflict method for early warning
The application is Application No.：201510150771.3, invention and created name is《Subway train conflict method for early warning》, The applying date is：The divisional application of the application for a patent for invention on March 31st, 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 assign 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 The influence of various enchancement factors is not considered, is easily caused traffic flow tactics and is managed crowded, the safety of reduction traffic system operation Property；(2) train scheduling work lays particular emphasis on the personal distance kept between single train, not yet rises to and strategic pipe is carried out 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- It is alert just to turn into the emphasis that subway transportation control works.Implementing effective subway conflict early warning just turns into what subway transportation control worked Emphasis.
Being directed to long-distance railway transportation the discussion object of existing documents and materials more, and for big flow, high density and closely-spaced The Scientific Regulation scheme of city underground traffic system under service condition still lacks system design.Under complicated road network service condition Train Coordinated Control Scheme needed on strategic level in region in transportation network single vehicles running status carry out calculate and Optimization, and collaborative planning is implemented to the traffic flow 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 train conflict early warning is 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 transportation conflict early warning of availability Method, the accuracy for early warning that this method is higher to the precision of prediction of subway train, subway train conflicts and ageing preferable.
Realize that one of technical scheme of the object of the invention is to provide a kind of subway transportation conflict method for early warning, including following step Suddenly：
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 certain following moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, using train initiating station stop position as the origin of coordinates, adopted each The sample moment, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,..., yn], processing 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 processing, lead to Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step D3, to the train track data after cluster using HMM carry out parameter training, 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 ', are rolled according to T' nearest position detection value and using B-W algorithms and obtain newest hidden Ma Erke Husband's model parameter λ '；Specifically：By the train track sets data length 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 certain following moment；Every period τ ', the T' according to 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 periodAccording to the HMM parameter lambda of 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, foundation are 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 that discrete observation value is expressed；When system is possible to violate traffic control rule, to subway transportation The Hybrid dynamics behavior implementing monitoring of hybrid system, timely warning information is provided for subway transportation control centre.
Further, step A detailed process is as follows：
The website letter that step A1, the database from subway transportation control centre are stopped in extracting each train travelling process Breath；
Step A2, the site information stopped according to positive and negative two traffic directions to each train are classified, 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 according to the space layout form of website with straight line.
Further, step B detailed process 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 represent phase link between website 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 u (t) single subsegment, wherein △ t represent sampling interval, Ψ (t) tables Show the state vector of t, A1、B1、C1And D1The state-transition matrix, input matrix, 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 u (t) many subsegments Simulation；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relation, Its controllability of qualitative analysis, according to the sensitivity coefficient matrix [C of 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, step C detailed process is as follows：
Step C1, train status transfer modeling, the process that train is run along track traffic road network are shown as between website 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 are m) train section metastasis model, wherein g represents each sub- section, G tables between website to E= Show the transfer point of train running speed state parameter, Pre and Post represent 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, continuous process is considered as by operation of the train between website, 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 The characteristic Recursive Solution any time train of change in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, is passed through in advance 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, cluster number M' value is 4, and hidden state number N' value is 3, and parameter updates period τ ' For 30 seconds, T' was 10,For 30 seconds, H was 10, and prediction time domain h' is 300 seconds.
Further, step E specific implementation process is as follows：
Step E1, conflict hypersurface collection of functions of the construction based on regulation rule：Set up hypersurface collection of functions is to reflect The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIFor I type hypersurfaces, arranged with two The related continuous function h of carIIFor Type-II hypersurface；
Step E2, foundation build train in traffic network by the observer of train continuous state to discrete conflict situation The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dij(t) represent 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 real-time monitoring 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 monitor for 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.
Realize that the two of the technical scheme of the object of the invention is to provide a kind of flow-optimized control of subway transportation for the early warning that conflicts System processed, including wire topologies generation module, data transmission module, car-mounted terminal module, control terminal module and rail Mark monitoring module, track monitoring module collects the status information of train and is 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 allocates model in advance, Lothrus apterus row are ultimately produced Car running orbit；
Train operation middle or short term Track Pick-up module：The train real time status information provided according to track monitoring module, 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 the situation for occurring violating safety regulation, to its dynamic behaviour implementing monitoring and warning information is provided for control terminal；
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 to carry out robust dual layer resist to train operation track by control terminal module, and pass through 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 was planned；
The method that the flow-optimized control system of the above-mentioned subway transportation for the early warning that conflicts carries out conflict early warning includes following walk Suddenly：
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 certain following moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, using train initiating station stop position as the origin of coordinates, adopted each The sample moment, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,..., yn], processing 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 processing, lead to Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step D3, to the train track data after cluster using HMM carry out parameter training, 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 ', are rolled according to T' nearest position detection value and using B-W algorithms and obtain newest hidden Ma Erke Husband's model parameter λ '；Specifically：By the train track sets data length 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 certain following moment；Every period τ ', the T' according to 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 periodAccording to the HMM parameter lambda of 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, foundation are 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 that discrete observation value is expressed；When system is possible to violate traffic control rule, to subway transportation The Hybrid dynamics behavior implementing monitoring of hybrid system, timely warning information is provided for subway transportation control centre；
The detailed process of the step C is as follows：
Step C1, train status transfer modeling, the process that train is run along track traffic road network are shown as between website 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 are m) train section metastasis model, wherein g represents each sub- section, G tables between website to E= Show the transfer point of train running speed state parameter, Pre and Post represent 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, continuous process is considered as by operation of the train between website, 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 The characteristic Recursive Solution any time train of change in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, is passed through in advance 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, step A detailed process is as follows：
The website letter that step A1, the database from subway transportation control centre are stopped in extracting each train travelling process Breath；
Step A2, the site information stopped according to positive and negative two traffic directions to each train are classified, 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 according to the space layout form of website with straight line.
Further, step B detailed process 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 represent phase link between website 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 u (t) single subsegment, wherein △ t represent sampling interval, Ψ (t) tables Show the state vector of t, A1、B1、C1And D1The state-transition matrix, input matrix, 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 u (t) many subsegments Simulation；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relation, Its controllability of qualitative analysis, according to the sensitivity coefficient matrix [C of 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, step C detailed process is as follows：
Step C1, train status transfer modeling, the process that train is run along track traffic road network are shown as between website 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 are m) train section metastasis model, wherein g represents each sub- section, G tables between website to E= Show the transfer point of train running speed state parameter, Pre and Post represent 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, continuous process is considered as by operation of the train between website, 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 The characteristic Recursive Solution any time train of change in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, is passed through in advance 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, cluster number M' value is 4, and hidden state number N' value is 3, and parameter updates period τ ' For 30 seconds, T' was 10,For 30 seconds, H was 10, and prediction time domain h' is 300 seconds.
Further, step E specific implementation process is as follows：
Step E1, conflict hypersurface collection of functions of the construction based on regulation rule：Set up hypersurface collection of functions is to reflect The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIFor I type hypersurfaces, arranged with two The related continuous function h of carIIFor Type-II hypersurface；
Step E2, foundation build train in traffic network by the observer of train continuous state to discrete conflict situation The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dij(t) represent 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 real-time monitoring 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 monitor for 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) a kind of subway transportation of the invention conflict method for early warning is meeting track friendship On the premise of siphunculus personal distance, based on the real-time position information of train, maintenance data excavates means and dynamically speculates row Wheel paths；According to track traffic regulation rule, the conflict being likely to occur is implemented to alert, the early warning effect to conflict is preferable, can Effectively, accurately and real-time predict the track of train and predict train conflict, effectively improve the security of subway transportation.
(2) the scene monitoring mechanism of the 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.
(3) present invention can be incorporated train in time 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.
(4) controllability and sensitivity analysis result of the invention based on Rail traffic network topological structure, can be handed over for subway Through-flow early warning provides scientific basis, overcomes the randomness of conventional early warning scheme selection.
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.
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 allocates model in advance, Lothrus apterus row are ultimately produced Car running orbit.
Train operation middle or short term Track Pick-up module：The train real time status information provided according to track monitoring module, 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 the situation for occurring violating safety regulation, to its dynamic behaviour implementing monitoring and warning information is provided for control terminal.
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 to carry out robust dual layer resist to train operation track by control terminal module, and pass through 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 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：
The website letter that step A1, the database from subway transportation control centre are stopped in extracting each train travelling process Breath；
Step A2, the site information stopped according to positive and negative two traffic directions to each train are classified, 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 according to the space layout form of website with straight line.
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, the Traffic flux detection model seen in Fig. 1, the single subsegment of structure；Its detailed process is as follows：
Step Bl.1, introducing state variable Ψ, input variable u and output variable Ω, wherein Ψ represent phase link between website 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 u (t) single subsegment, wherein △ t represent sampling interval, Ψ (t) tables Show the state vector of t, A1、B1、C1And D1The state-transition matrix, input matrix, 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 u (t) many subsegments Simulation；
Step B3, the controllable factor matrix [B according to Controlling model1,A1B1,...,A1 n-1B1] order and numerical value n relation, Its controllability of qualitative analysis, according to the sensitivity coefficient matrix [C of 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, according to the plan operational factor of 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 allocates model in advance, generates many train Lothrus apterus running orbits；Its Detailed process is as follows：
Step C1, train status transfer modeling, the process that train is run along track traffic road network are shown as between website 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 are m) train section metastasis model, wherein g represents each sub- section, G tables between website to E= Show the transfer point of train running speed state parameter, Pre and Post represent 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, continuous process is considered as by operation of the train between website, 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 The characteristic Recursive Solution any time train of change in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train each The dwell time data at station, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, is passed through in advance 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 certain following moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, using train initiating station stop position as the origin of coordinates, adopted each The sample moment, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,..., yn], processing 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 processing, lead to Setting cluster number M' is crossed, it is clustered respectively using genetic algorithm for clustering；
Step D3, to the train track data after cluster using HMM carry out parameter training, 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 ', are rolled according to T' nearest position detection value and using B-W algorithms and obtain newest hidden Ma Erke Husband's model parameter λ '；Specifically：By the train track sets data length 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 certain following moment；Every period τ ', the T' according to 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 periodAccording to the HMM parameter lambda of 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；
Above-mentioned cluster number M' value is 4, and hidden state number N' value is 3, and parameter renewal period τ ' is 30 seconds, and T' is 10,For 30 seconds, H was 10, and 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 be discrete observation value express 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, timely warning information is provided for subway transportation control centre；
The specific implementation process of the step E is as follows：
Step E1, conflict hypersurface collection of functions of the construction based on regulation rule：Set up hypersurface collection of functions is to reflect The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIFor I type hypersurfaces, arranged with two The related continuous function h of carIIFor Type-II hypersurface；
Step E2, foundation build train in traffic network by the observer of train continuous state to discrete conflict situation The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dij(t) represent 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 real-time monitoring 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 monitor for 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 the present invention The restriction of embodiment.For those of ordinary skill in the field, it can also be made on the basis of the above description Its various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.And these belong to this hair Among the obvious change or variation that bright spirit is extended out are still in protection scope of the present invention.

Claims (2)

  1. The method for early warning 1. a kind of subway transportation conflicts, 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, using train initiating station stop position as the origin of coordinates, in each sampling Carve, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,...,yn], adopt Processing 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 processing, by setting Surely number M' is clustered, it is clustered respectively using genetic algorithm for clustering；
    Step D3, to the train track data after cluster using HMM carry out parameter training, by will processing after Train operation track data △ x and △ y be considered as the aobvious observations of hidden Markov models, by set hidden state number N' and Parameter updates period τ ', is rolled according to T' nearest position detection value and using B-W algorithms and obtains newest Hidden Markov mould Shape parameter λ '；Specifically：By the train track sets data length obtained is dynamic change, in order to which 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 certain following moment；Every period τ ', the T' sight according to 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 periodAccording to the HMM parameter lambda of 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, foundation are 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, timely warning information is provided for subway transportation control centre；
    The detailed process of the step C is as follows：
    Step C1, train status transfer modeling, the process that train is run along track traffic road network show as the dynamic between website Handoff procedure, the website in train operation plan is set, and sets up the Petri of single train switched and transferred between different websites Pessimistic concurrency control：(g, G, Pre, Post, are m) train section metastasis model to E=, and wherein g represents each sub- section between website, and G represents row The transfer point of car speed of service state parameter, Pre and Post represent front and rear to annexation between each sub- section and website respectively,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, is considered as continuous process, from train by operation of the train between website Stress situation set out, derive kinetics equation of the train in the different operation phase according to energy model, with reference to external interference because Element, sets 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 represents 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 by time subdivision, utilization state consecutive variations Characteristic Recursive Solution any time train in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
    Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train at each station Dwell time data, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
    Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, passes through the period in advance Divide, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being unsatisfactory for peace near conflict point Implement robust secondary planning in the train track of full space requirement.
  2. 2. a kind of flow-optimized control system of subway transportation for the early warning that conflicts, it is characterised in that:Including wire topologies life Into module, data transmission module, car-mounted terminal module, control terminal module and track monitoring module, track monitoring module is received Collect the status information of train and be 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 dynamics mould is initially set up Type, then sets up train running conflict according to train running conflict Coupling point and allocates model in advance, ultimately produces Lothrus apterus train fortune Row track；
    Train operation middle or short term Track Pick-up module：The train real time status information provided according to track monitoring module, utilizes number According to 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 can when having between train When the situation for violating safety regulation can occur, to its dynamic behaviour implementing monitoring and warning information is provided for control terminal；
    Train collision avoidance track optimizing module：When train operation situation monitoring module sends warning information, train physics is being met On the premise of performance, region hold stream constraint and track traffic scheduling rule, by setting optimizing index function, using self-adaptive controlled Theoretical method processed carries out robust dual layer resist to train operation track by control terminal module, and will be advised by data transmission module Check off fruit is transferred to the execution of car-mounted terminal module.Train collision avoidance track optimizing module plans two classes rule comprising internal layer planning and outer layer Streak；
    The method that the flow-optimized control system of the above-mentioned subway transportation for the early warning that conflicts carries out conflict early warning comprises 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, using train initiating station stop position as the origin of coordinates, in each sampling Carve, according to the acquired original discrete two-dimensional position sequence x=[x of train1,x2,...,xn] and y=[y1,y2,...,yn], adopt Processing 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 processing, by setting Surely number M' is clustered, it is clustered respectively using genetic algorithm for clustering；
    Step D3, to the train track data after cluster using HMM carry out parameter training, by will processing after Train operation track data △ x and △ y be considered as the aobvious observations of hidden Markov models, by set hidden state number N' and Parameter updates period τ ', is rolled according to T' nearest position detection value and using B-W algorithms and obtains newest Hidden Markov mould Shape parameter λ '；Specifically：By the train track sets data length obtained is dynamic change, in order to which 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 certain following moment；Every period τ ', the T' sight according to 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 periodAccording to the HMM parameter lambda of 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, foundation are 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, timely warning information is provided for subway transportation control centre；
    The detailed process of the step C is as follows：
    Step C1, train status transfer modeling, the process that train is run along track traffic road network show as the dynamic between website Handoff procedure, the website in train operation plan is set, and sets up the Petri of single train switched and transferred between different websites Pessimistic concurrency control：(g, G, Pre, Post, are m) train section metastasis model to E=, and wherein g represents each sub- section between website, and G represents row The transfer point of car speed of service state parameter, Pre and Post represent front and rear to annexation between each sub- section and website respectively,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, is considered as continuous process, from train by operation of the train between website Stress situation set out, derive kinetics equation of the train in the different operation phase according to energy model, with reference to external interference because Element, sets 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 represents 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 by time subdivision, utilization state consecutive variations Characteristic Recursive Solution any time train in distance of a certain operation phase away from initial rest position point,Wherein J0For voyage of the initial time train away from initial rest position point, △ τ are the number of time window Value, J (τ) is τ moment distance of the train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
    Step C4, train are in station time probability distribution function modeling, for specific run circuit, by transferring train at each station Dwell time data, obtain the dwell time probability distribution of the different website condition Trains of different circuits；
    Step C5, the Lothrus apterus robust track allotment of many trains coupling, the time of conflict point is reached according to each train, passes through the period in advance Divide, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being unsatisfactory for peace near conflict point Implement robust secondary planning in the train track of full space requirement.
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Application publication date: 20170711