CN106741020A - Subway conflict early warning method - Google Patents
Subway conflict early warning method Download PDFInfo
- Publication number
- CN106741020A CN106741020A CN201710078606.0A CN201710078606A CN106741020A CN 106741020 A CN106741020 A CN 106741020A CN 201710078606 A CN201710078606 A CN 201710078606A CN 106741020 A CN106741020 A CN 106741020A
- Authority
- CN
- China
- Prior art keywords
- train
- conflict
- track
- time
- subway
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000005070 sampling Methods 0.000 claims abstract description 9
- 238000013507 mapping Methods 0.000 claims abstract description 4
- 230000035945 sensitivity Effects 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 20
- 238000001514 detection method Methods 0.000 claims description 11
- 230000008859 change Effects 0.000 claims description 10
- 238000012544 monitoring process Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 230000008878 coupling Effects 0.000 claims description 7
- 238000010168 coupling process Methods 0.000 claims description 7
- 238000005859 coupling reaction Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 206010027476 Metastases Diseases 0.000 claims description 3
- 238000005094 computer simulation Methods 0.000 claims description 3
- 238000010924 continuous production Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 230000000977 initiatory effect Effects 0.000 claims description 3
- 238000003064 k means clustering Methods 0.000 claims description 3
- 230000009401 metastasis Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 abstract description 2
- 239000011159 matrix material Substances 0.000 description 12
- 230000004907 flux Effects 0.000 description 8
- 238000010276 construction Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000005267 amalgamation Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000009365 direct transmission Effects 0.000 description 2
- 239000010410 layer Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000004451 qualitative analysis Methods 0.000 description 2
- 238000004445 quantitative analysis Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 239000002355 dual-layer Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
Classifications
-
- 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 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 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
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention relates to a subway conflict early warning method, which comprises the following steps: firstly, generating a topological structure chart of a rail transit network according to planned operation parameters of each train; then, based on the topological structure chart, the controllability and the sensitivity of the train flow are analyzed; generating a conflict-free running track of the multiple trains according to the planned running parameters of each train; predicting the advancing position of the train at a certain future moment at each sampling moment based on the current running state and the historical position observation sequence of the train, establishing an observer from the continuous dynamic state of the train to the discrete conflict logic, and mapping the continuous dynamic state into a conflict state expressed by a discrete observation value; when the system possibly violates the traffic control rule, the hybrid dynamic behavior of the subway traffic hybrid system is monitored, and warning information is provided for the control center. The invention predicts the subway train track in real time by rolling, effectively warns train conflict and improves the safety of subway traffic.
Description
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 ', and the newest hidden Ma Erke of acquisition is rolled according to the individual position detection values of nearest T ' and using 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 right on the basis of initial track HMM parameter lambda '=(π, A, B)
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') track HMM parameter lambda '=(π, A, B) is 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 '=(π, A, B) and nearest H according to newest acquisition
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 is continuous
Distance of the characteristic Recursive Solution any time train of change in a certain operation phase away from initial rest position point,Wherein J0Voyage for initial time train away from initial rest position point, △ τ are the number of time window
Value, J (τ) is distance of the τ moment train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are modeled in station time probability distribution function, for specific run circuit, by transferring train each
The dwell time data at station, obtain the dwell time probability distribution of different circuit difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, according to each train in advance up to the time of conflict point, pass through
Time segments division, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being discontented near conflict point
Implement robust secondary planning in the train track of sufficient personal distance requirement.
Further, in step D, the value of cluster number M' is 4, and the value of hidden state number N' is 3, and parameter updates period τ '
It is 30 seconds, T ' is 10,It it is 30 seconds, H is 10, prediction time domain h ' is 300 seconds.
Further, the specific implementation process of step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Set up hypersurface collection of functions and be used to reflect and be
The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two row
The related continuous function h of carIIIt is Type-II hypersurface；
Step E2, set up by train continuous state to discrete conflict situation observer, build train in traffic network
The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dijT () represents train i and train j between the reality of t
Every dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build
Train monitor in real time mechanism of the people in loop, it is ensured that the operation of system is in safe reachable set, design solution from conflicting to conflicting
The discrete watch-dog of section of slipping out of the hand, when the discrete observation vector of observer shows that safety regulation rally is breached, 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 ', and the newest hidden Ma Erke of acquisition is rolled according to the individual position detection values of nearest T ' and using 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 right on the basis of initial track HMM parameter lambda '=(π, A, B)
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′) track HMM parameter lambda '=(π, A, B) is 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 '=(π, A, B) and nearest H according to newest acquisition
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；
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；
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；The specific mistake of step C
Journey is as follows：
Step C1, train status transfer modeling, train show as the dynamic between website along the process that track traffic road network runs
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 each sub- sections between representing website, G represents row
The transfer point of car speed of service state parameter, Pre and Post represents 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, the operation by train between website is considered as continuous process, from train
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
Distance of the characteristic Recursive Solution any time train 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 at each station
Dwell time data, obtain the dwell time probability distribution of different circuits 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, by the period
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；
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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710078606.0A CN106741020A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710078606.0A CN106741020A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method |
| CN201510150113.4A CN105083322B (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510150113.4A Division CN105083322B (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106741020A true CN106741020A (en) | 2017-05-31 |
Family
ID=54564882
Family Applications (6)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710078599.4A Pending CN106864483A (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method based on robust strategy |
| CN201510150113.4A Active CN105083322B (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method |
| CN201710078598.XA Pending CN106864482A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method |
| CN201710078606.0A Pending CN106741020A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method |
| CN201710078933.6A Pending CN106926873A (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method based on robust strategy |
| CN201710078411.6A Pending CN106741006A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method based on robust strategy |
Family Applications Before (3)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710078599.4A Pending CN106864483A (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method based on robust strategy |
| CN201510150113.4A Active CN105083322B (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method |
| CN201710078598.XA Pending CN106864482A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method |
Family Applications After (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710078933.6A Pending CN106926873A (en) | 2015-03-31 | 2015-03-31 | Subway train collision early warning method based on robust strategy |
| CN201710078411.6A Pending CN106741006A (en) | 2015-03-31 | 2015-03-31 | Subway conflict early warning method based on robust strategy |
Country Status (1)
| Country | Link |
|---|---|
| CN (6) | CN106864483A (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108116456A (en) * | 2017-12-18 | 2018-06-05 | 湖南中车时代通信信号有限公司 | For the storage system of remote backup train operation monitoring device data |
| CN109552364A (en) * | 2018-11-19 | 2019-04-02 | 东莞理工学院 | A kind of rail traffic scheduling early warning system based on big data |
| CN110162912B (en) * | 2019-05-30 | 2020-09-01 | 北京航空航天大学 | Network resource optimization method considering resource competition coupling |
| CN111152818B (en) * | 2020-01-03 | 2021-12-28 | 中国铁道科学研究院集团有限公司通信信号研究所 | Operation diagram stage plan logic checking method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102336204A (en) * | 2011-07-31 | 2012-02-01 | 宁波市镇海西门专利技术开发有限公司 | Anti-collision system for trains |
| CN102700570A (en) * | 2012-05-22 | 2012-10-03 | 西南交通大学 | Anti-collision and early-warning system of railway vehicles |
| CN103310118A (en) * | 2013-07-04 | 2013-09-18 | 文超 | Method for predicting train operation conflicts on high speed railways |
| US8812227B2 (en) * | 2011-05-19 | 2014-08-19 | Metrom Rail, Llc | Collision avoidance system for rail line vehicles |
| CN104462856A (en) * | 2014-12-30 | 2015-03-25 | 江苏理工学院 | Ship conflict early warning method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203211332U (en) * | 2013-04-18 | 2013-09-25 | 南京理工大学 | Anti-collision pre-warning device for subway trains |
| DE102013210081A1 (en) * | 2013-05-29 | 2014-12-04 | Siemens Aktiengesellschaft | Method for detecting a hazard to a rail vehicle |
-
2015
- 2015-03-31 CN CN201710078599.4A patent/CN106864483A/en active Pending
- 2015-03-31 CN CN201510150113.4A patent/CN105083322B/en active Active
- 2015-03-31 CN CN201710078598.XA patent/CN106864482A/en active Pending
- 2015-03-31 CN CN201710078606.0A patent/CN106741020A/en active Pending
- 2015-03-31 CN CN201710078933.6A patent/CN106926873A/en active Pending
- 2015-03-31 CN CN201710078411.6A patent/CN106741006A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8812227B2 (en) * | 2011-05-19 | 2014-08-19 | Metrom Rail, Llc | Collision avoidance system for rail line vehicles |
| CN102336204A (en) * | 2011-07-31 | 2012-02-01 | 宁波市镇海西门专利技术开发有限公司 | Anti-collision system for trains |
| CN102700570A (en) * | 2012-05-22 | 2012-10-03 | 西南交通大学 | Anti-collision and early-warning system of railway vehicles |
| CN103310118A (en) * | 2013-07-04 | 2013-09-18 | 文超 | Method for predicting train operation conflicts on high speed railways |
| CN104462856A (en) * | 2014-12-30 | 2015-03-25 | 江苏理工学院 | Ship conflict early warning method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106864483A (en) | 2017-06-20 |
| CN106864482A (en) | 2017-06-20 |
| CN105083322A (en) | 2015-11-25 |
| CN106926873A (en) | 2017-07-07 |
| CN106741006A (en) | 2017-05-31 |
| CN105083322B (en) | 2017-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105083333B (en) | Subway traffic flow optimization control method | |
| CN105083334B (en) | Subway train collision early warning method | |
| CN105095984B (en) | Real-time prediction method for subway train track | |
| CN105083335B (en) | Subway traffic flow optimization control method | |
| CN105083322B (en) | Subway train collision early warning method | |
| Peng et al. | A dynamic rescheduling and speed management approach for high-speed trains with uncertain time-delay | |
| Qiao et al. | Modelling and simulation of urban traffic systems: present and future | |
| CN105095983B (en) | Real-time prediction method for subway train track | |
| Yin et al. | Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm | |
| Xiong et al. | Parallel bus rapid transit (BRT) operation management system based on ACP approach | |
| Khelifi et al. | Lagrangian discretization of generic second order models: Application to traffic control | |
| Xin et al. | Modeling bus bunching and anti-bunching control accounting for signal control and passenger swapping behavior | |
| Oskarbski et al. | Multi-level transport systems model for traffic management activities | |
| CN105093929B (en) | Planning method for conflict resolution of subway train | |
| Sheu | Microscopic modeling and control logic for incident-responsive automatic vehicle movements in single-automated-lane highway systems | |
| Kunicina et al. | Traffic Route Modelling and Assignment with Intelligent Transport System | |
| Zhang et al. | Modeling of Distribution Service Network System in Comprehensive Passenger Transportation Hub |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170531 |