CN106853833A - Subway traffic flow control method - Google Patents
Subway traffic flow control method Download PDFInfo
- Publication number
- CN106853833A CN106853833A CN201710078414.XA CN201710078414A CN106853833A CN 106853833 A CN106853833 A CN 106853833A CN 201710078414 A CN201710078414 A CN 201710078414A CN 106853833 A CN106853833 A CN 106853833A
- Authority
- CN
- China
- Prior art keywords
- train
- track
- traffic
- conflict
- flow
- 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 49
- 238000013439 planning Methods 0.000 claims abstract description 42
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 11
- 230000035945 sensitivity Effects 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 26
- 230000008859 change Effects 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 9
- 238000010586 diagram Methods 0.000 claims description 7
- 238000005096 rolling process Methods 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 6
- 238000010168 coupling process Methods 0.000 claims description 6
- 238000005859 coupling reaction Methods 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000009977 dual effect 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
- 238000012549 training Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000000704 physical effect Effects 0.000 claims description 2
- 238000013507 mapping Methods 0.000 abstract description 3
- 239000010410 layer Substances 0.000 description 17
- 239000011159 matrix material Substances 0.000 description 14
- 230000004907 flux Effects 0.000 description 8
- 238000010276 construction Methods 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 238000012546 transfer Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 206010027476 Metastases Diseases 0.000 description 2
- 238000005267 amalgamation Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005094 computer simulation Methods 0.000 description 2
- 238000010924 continuous production Methods 0.000 description 2
- 230000009365 direct transmission Effects 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 239000002355 dual-layer Substances 0.000 description 2
- 238000009472 formulation Methods 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000009401 metastasis Effects 0.000 description 2
- 239000000203 mixture Substances 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
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005206 flow analysis Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 238000000926 separation method 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
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/16—Trackside optimisation of vehicle or train operation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/36—Circuit design at the analogue level
- G06F30/367—Design verification, e.g. using simulation, simulation program with integrated circuit emphasis [SPICE], direct methods or relaxation methods
Landscapes
- Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mechanical Engineering (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a subway traffic flow control 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, monitoring the hybrid dynamic behavior of the subway traffic hybrid system and providing alarm information for the control center; and finally, when the alarm information appears, performing robust double-layer planning on the train running track by adopting a self-adaptive control theory method, and transmitting a planning result to each train.
Description
The application is Application No.：201510150696.0, invention and created name is《A kind of flow-optimized control of subway transportation
Method》, the applying date is：The divisional application of the application for a patent for invention on March 31 in 2015.
Technical field
The present invention relates to a kind of flow-optimized control method of subway transportation, more particularly to a kind of double-deck ground based on Robust Strategies
Iron traffic optimization control method.
Background technology
With expanding day by day for China's big and medium-sized cities scale, Traffic Systems are faced with the increasing pressure, energetically
Feasibility of developing track transportation system turns into the important means for solving urban traffic congestion.National Eleventh Five-Year Plan guiding principle is it is to be noted, that there is bar
The big city and group of cities area of part are using track traffic as Priority setting.China's just one unprecedented rail of experience
Road transport development peak period, some cities have been turned to the construction of net by the construction of line, urban mass transit network progressively shape
Into.In the complex region that Rail traffic network and train flow are intensive, still combined using train operation plan and be based on subjective experience
Train interval dispensing mode gradually show its lag, be in particular in：(1) formulation of train operation plan timetable is simultaneously
Not in view of the influence of various enchancement factors, easily cause traffic flow tactics and manage crowded, reduce the safety of traffic system operation
Property；(2) train scheduling work lays particular emphasis on the personal distance kept between single train, and not yet rise to carries out strategic pipe to train flow
The macroscopic aspect of reason；(3) subjective experience of a line dispatcher is depended on train allocation process, the selection for allocating opportunity is random more
Property it is larger, lack scientific theory support；(4) the less shadow in view of external interference factor of allotment means that dispatcher is used
Ring, the robustness and availability of train programs are poor.
It is directed to long-distance railway transportation more the discussion object of existing documents and materials, and is directed to big flow, high density and closely-spaced
The Scientific Regulation scheme of the city underground traffic system under service condition still lacks system design.Under complicated road network service condition
Train Coordinated Control Scheme need to carry out the running status of single vehicles in transportation network in region on strategic level to calculate and
Optimization, and the traffic flow implementation collaborative planning to being made up of multiple trains；Pass through effective monitoring mechanism on pre- tactical level
Adjust the subregional critical operational parameters in transportation network top to solve congestion problems, and ensure the fortune of all trains in the region
Line efficiency；The running status of related train is then adjusted according to critical operational parameters on tactical level, single-row wheel paths are obtained
Prioritization scheme, consideration train performance, scheduling rule and extraneous ring are changed into by the headway management of train from fixed manual type
The factors such as border are in interior variable " microcosmic-macroscopic view-middle sight-microcosmic " Separation control mode.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of robustness and the preferable subway transportation flow control side of availability
Method, the method can strengthen the subject of programs formulation and can effectively prevent subway train from running conflict.
Realize that the technical scheme of the object of the invention is to provide a kind of subway transportation method of flow control, comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze train flow controllability and
The class feature of sensitiveness two；
Step C, the plan operational factor according to each train, on the basis of Modeling Method for Train Dynamics is built, according to row
Car operation conflict Coupling point sets up train running conflict and allocates model in advance, generates many train Lothrus apterus running orbits；
Step D, in each sampling instant t, based on the current running status of train and historical position observation sequence, to train
The advanced positions at following certain moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, with train initiating station stop position as the origin of coordinates, adopted each
Sample moment, the original discrete two-dimensional position sequence x=[x of train acquired in1,x2,...,xn] and y=[y1,y2,...,
yn], treatment is carried out to it using first-order difference method and obtains new train discrete location sequence Δ x=[Δ x1,Δx2,...,Δ
xn-1] and Δ y=[Δ y1,Δy2,...,Δyn-1], wherein Δ xi=xi+1-xi,Δyi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, to train track data cluster, to train discrete two-dimensional position sequence Δ x and Δ 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 Δ x and Δ y after reason are considered as the aobvious observation of hidden Markov models, by setting hidden state number
N' and parameter update period τ ', roll and obtain newest hidden Ma Erke according to T' nearest position detection value and use B-W algorithms
Husband's model parameter λ '；Specifically：By the train track sets data length for being obtained is dynamic change, in order in real time with
The state change of track train track, it is necessary to initial track HMM parameter lambda '=(π, A, B) on the basis of it is right
It is readjusted, more accurately to speculate train in the position at following certain moment；Every period τ ', according to the T' of newest acquisition
Individual observation (o1,o2,...,oT') to track HMM parameter lambda '=(π, A, B) reevaluated；
Step D4, foundation HMM parameter, are obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step D5, every the periodHMM parameter lambda according to newest acquisition '=(π, A, B) and nearest H
Individual history observation (o1,o2,...,oH), the hidden state q based on train current time, in moment t, by setting prediction time domain
H', obtains the position prediction value O of future time period train；
Step E, set up from the continuous dynamic of train to the observer of discrete conflict logic, by the continuous of subway transportation system
Dynamic mapping is the conflict situation of discrete observation value expression；When system is possible to violate traffic control rule, to subway transportation
The Hybrid dynamics behavior implementing monitoring of hybrid system, for control centre provides timely warning information；
Step F, when warning information occurs, meeting train physical property, region hold stream constraint and track traffic scheduling
On the premise of rule, by setting optimizing index function, Shandong is carried out to train operation track using Adaptive Control Theory method
Rod dual layer resist, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each train is equal
Reach it and free terminal.
Further, the detailed process of step A is as follows：
Step A1, the database from subway transportation control centre extract the website letter stopped in each train travelling process
Breath；
Step A2, the site information that each train is stopped is classified according to positive and negative two traffic directions, and will be same
Same site on one traffic direction is merged；
Step A3, according to website amalgamation result, multiple websites before and after being connected with straight line according to the space layout form of website.
Further, the detailed process of step B is as follows：
Step Bl, the Traffic flux detection model built in single subsegment；Its detailed process is as follows：
Step Bl.1, introduce state variable Ψ, input variable u and output variable Ω, wherein Ψ represent website between phase link
The train quantity that certain moment is present in section, it includes single channel section and Multiple Sections two types, and u represents that track traffic dispatcher is directed to
The Operation Measures that certain section is implemented, such as adjust train speed or change train in the station time, Ω represents certain section period
On the train quantity left；
Step B1.2, by by time discretization, setting up shape such as Ψ (t+ Δs 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 Δ t represents 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+ Δs 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, Δ τ is the number of time window
Value, J (τ) is distance of the τ moment train away from initial rest position point, thereby it is assumed that and obtains single-row wheel paths；
Step C4, train are modeled in station time probability distribution function, for specific run circuit, by transferring train each
The dwell time data at station, obtain the dwell time probability distribution of different circuit difference website condition Trains；
Step C5, the Lothrus apterus robust track allotment of many trains coupling, according to each train in advance up to the time of conflict point, pass through
Time segments division, in each sampling instant t, on the premise of random factor is incorporated, according to scheduling rule to being discontented near conflict point
Implement robust secondary planning in the train track of sufficient personal distance requirement.
Further, in step D, the value of cluster number M' is 4, and the value of hidden state number N' is 3, and parameter updates period τ '
It is 30 seconds, T' is 10,It it is 30 seconds, H is 10, prediction time domain h' is 300 seconds.
Further, the specific implementation process of step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Set up hypersurface collection of functions and be used to reflect and be
The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two row
The related continuous function h of carIIIt is Type-II hypersurface；
Step E2, set up by train continuous state to discrete conflict situation observer, build train in traffic network
The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dijT () represents train i and train j between the reality of t
Every dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build
Train monitor in real time mechanism of the people in loop, it is ensured that the operation of system is in safe reachable set, design solution from conflicting to conflicting
The discrete watch-dog of section of slipping out of the hand, when the discrete observation vector of observer shows that safety regulation rally is breached, sends phase at once
The warning information answered.
Further, the detailed process of step F is as follows：
Step F1, the analysis result based on step B and step E, it is determined that the traffic flow regulation measure specifically taken, including
The speed of service and/or adjustment train of train are adjusted in the station class measure of time two, and using above regulation measure specifically
Point and opportunity；
Step F2, termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions of the collision avoidance planning of setting train
Time domain Υ；
Step F3, operation conflict Resolution process model building, the operation conflict Resolution in the above-listed workshop of Rail traffic network is considered as
Inside and outside dual planning problem based on both macro and micro aspect, whereinRepresent outer layer plan model, i.e. rail
Train flow flow-Density and distribution problem on road traffic network,Represent internal layer plan model, i.e. track traffic
The state adjustment problem of single vehicles on section；F、x1And u1It is respectively object function, state vector and the decision-making of outer layer planning problem
Vector, G (x1,u1)≤0 is the constraints of outer layer planning, f, x2And u2It is respectively object function, the state of internal layer planning problem
Vector sum decision vector, g (x2,u2)≤0 is the constraints of internal layer planning, using the outer layer program results of macroscopic aspect as micro-
The reference input of sight aspect internal layer planning；
Step F4, the variable bound modeling of operation conflict Resolution, build and include adjustable train quantity a, train speed ω and row
Car is in variables such as station times γ in interior both macro and micro constraints：Wherein t need to implement the change of the section k of conflict Resolution
Amount constraint can be described as：ak(t)≤aM、ωk(t)≤ωM、γk(t)≤γM, aM、ωM、γMRespectively maximum adjustable train number
, in the station time, such variable of freeing can be subject to traffic flow distribution, train thing for amount, maximum train running speed and train most long
The constraint of the aspect such as rationality energy and personal distance；
The optimal road network flow allocation plan of step F5, Multi-objective Robust is solved：Based on cooperative collision avoidance trajectory planning thought,
For different performance indications, by selecting different conflict Resolution object functions, base is solved in traffic flow operation macroscopic aspect
In Euler's network model multiple target traffic flow optimum flow allocation plan and each control section is only real in Rolling Planning interval
Apply its first Optimal Control Strategy；
The optimal section train operation state adjustment of step F6, Multi-objective Robust：According to each section or zone flow configuration knot
Really, evolutionary model is mixed based on train operation and Lagrangian plan model obtains optimal single vehicles controlled quentity controlled variable, generated optimal
Single vehicles running orbit and each regulation and control train only implements its first Optimal Control Strategy in Rolling Planning interval；
Step F7, each train are received and perform train collision avoidance instruction；
Step F8, in next sampling instant, repeat step F5 to F7 is until each train reaches it and frees terminal.
Further, in step F2, it is next website that stops of train to terminate reference point locations P, and the value of parameter Θ is
300 seconds, the value of Υ was 300 seconds.
Further, the detailed process of step F5 is as follows：Order
WhereinRepresent distance between t train i present positions and next website square, Pi(t)=(xit,
yit) two-dimensional coordinate value of t train i is represented,The next two-dimensional coordinate values for stopping website of train i are represented,
The priority index of so t train i may be set to：
Wherein ntRepresent there is the train number of conflict on t section, from the implication of priority index, train away from
From next website more close to, its priority is higher；
Setting optimizing index
Wherein i ∈ I (t) represent train code and I (t)=1,2 ..., nt, Pi(t+s Δs t) represents train in moment (t
The position vector of+s Δs t), Π represents control time, i.e., the time span of Future Trajectory planning, u from current timeiExpression is treated
The optimal control sequence of the train i of optimization, QitIt is positive definite diagonal matrix, its diagonal element refers to for train i in the priority of t
Number λit, and
The present invention has positive effect：(1) subway transportation method of flow control of the invention is meeting track traffic control
On the premise of personal distance, based on the real-time position information of train, maintenance data excavates means and dynamically speculates train track；
According to track traffic regulation rule, alarm is implemented in the conflict to being likely to occur, according to train performance data and relevant constraint
Give each train planning conflict Resolution track；When being configured to train schedule, it is contemplated that influence all kinds of of train
The probability distribution of random factor and the robustness of train schedule, strengthen the availability of configuration result.
(2) controllability and sensitivity analysis result of the present invention based on Rail traffic network topological structure, can hand over for subway
Through-flow allotment time, the selection in allotment place and allotment means provide scientific basis, it is to avoid the randomness that regulation and control scheme is chosen.
(3) scene monitoring mechanism of the present invention based on constructed " people is in loop ", can be to train inside continuous variable
Frequent interaction with external discrete event makes effecting reaction in time, overcomes the shortcoming of conventional open loop monitored off-line scheme.
(4) the dual layer resist scheme of train flow of the invention can not only reduce the solution dimension of Optimal Control Problem, also
The practicality of regulation and control scheme can be strengthened, overcome model and algorithm in existing document only focus on train AT STATION to hair when
Between, and lack the defect of control when being run on specific railroad section to train and prediction.
(5) present invention can in time be incorporated train and be transported in real time based on constructed train operation track rolling forecast scheme
All kinds of disturbing factors in row, improve the accuracy of train trajectory predictions, overcome Conventional Off-line prediction scheme accuracy not high
Shortcoming.
Brief description of the drawings
Fig. 1 is train flow analysis on Operating figure；
Fig. 2 is that Lothrus apterus 3D robusts track speculates figure；
Fig. 3 mixes monitoring figure for train operation state；
Fig. 4 frees figure for train running conflict is optimal；
Fig. 5 is the schematic diagram of traffic flow bilayer allocation plan.
Specific embodiment
(embodiment 1)
A kind of flow-optimized control system of subway transportation, including it is wire topologies generation module, data transmission module, vehicle-mounted
Terminal module, control terminal module and track monitoring module, track monitoring module are collected the status information of train and are supplied to
Control terminal module.
The control terminal module includes following submodule：
Lothrus apterus Track Pick-up module before train operation：According to Train operation plan time of running table, train dynamicses are initially set up
Model is learned, then train running conflict is set up according to train running conflict Coupling point and is allocated model in advance, ultimately produce Lothrus apterus row
Car running orbit.
Train operation middle or short term Track Pick-up module：According to the train real time status information that track monitoring module is provided, profit
With data mining model, thus it is speculated that the running orbit of train in future time period.
Train operation situation monitoring module：In each sampling instant t, the track estimation result based on train, when between train
When being possible to occur violating the situation of safety regulation, to its dynamic behaviour implementing monitoring and for control terminal provides warning information.
Train collision avoidance track optimizing module：When train operation situation monitoring module sends warning information, train is being met
On the premise of physical property, region hold stream constraint and track traffic scheduling rule, by setting optimizing index function, using adaptive
Answer control theory method carries out robust dual layer resist by control terminal module to train operation track, and by data transmission module
Program results is transferred into car-mounted terminal module to perform.Train collision avoidance track optimizing module includes internal layer planning and outer layer planning two
Class planning process.
Using the subway transportation method of flow control of the flow-optimized control system of above-mentioned subway transportation, comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；Its is specific
Process is as follows：
Step A1, the database from subway transportation control centre extract the website letter stopped in each train travelling process
Breath；
Step A2, the site information that each train is stopped is classified according to positive and negative two traffic directions, and will be same
Same site on one traffic direction is merged；
Step A3, according to website amalgamation result, multiple websites before and after being connected with straight line according to the space layout form of website.
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze train flow controllability and
The class feature of sensitiveness two；Its detailed process is as follows：
Step Bl, see Fig. 1, build the Traffic flux detection model in single subsegment；Its detailed process is as follows：
Step Bl.1, introduce state variable Ψ, input variable u and output variable Ω, wherein Ψ represent website between phase link
The train quantity that certain moment is present in section, it includes single channel section and Multiple Sections two types, and u represents that track traffic dispatcher is directed to
The Operation Measures that certain section is implemented, such as adjust train speed or change train in the station time, Ω represents certain section period
On the train quantity left；
Step B1.2, by by time discretization, setting up shape such as Ψ (t+ Δs 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 Δ t represents 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+ Δs 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, Δ τ is 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 Δ x=[Δ x1,Δx2,...,Δ
xn-1] and Δ y=[Δ y1,Δy2,...,Δyn-1], wherein Δ xi=xi+1-xi,Δyi=yi+1-yi(i=1,2 ..., n-1)；
Step D2, to train track data cluster, to train discrete two-dimensional position sequence Δ x and Δ 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 Δ x and Δ y after reason are considered as the aobvious observation of hidden Markov models, by setting hidden state number
N' and parameter update period τ ', roll and obtain newest hidden Ma Erke according to T' nearest position detection value and use B-W algorithms
Husband's model parameter λ '；Specifically：By the train track sets data length for being obtained is dynamic change, in order in real time with
The state change of track train track, it is necessary to initial track HMM parameter lambda '=(π, A, B) on the basis of it is right
It is readjusted, more accurately to speculate train in the position at following certain moment；Every period τ ', according to the T' of newest acquisition
Individual observation (o1,o2,...,oT') to track HMM parameter lambda '=(π, A, B) reevaluated；
Step D4, foundation HMM parameter, are obtained corresponding to current time observation using Viterbi algorithm
Hidden state q；
Step D5, every the periodHMM parameter lambda according to newest acquisition '=(π, A, B) and nearest H
Individual history observation (o1,o2,...,oH), the hidden state q based on train current time, in moment t, by setting prediction time domain
H', obtains the position prediction value O of future time period train；
The value of above-mentioned cluster number M' is 4, and the value of hidden state number N' is 3, and parameter renewal period τ ' is 30 seconds, and T' is
10,It it is 30 seconds, H is 10, prediction time domain h' is 300 seconds.
Step E, see Fig. 3, set up from the continuous dynamic of train to the observer of discrete conflict logic, by subway transportation system
Continuous dynamic mapping for discrete observation value expression conflict situation；When system is possible to violate traffic control rule, over the ground
The Hybrid dynamics behavior implementing monitoring of iron traffic hybrid system, for control centre provides timely warning information；
The specific implementation process of the step E is as follows：
The conflict hypersurface collection of functions of step E1, construction based on regulation rule：Set up hypersurface collection of functions and be used to reflect and be
The contention situation of system, wherein, the continuous function h related to single train in conflict hypersurfaceIIt is I type hypersurfaces, with two row
The related continuous function h of carIIIt is Type-II hypersurface；
Step E2, set up by train continuous state to discrete conflict situation observer, build train in traffic network
The safety regulation collection d that need to be met during operationij(t)≥dmin, wherein dijT () represents train i and train j between the reality of t
Every dminRepresent the minimum safety interval between train；
Step E3, based on person machine system is theoretical and complication system hierarchical control principle, according to train operation pattern, build
Train monitor in real time mechanism of the people in loop, it is ensured that the operation of system is in safe reachable set, design solution from conflicting to conflicting
The discrete watch-dog of section of slipping out of the hand, when the discrete observation vector of observer shows that safety regulation rally is breached, sends phase at once
The warning information answered.
Step F, see Fig. 4, when warning information occurs, train physical property, region hold stream constraint and track is handed over meeting
On the premise of logical scheduling rule, by setting optimizing index function, using Adaptive Control Theory method to train operation track
Robust dual layer resist is carried out, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each
Train reaches it and frees terminal；Its detailed process is as follows：
Step F1, the analysis result based on step B3 and step E3, it is determined that the traffic flow regulation measure specifically taken, bag
The speed of service and/or adjustment train of adjustment train are included in the class measure of station time two, and using the specific of above regulation measure
Place and opportunity；
Step F2, termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions of the collision avoidance planning of setting train
Time domain Υ；
It is next website that stops of train to terminate reference point locations P, and the value of parameter Θ is 300 seconds, and the value of Υ is 300
Second；
Step F3, operation conflict Resolution process model building, the operation conflict Resolution in the above-listed workshop of Rail traffic network is considered as
Inside and outside dual planning problem based on both macro and micro aspect, is shown in Fig. 5, whereinRepresent outer layer planning mould
Train flow flow-Density and distribution problem in type, i.e. track traffic road network,Internal layer plan model is represented, i.e.,
The state adjustment problem of single vehicles on track traffic section；F、x1And u1Be respectively the object function of outer layer planning problem, state to
Amount and decision vector, G (x1,u1)≤0 is the constraints of outer layer planning, f, x2And u2It is respectively the target of internal layer planning problem
Function, state vector and decision vector, g (x2,u2)≤0 is the constraints of internal layer planning, and the outer layer of macroscopic aspect is planned into knot
The reference input that fruit is planned as microcosmic point internal layer；
Step F4, the variable bound modeling of operation conflict Resolution, build and include adjustable train quantity a, train speed ω and row
Car is in variables such as station times γ in interior both macro and micro constraints：Wherein t need to implement the change of the section k of conflict Resolution
Amount constraint can be described as：ak(t)≤aM、ωk(t)≤ωM、γk(t)≤γM, aM、ωM、γMRespectively maximum adjustable train number
, in the station time, such variable of freeing can be subject to traffic flow distribution, train thing for amount, maximum train running speed and train most long
The constraint of the aspect such as rationality energy and personal distance；
The optimal road network flow allocation plan of step F5, Multi-objective Robust is solved：Based on cooperative collision avoidance trajectory planning thought,
For different performance indications, by selecting different conflict Resolution object functions, base is solved in traffic flow operation macroscopic aspect
In Euler's network model multiple target traffic flow optimum flow allocation plan and each control section is only real in Rolling Planning interval
Apply its first Optimal Control Strategy；Its detailed process is as follows：Order
WhereinRepresent distance between t train i present positions and next website square, Pi(t)=(xit,
yit) two-dimensional coordinate value of t train i is represented,The next two-dimensional coordinate values for stopping website of train i are represented,
The priority index of that so t train i may be set to：
Wherein ntRepresent there is the train number of conflict on t section, from the implication of priority index, train away from
From next website more close to, its priority is higher；
Setting optimizing index
Wherein i ∈ I (t) represent train code and I (t)=1,2 ..., nt, Pi(t+s Δs t) represents train in moment (t
The position vector of+s Δs t), Π represents control time, i.e., the time span of Future Trajectory planning, u from current timeiExpression is treated
The optimal control sequence of the train i of optimization, QitIt is positive definite diagonal matrix, its diagonal element refers to for train i in the priority of t
Number λit, and
The optimal section train operation state adjustment of step F6, Multi-objective Robust：According to each section or zone flow configuration knot
Really, evolutionary model is mixed based on train operation and Lagrangian plan model obtains optimal single vehicles controlled quentity controlled variable, generated optimal
Single vehicles running orbit and each regulation and control train only implements its first Optimal Control Strategy in Rolling Planning interval；
Step F7, each train are received and perform train collision avoidance instruction；
Step F8, in next sampling instant, repeat step F5 to F7 is until each train reaches it and frees terminal.
Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to of the invention
The restriction of implementation method.For those of ordinary skill in the field, it can also be made on the basis of the above description
The change or variation of its multi-form.There is no need and unable to be exhaustive to all of implementation method.And these belong to this hair
Obvious change that bright spirit is extended out or among changing still in protection scope of the present invention.
Claims (1)
1. a kind of subway transportation method of flow control, it is characterised in that comprise the following steps：
Step A, the plan operational factor according to each train, generate the topology diagram of Rail traffic network；
Step B, the topology diagram based on the Rail traffic network constructed by step A, analyze the controllability and sensitivity of train flow
Two class features of property；
Step C, the plan operational factor according to each train, on the basis of Modeling Method for Train Dynamics is built, according to train fortune
Row conflict Coupling point sets up train running conflict and allocates model in advance, generates many train Lothrus apterus running orbits；
Step D, in each sampling instant t, based on the current running status of train and historical position observation sequence, to train future
The advanced positions at certain moment are predicted；Its detailed process is as follows：
Step D1, train track data pretreatment, with train initiating station stop position as the origin of coordinates, in each sampling
Carve, the original discrete two-dimensional position sequence x=[x of train acquired in1,x2,...,xn] and y=[y1,y2,...,yn], 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 control centre provides timely warning information；
Step F, when warning information occurs, meeting train physical property, region hold stream constraint and track traffic scheduling rule
On the premise of, by setting optimizing index function, robust is carried out to train operation track using Adaptive Control Theory method double
Layer planning, and program results is transferred to each train, each train is received and performs train collision avoidance instruction until each train is reached
It frees terminal；The detailed process of step F is as follows：
Step F1, the analysis result based on step B and step E, it is determined that the traffic flow regulation measure specifically taken, including adjustment
The speed of service of train and/or adjustment train in the station class measure of time two, and specified place using above regulation measure and
Opportunity；
Step F2, termination reference point locations P, collision avoidance policy control time domain Θ, the trajectory predictions time domain of the collision avoidance planning of setting train
Υ；
Step F3, operation conflict Resolution process model building, the operation conflict Resolution in the above-listed workshop of Rail traffic network are considered as and are based on
The inside and outside dual planning problem of both macro and micro aspect, whereinRepresent that outer layer plan model, i.e. track are handed over
The online train flow flow-Density and distribution problem of path,Represent internal layer plan model, i.e. track traffic section
The state adjustment problem of upper single vehicles；F、x1And u1Be respectively outer layer planning problem object function, state vector and decision-making to
Amount, G (x1,u1)≤0 is the constraints of outer layer planning, f, x2And u2Be respectively the object function of internal layer planning problem, state to
Amount and decision vector, g (x2,u2)≤0 is the constraints of internal layer planning, using the outer layer program results of macroscopic aspect as microcosmic
The reference input of aspect internal layer planning；
Step F4, the variable bound modeling of operation conflict Resolution, structure exist comprising adjustable train quantity a, train speed ω and train
The variables such as time γ stand in interior both macro and micro constraints：Wherein t need to implement the variable of the section k of conflict Resolution about
Beam can be described as：ak(t)≤aM、ωk(t)≤ωM、γk(t)≤γM, aM、ωM、γMRespectively maximum adjustable train quantity, most
, in the station time, such variable of freeing can be subject to traffic flow distribution, train physical for big train running speed and train most long
The constraint of the aspect such as energy and personal distance；
The optimal road network flow allocation plan of step F5, Multi-objective Robust is solved：Based on cooperative collision avoidance trajectory planning thought, for
Different performance indications, by selecting different conflict Resolution object functions, solve in traffic flow operation macroscopic aspect and are based on Europe
It is only implemented in the multiple target traffic flow optimum flow allocation plan of pull-up network model and respectively control section in Rolling Planning interval
First Optimal Control Strategy；
The optimal section train operation state adjustment of step F6, Multi-objective Robust：According to each section or zone flow configuration result, base
Mix evolutionary model in train operation and Lagrangian plan model obtains optimal single vehicles controlled quentity controlled variable, generate optimal single-row
Car running orbit and respectively regulation and control train only implement its first Optimal Control Strategy in Rolling Planning interval；
Step F7, each train are received and perform train collision avoidance instruction；
Step F8, in next sampling instant, repeat step F5 to F7 is until each train reaches it and frees terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710078414.XA CN106853833A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow control method |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510150696.0A CN105083333B (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method |
| CN201710078414.XA CN106853833A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow control method |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510150696.0A Division CN105083333B (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106853833A true CN106853833A (en) | 2017-06-16 |
Family
ID=54564891
Family Applications (6)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710078414.XA Pending CN106853833A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow control method |
| CN201710078608.XA Pending CN106777833A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method based on robust strategy |
| CN201710078938.9A Pending CN106672028A (en) | 2015-03-31 | 2015-03-31 | Double-layer subway traffic flow optimization control method based on robust strategy |
| CN201510150696.0A Active CN105083333B (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method |
| CN201710078413.5A Pending CN106828546A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow control method |
| CN201710078412.0A Pending CN106828545A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method based on robust strategy |
Family Applications After (5)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710078608.XA Pending CN106777833A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method based on robust strategy |
| CN201710078938.9A Pending CN106672028A (en) | 2015-03-31 | 2015-03-31 | Double-layer subway traffic flow optimization control method based on robust strategy |
| CN201510150696.0A Active CN105083333B (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method |
| CN201710078413.5A Pending CN106828546A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow control method |
| CN201710078412.0A Pending CN106828545A (en) | 2015-03-31 | 2015-03-31 | Subway traffic flow optimization control method based on robust strategy |
Country Status (1)
| Country | Link |
|---|---|
| CN (6) | CN106853833A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117381805A (en) * | 2023-12-13 | 2024-01-12 | 成都航空职业技术学院 | Mechanical arm operation control method and system for conflict handling |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107403273B (en) * | 2017-07-28 | 2020-11-27 | 中铁程科技有限责任公司 | Information processing method, device and non-transitory computer readable storage medium |
| CN108647802B (en) * | 2018-03-26 | 2021-06-25 | 复旦大学 | Anti-congestion method based on double-layer traffic network model |
| CN108898321B (en) * | 2018-07-09 | 2021-08-24 | 西北工业大学 | Semantic template-based method for acquiring standard conflict parameters of manufacturing technical problem |
| CN108873737B (en) * | 2018-07-18 | 2020-12-25 | 东北大学 | Automatic sorting control and decision-making system based on M-HSTPN model |
| CN109703606B (en) * | 2019-01-16 | 2020-12-15 | 北京交通大学 | High-speed train intelligent driving control method based on historical operation data |
| CN111191383B (en) * | 2020-01-17 | 2023-10-31 | 中车青岛四方机车车辆股份有限公司 | Method and device for generating simulation track, storage medium and electronic equipment |
| CN114815852B (en) * | 2022-06-14 | 2023-02-03 | 北京航空航天大学 | CACC fleet track planning method based on space discretization |
| CN114937364B (en) * | 2022-06-17 | 2023-09-15 | 北京交通大学 | Construction method of urban rail transit hierarchical network based on topology transformation |
| CN117681932A (en) * | 2024-01-02 | 2024-03-12 | 北京交通大学 | Virtual-connection-based heavy-duty train control method, system and storage medium |
| CN118082930B (en) * | 2024-03-21 | 2024-08-20 | 合肥工业大学 | Train operation adjustment method under continuous random interval multi-interference based on scene |
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 (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9733625B2 (en) * | 2006-03-20 | 2017-08-15 | General Electric Company | Trip optimization system and method for a train |
| US8370006B2 (en) * | 2006-03-20 | 2013-02-05 | General Electric Company | Method and apparatus for optimizing a train trip using signal information |
| DE102011081995A1 (en) * | 2011-09-01 | 2012-10-25 | Siemens Ag | Drive optimization module for vehicle, particularly rail vehicle, has communication device for direct communication with one or multiple other vehicles for transmission of ride data or for receiving data of vehicles |
| CN103043084A (en) * | 2012-12-31 | 2013-04-17 | 北京交通大学 | Method and system for optimizing urban railway transit transfer |
-
2015
- 2015-03-31 CN CN201710078414.XA patent/CN106853833A/en active Pending
- 2015-03-31 CN CN201710078608.XA patent/CN106777833A/en active Pending
- 2015-03-31 CN CN201710078938.9A patent/CN106672028A/en active Pending
- 2015-03-31 CN CN201510150696.0A patent/CN105083333B/en active Active
- 2015-03-31 CN CN201710078413.5A patent/CN106828546A/en active Pending
- 2015-03-31 CN CN201710078412.0A patent/CN106828545A/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 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117381805A (en) * | 2023-12-13 | 2024-01-12 | 成都航空职业技术学院 | Mechanical arm operation control method and system for conflict handling |
| CN117381805B (en) * | 2023-12-13 | 2024-02-27 | 成都航空职业技术学院 | Mechanical arm operation control method and system for conflict handling |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105083333B (en) | 2017-03-15 |
| CN106828545A (en) | 2017-06-13 |
| CN106828546A (en) | 2017-06-13 |
| CN105083333A (en) | 2015-11-25 |
| CN106672028A (en) | 2017-05-17 |
| CN106777833A (en) | 2017-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105083333B (en) | Subway traffic flow optimization control method | |
| Jiang et al. | Reinforcement learning approach for coordinated passenger inflow control of urban rail transit in peak hours | |
| CN105083334B (en) | Subway train collision early warning method | |
| CN105083335B (en) | Subway traffic flow optimization control method | |
| CN105095984B (en) | Real-time prediction method for subway train track | |
| CN105083322B (en) | Subway train collision early warning method | |
| Roncoli et al. | Model predictive control for multi-lane motorways in presence of VACS | |
| Yin et al. | Balise arrangement optimization for train station parking via expert knowledge and genetic algorithm | |
| CN105095983B (en) | Real-time prediction method for subway train track | |
| Xiong et al. | Parallel bus rapid transit (BRT) operation management system based on ACP approach | |
| CN105093929B (en) | Planning method for conflict resolution of subway train | |
| Nie et al. | A robust integrated multi-strategy bus control system via deep reinforcement learning | |
| Hajbabaie et al. | Dynamic Speed Harmonization in Connected Urban Street Networks: Improving Mobility | |
| Wan et al. | Fair and Efficient Traffic Light Control with Reinforcement Learning | |
| Zhang et al. | A Deep Reinforcement Learning Traffic Control Model for Pedestrian and Vehicle Evacuation in the Parking Lot | |
| Li et al. | Elevating adaptive traffic signal control in semi‐autonomous traffic dynamics by using connected and automated vehicles as probes | |
| Li et al. | STAdi-DMPC: A Trajectory Prediction Based Longitudinal Control of Connected and Automated Truck Platoon for Mixed Traffic Flow |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| 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 |
Application publication date: 20170616 |
|
| RJ01 | Rejection of invention patent application after publication |