CA3147820A1 - Method for controlling a train within a train control system, and train control system - Google Patents
Method for controlling a train within a train control system, and train control system Download PDFInfo
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- CA3147820A1 CA3147820A1 CA3147820A CA3147820A CA3147820A1 CA 3147820 A1 CA3147820 A1 CA 3147820A1 CA 3147820 A CA3147820 A CA 3147820A CA 3147820 A CA3147820 A CA 3147820A CA 3147820 A1 CA3147820 A1 CA 3147820A1
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000012502 risk assessment Methods 0.000 claims abstract description 67
- 230000005540 biological transmission Effects 0.000 claims description 13
- 101000879675 Streptomyces lavendulae Subtilisin inhibitor-like protein 4 Proteins 0.000 abstract description 8
- 230000033001 locomotion Effects 0.000 description 14
- 230000008859 change Effects 0.000 description 8
- 238000007726 management method Methods 0.000 description 8
- 238000004458 analytical method Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 125000002015 acyclic group Chemical group 0.000 description 3
- 238000013459 approach Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013179 statistical model Methods 0.000 description 2
- 208000033748 Device issues Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000001364 causal effect Effects 0.000 description 1
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- 230000007613 environmental effect Effects 0.000 description 1
- 238000005755 formation reaction Methods 0.000 description 1
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- 239000011435 rock Substances 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L15/00—Indicators provided on the vehicle or train for signalling purposes
- B61L15/0062—On-board target speed calculation or supervision
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
- B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
- B61L27/10—Operations, e.g. scheduling or time tables
- B61L27/16—Trackside optimisation of vehicle or train operation
-
- 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
- B61L2027/202—Trackside control of safe travel of vehicle or train, e.g. braking curve calculation using European Train Control System [ETCS]
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Train Traffic Observation, Control, And Security (AREA)
Abstract
The invention relates to a method for controlling a train within a train control system, comprising the following method steps - creating an accident model (AccM), wherein accident classes and accident influence factors are determined; - determining a route reservation (RES) specific to the train, comprising a route reservation region and a route profile; - sending a request (A) for releasing the determined route reservation (RES) to a risk analysis device (MAXd); - carrying out a real-time risk analysis for the route reservation (RES) by means of the risk analysis device (MAXd) for at least some of the determined different accident classes, wherein a risk factor (RF) for the route reservation (RES) is determined, and as an outcome it is determined whether the risk factor (RF) is acceptable; - releasing or rejecting the route reservation (RES) depending on the outcome of the risk analysis. The planning, configuration/project planning and approval can thus be simplified and the route utilization can be optimized at a high safety level (safety integrity level SIL4).
Description
Method for controlling a train within a train control system, and train control system Background of the invention 5 The invention relates to a method for controlling a train within a train control system and to a train control system for carrying out the method.
To avoid accidents in railway operations, it is known to use automatic train control systems (track control and train sequence control) [1][7][8].
Track control and train sequence control for a railway system are currently carried out by technical systems, in particular by signal boxes, electronic train control systems such as ETCS (European Train Control System) and operating rules [2].
15 For the design of a railway system according to the prior art, a static hazard analysis and a risk assessment for the planning of infrastructure and operational running are carried out once in advance.
To avoid accidents in railway operations, it is known to use automatic train control systems (track control and train sequence control) [1][7][8].
Track control and train sequence control for a railway system are currently carried out by technical systems, in particular by signal boxes, electronic train control systems such as ETCS (European Train Control System) and operating rules [2].
15 For the design of a railway system according to the prior art, a static hazard analysis and a risk assessment for the planning of infrastructure and operational running are carried out once in advance.
[2] discloses the use of a Bayesian network for probabilistic safety analysis (PSA) of railway lines.
20 A Bayesian network (also called: decision network, Bayes(ian) model or probabilistically directed acyclic graph model) is a probabilistic graph model (statistical model) that represents a set of variables and their conditional dependencies over a directed acyclic graph (DAG).
In order for a railway system, for which a risk analysis is carried out during infrastructure planning, to 25 be able to be operated safely, it is designed in such a way that even the slowest and longest train can be guided safely. However, this means that the system is designed too defensively for most trains (too long rail sections, too large braking distances, etc.), so that optimal track utilization is not usually achieved. In addition, static routes and static movement authorities are created. Due to different train types and different routes, this requires a high level of effort in terms of project planning and 30 verification.
Bayesian networks are also used in the IT sector to carry out risk assessments in the context of security management [3][4][5][6].
35 [3] and [4] describe a real-time safety assessment for carrying out a dynamic assessment of health and safety conditions on the construction site using a Hidden Markov Model. The safety risk of the workers is linked to the locations on site.
Object of the invention The object of the invention is to provide a method for controlling a train within a train control system, and a train control system, with the aid of which method and system planning, configuration/project planning and approval can be simplified and track utilization can be optimized with a high safety level (safety integrity level SIL4).
Description of the invention This object is achieved according to the invention by a method according to claim 1 and a train control system according to claim 8.
The method according to the invention comprises the following method steps:
= creating an accident model, accident classes and accident influencing factors being determined;
= ascertaining an individual track reservation for the train, comprising a track reservation area and a track profile;
= sending, to a risk assessment device, a request to release the ascertained track reservation, so = carrying out a real-time risk assessment for the track reservation by means of the risk assessment device for at least some of the ascertained accident classes, a risk factor for the track reservation being ascertained, and as a result it being ascertained whether the risk factor is acceptable; and = releasing or rejecting the track reservation depending on the result of the risk assessment.
The measured method uses a statically created accident model to understand and describe railway accidents. The accident modeling is preferably carried out outside of the operation of the train control system. The following can be defined as accident classes, for example:
"derailment," "collision with other trains," "collision with people/objects," and "accidents at level crossings." Accident influencing factors are understood to be factors (elements) that can contribute to the events contained in the accident classes, i.e., that influence the accident risk, e.g., environment, driver, driver decision, train, infrastructure, speed, and supervision). The description of the infrastructure and the train, inter alia, must be ascertained safely (SIL4) for this purpose.
According to the invention, a predefined route is not released, but rather an individual track reservation is created, i.e., the track reservation is ascertained specifically for a selected train at a particular location at a particular time. A track reservation comprises a track section, individually selected/ascertained for a particular train, of the track to be traveled by the train, but is not pre-planned, i.e., does not affect a so specified track section.
20 A Bayesian network (also called: decision network, Bayes(ian) model or probabilistically directed acyclic graph model) is a probabilistic graph model (statistical model) that represents a set of variables and their conditional dependencies over a directed acyclic graph (DAG).
In order for a railway system, for which a risk analysis is carried out during infrastructure planning, to 25 be able to be operated safely, it is designed in such a way that even the slowest and longest train can be guided safely. However, this means that the system is designed too defensively for most trains (too long rail sections, too large braking distances, etc.), so that optimal track utilization is not usually achieved. In addition, static routes and static movement authorities are created. Due to different train types and different routes, this requires a high level of effort in terms of project planning and 30 verification.
Bayesian networks are also used in the IT sector to carry out risk assessments in the context of security management [3][4][5][6].
35 [3] and [4] describe a real-time safety assessment for carrying out a dynamic assessment of health and safety conditions on the construction site using a Hidden Markov Model. The safety risk of the workers is linked to the locations on site.
Object of the invention The object of the invention is to provide a method for controlling a train within a train control system, and a train control system, with the aid of which method and system planning, configuration/project planning and approval can be simplified and track utilization can be optimized with a high safety level (safety integrity level SIL4).
Description of the invention This object is achieved according to the invention by a method according to claim 1 and a train control system according to claim 8.
The method according to the invention comprises the following method steps:
= creating an accident model, accident classes and accident influencing factors being determined;
= ascertaining an individual track reservation for the train, comprising a track reservation area and a track profile;
= sending, to a risk assessment device, a request to release the ascertained track reservation, so = carrying out a real-time risk assessment for the track reservation by means of the risk assessment device for at least some of the ascertained accident classes, a risk factor for the track reservation being ascertained, and as a result it being ascertained whether the risk factor is acceptable; and = releasing or rejecting the track reservation depending on the result of the risk assessment.
The measured method uses a statically created accident model to understand and describe railway accidents. The accident modeling is preferably carried out outside of the operation of the train control system. The following can be defined as accident classes, for example:
"derailment," "collision with other trains," "collision with people/objects," and "accidents at level crossings." Accident influencing factors are understood to be factors (elements) that can contribute to the events contained in the accident classes, i.e., that influence the accident risk, e.g., environment, driver, driver decision, train, infrastructure, speed, and supervision). The description of the infrastructure and the train, inter alia, must be ascertained safely (SIL4) for this purpose.
According to the invention, a predefined route is not released, but rather an individual track reservation is created, i.e., the track reservation is ascertained specifically for a selected train at a particular location at a particular time. A track reservation comprises a track section, individually selected/ascertained for a particular train, of the track to be traveled by the train, but is not pre-planned, i.e., does not affect a so specified track section.
- 3 -The track reservation can be requested directly by the train, by the dispatcher or by an operational device. For this purpose, the dispatcher/operational device first ascertains the extent of the track reservation for the selected train and the track profile. The track profile includes a track description for 85 the extent of the track reservation, in particular a gradient profile (altitude difference in the reservation area depending on the distance), a speed profile (maximum speed allowed within the reservation area depending on the distance, permitted axle load, superelevation of the curve, etc.). The maximum speed permitted in the reservation area depends in particular on the maximum track speed (maximum permitted speed), the maximum train speed (e.g., depending on the axle load, freight train, passenger 90 train, braking power, etc.), curve radius and superelevation in the curves, and temporary speed limits.
The extent of the track reservation is influenced, for example, by whether/where there are other trains, workers, construction sites, etc. on the track to be traveled.
The request is made to the risk assessment device, which carries out a risk assessment for the 95 requested (individually ascertained) track reservation in real time. The risk assessment is preferably carried out for all accident classes previously ascertained within the framework of the method. The real-time risk assessment includes determining a risk factor for the movement of the selected train within the track reservation. The risk (risk factor) is thus ascertained and assessed individually for each train in real time.
loo The risk assessment device uses the real-time risk assessment to assess whether the commands required for track reservation (changing of points, movement authority for trains, etc.) are permissible.
For this purpose, the accident probability (risk factor) is ascertained for the predefined accident classes. If the risk factor remains below a previously defined limit value (risk factor = acceptable), the 105 commands necessary for the track reservation (e.g., setting of points, signals, etc.) are executed and the track reservation is released. The track reservation is released when the field elements required for the track reservation are set (e.g., correct switch position, signal position, instruction of the level crossing opening). The release of the track reservation causes a movement authority to be issued to a train.
By means of the risk assessment according to the invention, the risk assessment device can, in the event of fault reports from field elements or position/speed reports from the train, assess whether the current situation will lead to a hazard and, if necessary, take safety measures.
115 The method according to the invention adapts dynamically to the traffic situation and the commands from the operator, calculates all individual risks and ensures maximum safety integrity (SIL4) with the highest throughput within a supervised control area, before issuing commands to field elements and driving authorizations to trains.
The extent of the track reservation is influenced, for example, by whether/where there are other trains, workers, construction sites, etc. on the track to be traveled.
The request is made to the risk assessment device, which carries out a risk assessment for the 95 requested (individually ascertained) track reservation in real time. The risk assessment is preferably carried out for all accident classes previously ascertained within the framework of the method. The real-time risk assessment includes determining a risk factor for the movement of the selected train within the track reservation. The risk (risk factor) is thus ascertained and assessed individually for each train in real time.
loo The risk assessment device uses the real-time risk assessment to assess whether the commands required for track reservation (changing of points, movement authority for trains, etc.) are permissible.
For this purpose, the accident probability (risk factor) is ascertained for the predefined accident classes. If the risk factor remains below a previously defined limit value (risk factor = acceptable), the 105 commands necessary for the track reservation (e.g., setting of points, signals, etc.) are executed and the track reservation is released. The track reservation is released when the field elements required for the track reservation are set (e.g., correct switch position, signal position, instruction of the level crossing opening). The release of the track reservation causes a movement authority to be issued to a train.
By means of the risk assessment according to the invention, the risk assessment device can, in the event of fault reports from field elements or position/speed reports from the train, assess whether the current situation will lead to a hazard and, if necessary, take safety measures.
115 The method according to the invention adapts dynamically to the traffic situation and the commands from the operator, calculates all individual risks and ensures maximum safety integrity (SIL4) with the highest throughput within a supervised control area, before issuing commands to field elements and driving authorizations to trains.
- 4 -120 The method according to the invention describes a generic solution which does not require any specific project planning and which considerably simplifies the approval process. It does not require any operational rules and does not require planning of routes or movement authorities.
A current position of the train is preferably determined to ascertain the track reservation. The position 125 is preferably determined by means of satellites (GNSS).
In order to prevent a part of a train remaining on the track and posing a danger to other trains, the integrity of the train is preferably determined in order to ascertain the track reservation.
130 It is particularly advantageous if the real-time risk assessment is carried out exclusively on the basis of physical and/or geometric parameters of the accident influencing factors and of error probabilities of the accident influencing factors. This is advantageous because the physical and geometric parameters can be ascertained easily or are known in any case.
135 The error probability of one accident influencing factor influences the error probability of the other accident influencing factors.
In a special variant of the method according to the invention, a probabilistic graph model (graphic model) is used for the real-time risk assessment, which model describes the accident model previously created, tcto a graph with nodes and edges being built/instantiated, conditional probabilities being stored for each node. Probabilistic Graphic Models (PGM) are graphs with nodes and edges, the nodes representing probability variables. The absence of edges between nodes of the graph indicates their independence.
According to the invention, a graph is instantiated/generated which represents the topology of the railroad system. The instantiated graph describes the train and the geometric infrastructure of the 145 railroad system.
The graph is preferably a directed and/or acyclic graph, for example a graph according to a Bayesian network (directed and acyclic) or according to a Markov model.
150 The graph model used according to the invention is a statistical model which represents a set of variables (accident influencing factors/nodes) and their dependencies via a directed, in particular acyclic, graph (DAG). The dependencies of the nodes are modeled using conditional probabilities. The graph generically describes the accident model created previously. The graph can be viewed as a dynamic network which is set up to calculate accident probability rates (probability network). The generic graph 155 is generated dynamically, i.e., depending on the current situation, the track reservation and the geometric description of the track and the train. Ideally, a Bayesian network (BN) is used as the graph, since Bayesian networks are ideal for detecting an event that has occurred and for predicting the probability that one of several possible known causes was the decisive factor.
The probability distribution of all accident influencing factors involved is represented in compact form using known
A current position of the train is preferably determined to ascertain the track reservation. The position 125 is preferably determined by means of satellites (GNSS).
In order to prevent a part of a train remaining on the track and posing a danger to other trains, the integrity of the train is preferably determined in order to ascertain the track reservation.
130 It is particularly advantageous if the real-time risk assessment is carried out exclusively on the basis of physical and/or geometric parameters of the accident influencing factors and of error probabilities of the accident influencing factors. This is advantageous because the physical and geometric parameters can be ascertained easily or are known in any case.
135 The error probability of one accident influencing factor influences the error probability of the other accident influencing factors.
In a special variant of the method according to the invention, a probabilistic graph model (graphic model) is used for the real-time risk assessment, which model describes the accident model previously created, tcto a graph with nodes and edges being built/instantiated, conditional probabilities being stored for each node. Probabilistic Graphic Models (PGM) are graphs with nodes and edges, the nodes representing probability variables. The absence of edges between nodes of the graph indicates their independence.
According to the invention, a graph is instantiated/generated which represents the topology of the railroad system. The instantiated graph describes the train and the geometric infrastructure of the 145 railroad system.
The graph is preferably a directed and/or acyclic graph, for example a graph according to a Bayesian network (directed and acyclic) or according to a Markov model.
150 The graph model used according to the invention is a statistical model which represents a set of variables (accident influencing factors/nodes) and their dependencies via a directed, in particular acyclic, graph (DAG). The dependencies of the nodes are modeled using conditional probabilities. The graph generically describes the accident model created previously. The graph can be viewed as a dynamic network which is set up to calculate accident probability rates (probability network). The generic graph 155 is generated dynamically, i.e., depending on the current situation, the track reservation and the geometric description of the track and the train. Ideally, a Bayesian network (BN) is used as the graph, since Bayesian networks are ideal for detecting an event that has occurred and for predicting the probability that one of several possible known causes was the decisive factor.
The probability distribution of all accident influencing factors involved is represented in compact form using known
- 5 -160 conditional probabilities. The conditional probabilities for each node are stored in a probability table.
The structure of this graph and its probability tables are determined by the accident model. The probability tables include physical and/or geometric parameters of the relevant accident influencing factor and the error probabilities thereof. The physical and/or geometric parameters of the train (axle load, braking power, etc.) can be ascertained from the train mechanics, for example.
A system-theoretical approach, STAMP (Systems-Theoretic Accident Model and Processes), using a Systems-Theoretic Process Analysis (STPA) is preferably used to create the accident model. The accident model represents the causal dependencies between accident influencing factors and accident classes and serves as the basis for the structure of the graph. Other methods can also be 170 used instead of STAMP/STPA.
The nodes are preferably accident class nodes and element nodes, an element node representing an accident influencing factor and an accident class node representing one of the accident classes.
175 A conditional probability distribution (probability table) of the random variables (accident influencing factor) represented by the node is stored for each node. The probability distribution assigns random variables to the parent nodes.
The track reservation can include a plurality of track reservation subareas, a (preferably directed, in 18o particular acyclic) subgraph being built/instantiated for each track reservation subarea, which subgraph represents a subnetwork.
The track reservation subareas are preferably defined (limited) by changes on the track. This means that if a track parameter changes (e.g., due to a new gradient profile, branching of the track through a 185 switch, or change in the gradient of the track), the previous subarea ends and the next track reservation subarea begins. This is particularly advantageous since a change in the track parameters can influence the accident risk (risk factor). A partial risk factor is therefore calculated for each track reservation subarea. The accident risk of the various track reservation subareas can depend on different accident influencing factors. The subnetworks of the different track reservation subareas can 190 therefore include different types and numbers of nodes. The track reservation is only released if the accident risk (partial risk factor) in each track reservation subarea is lower than the acceptable risk factor. The risk factor for the entire track reservation is calculated as the sum of the partial risk factors.
Position reports of the train are preferably determined and transmitted to the risk assessment device in 195 time intervals. After each position report, data obtained from the position reports for track reservation subareas are entered in the graph.
A position report preferably includes position data, train information (e.g., train integrity, train length) and speed information. As soon as a position report of the train (OBU) is updated, the current data of
The structure of this graph and its probability tables are determined by the accident model. The probability tables include physical and/or geometric parameters of the relevant accident influencing factor and the error probabilities thereof. The physical and/or geometric parameters of the train (axle load, braking power, etc.) can be ascertained from the train mechanics, for example.
A system-theoretical approach, STAMP (Systems-Theoretic Accident Model and Processes), using a Systems-Theoretic Process Analysis (STPA) is preferably used to create the accident model. The accident model represents the causal dependencies between accident influencing factors and accident classes and serves as the basis for the structure of the graph. Other methods can also be 170 used instead of STAMP/STPA.
The nodes are preferably accident class nodes and element nodes, an element node representing an accident influencing factor and an accident class node representing one of the accident classes.
175 A conditional probability distribution (probability table) of the random variables (accident influencing factor) represented by the node is stored for each node. The probability distribution assigns random variables to the parent nodes.
The track reservation can include a plurality of track reservation subareas, a (preferably directed, in 18o particular acyclic) subgraph being built/instantiated for each track reservation subarea, which subgraph represents a subnetwork.
The track reservation subareas are preferably defined (limited) by changes on the track. This means that if a track parameter changes (e.g., due to a new gradient profile, branching of the track through a 185 switch, or change in the gradient of the track), the previous subarea ends and the next track reservation subarea begins. This is particularly advantageous since a change in the track parameters can influence the accident risk (risk factor). A partial risk factor is therefore calculated for each track reservation subarea. The accident risk of the various track reservation subareas can depend on different accident influencing factors. The subnetworks of the different track reservation subareas can 190 therefore include different types and numbers of nodes. The track reservation is only released if the accident risk (partial risk factor) in each track reservation subarea is lower than the acceptable risk factor. The risk factor for the entire track reservation is calculated as the sum of the partial risk factors.
Position reports of the train are preferably determined and transmitted to the risk assessment device in 195 time intervals. After each position report, data obtained from the position reports for track reservation subareas are entered in the graph.
A position report preferably includes position data, train information (e.g., train integrity, train length) and speed information. As soon as a position report of the train (OBU) is updated, the current data of
-6-200 the position report is entered in the graph and the graph is recalculated at least for the track reservation subarea lying ahead (based on the current position of the train).
The subnetworks are interconnected, and they thus form a dynamic network such that a change in one subnetwork can lead to changes in the other subnetworks.
The invention also relates to a train control system for carrying out the method described above. The train control system according to the invention comprises:
= a risk assessment device for creating a real-time risk assessment for a previously determined track 210 reservation for a train using an accident model and for transmitting commands to field elements;
and = a transmission device for transmitting train information relating to the train, the transmission device having an interface to the risk assessment device.
The risk assessment device according to the invention is set up to calculate, with the aid of a graph model, a risk factor for a particular track reservation at SIL4 level, to compare this risk factor with a previously defined acceptable risk factor and, if necessary, to release the track reservation if the calculated risk factor is smaller than the acceptable risk factor.
The transmission device is preferably arranged in the train.
The risk assessment device preferably has interfaces to field elements of the train control system. The field elements can be actuated and supervised via these interfaces in order to create the conditions for 225 releasing the track reservation (e.g., by changing points, switching signals, etc.).
In a specific embodiment of the train control system according to the invention, there is an operational device (0B) for ascertaining the individual track reservation for the train, the track reservation comprising a track reservation area and a track profile, the operational device having an interface to the 230 risk assessment device.
The operational device is preferably a device having an interface to a train management system. The train management system is set up to provide a timetable, to control and supervise the train traffic according to the timetable, to optimize the current timetable, to identify and solve conflicts in the event 235 of problems in the current train traffic.
The operational device creates the track reservations with current train and track parameters (train length, braking power, track properties, rail condition) at SILO level.
The subnetworks are interconnected, and they thus form a dynamic network such that a change in one subnetwork can lead to changes in the other subnetworks.
The invention also relates to a train control system for carrying out the method described above. The train control system according to the invention comprises:
= a risk assessment device for creating a real-time risk assessment for a previously determined track 210 reservation for a train using an accident model and for transmitting commands to field elements;
and = a transmission device for transmitting train information relating to the train, the transmission device having an interface to the risk assessment device.
The risk assessment device according to the invention is set up to calculate, with the aid of a graph model, a risk factor for a particular track reservation at SIL4 level, to compare this risk factor with a previously defined acceptable risk factor and, if necessary, to release the track reservation if the calculated risk factor is smaller than the acceptable risk factor.
The transmission device is preferably arranged in the train.
The risk assessment device preferably has interfaces to field elements of the train control system. The field elements can be actuated and supervised via these interfaces in order to create the conditions for 225 releasing the track reservation (e.g., by changing points, switching signals, etc.).
In a specific embodiment of the train control system according to the invention, there is an operational device (0B) for ascertaining the individual track reservation for the train, the track reservation comprising a track reservation area and a track profile, the operational device having an interface to the 230 risk assessment device.
The operational device is preferably a device having an interface to a train management system. The train management system is set up to provide a timetable, to control and supervise the train traffic according to the timetable, to optimize the current timetable, to identify and solve conflicts in the event 235 of problems in the current train traffic.
The operational device creates the track reservations with current train and track parameters (train length, braking power, track properties, rail condition) at SILO level.
-7-240 The risk assessment device does not make any decision as to where the train is to travel to and with which speed profile the train is allowed to move, so it has no influence on the configuration of the track reservation.
The operational device is preferably also responsible for inquiring about the track reservation from the 245 risk assessment device. Alternatively, a request for a track reservation can also be made by the train itself for this purpose, for example.
The device for transmitting train information can be, for example, an on-board unit (OBU) of the train or a track-side device.
The on-board unit (OBU) preferably comprises a position-determining device.
By means of the method according to the invention and the train control system according to the invention, the optimization of operations (optimization of train journeys, conflict resolution) can be 255 carried out at SILO level with current parameters, whereas the risk analysis is carried out flexibly in real time at 5IL4 level.
Further advantages of the invention can be found in the descriptions and the drawings. Likewise, according to the invention, the aforementioned features and those which are to be explained below can 260 each be used individually for themselves or for a plurality of combinations of any kind. The embodiments shown and described are not to be understood as an exhaustive enumeration but rather have exemplary character for the description of the invention.
Detailed descriptions of the invention and drawings Fig. 1 is a schematic representation of the 5IL4 area of a preferred embodiment of a control system according to the invention.
Fig. 2 is a schematic representation of a preferred embodiment of a control system according to 270 the invention with an SILO area.
Fig. 3 shows the sequence of the various method steps of the method according to the invention.
Fig. 4 shows a graph of a probabilistic graph model based on an accident model with an accident 275 class node and a plurality of element nodes.
Fig. 5 shows a speed profile of a track reservation as well as subnetworks of the graph created for the risk assessment for the track reservation.
The operational device is preferably also responsible for inquiring about the track reservation from the 245 risk assessment device. Alternatively, a request for a track reservation can also be made by the train itself for this purpose, for example.
The device for transmitting train information can be, for example, an on-board unit (OBU) of the train or a track-side device.
The on-board unit (OBU) preferably comprises a position-determining device.
By means of the method according to the invention and the train control system according to the invention, the optimization of operations (optimization of train journeys, conflict resolution) can be 255 carried out at SILO level with current parameters, whereas the risk analysis is carried out flexibly in real time at 5IL4 level.
Further advantages of the invention can be found in the descriptions and the drawings. Likewise, according to the invention, the aforementioned features and those which are to be explained below can 260 each be used individually for themselves or for a plurality of combinations of any kind. The embodiments shown and described are not to be understood as an exhaustive enumeration but rather have exemplary character for the description of the invention.
Detailed descriptions of the invention and drawings Fig. 1 is a schematic representation of the 5IL4 area of a preferred embodiment of a control system according to the invention.
Fig. 2 is a schematic representation of a preferred embodiment of a control system according to 270 the invention with an SILO area.
Fig. 3 shows the sequence of the various method steps of the method according to the invention.
Fig. 4 shows a graph of a probabilistic graph model based on an accident model with an accident 275 class node and a plurality of element nodes.
Fig. 5 shows a speed profile of a track reservation as well as subnetworks of the graph created for the risk assessment for the track reservation.
-8-280 Fig. 1 shows the essential components of the train control system according to the invention. The core of the control system according to the invention is a risk assessment device MAXd.
The risk assessment device MAXd includes elements for track control and train sequence control. In addition, the risk assessment device can include elements for rail track worker protection and for 285 controlling level crossings.
The risk assessment device MAXd receives, from a transmission device OBS via an interface, train information lz relating to a train for which a track reservation RES is to be released (selected train).
The train information lz can include train position, speed, train length, and mass, for example. The 290 train position is ascertained by means of a train detection device VD. The train detection device VD
and the transmission device OBS can (but do not have to) be arranged in the train. The transmission device OBS can be a vehicle-mounted control device, for example. However, it is also possible that the train formations lz are transmitted from a (train-)external transmission device to the risk assessment device MAXd and/or that the train detection device VD is arranged on the track side.
The risk assessment device MAXd receives, via a further interface, information IFE from field elements FE relating to the current states of the field elements FE.
The risk assessment device MAXd is set up to forward commands K to the field elements in order to 300 create the prerequisites for release of a track reservation (RES). In addition, the risk assessment device MAXd can be set up to transmit movement authorities MA to trains.
Alternatively, the movement authority MA can also be communicated to the train via track-side signals (not shown). In Fig. 1, it is assumed that the transmission device OBS is arranged within the train, so that the transmission device OBS can also receive the movement authority MA.
According to the invention, the track reservation RES is created outside the 5IL4 area of the train control system according to the invention. This can be done by an operational device OP, as shown in Fig. 2.
A computer with business logic for motion control, in particular with decision support software (decision support system), can be used as the "operational device." Instead, however, a dispatcher can also take 310 over some of the tasks of the operational unit. The operational device OP is responsible for optimization of operations (optimization of train journeys). This optimization of operations takes place at SILO level with current parameters (train length, braking power, track properties, rail condition).
The operational device OP knows the national requirements and the operating rules. The system 315 state SYS (i.e., field element states, position, speed of trains, etc.) is transmitted from the risk assessment device MAXd to the operational device OR On this basis, the operational device OP can generate a track reservation RES individually for a specific train, i.e., individually define the extent of the track reservation RES, ascertain a track profile including a speed profile MP and put the points in the correct position for the track reservation RES.
The risk assessment device MAXd includes elements for track control and train sequence control. In addition, the risk assessment device can include elements for rail track worker protection and for 285 controlling level crossings.
The risk assessment device MAXd receives, from a transmission device OBS via an interface, train information lz relating to a train for which a track reservation RES is to be released (selected train).
The train information lz can include train position, speed, train length, and mass, for example. The 290 train position is ascertained by means of a train detection device VD. The train detection device VD
and the transmission device OBS can (but do not have to) be arranged in the train. The transmission device OBS can be a vehicle-mounted control device, for example. However, it is also possible that the train formations lz are transmitted from a (train-)external transmission device to the risk assessment device MAXd and/or that the train detection device VD is arranged on the track side.
The risk assessment device MAXd receives, via a further interface, information IFE from field elements FE relating to the current states of the field elements FE.
The risk assessment device MAXd is set up to forward commands K to the field elements in order to 300 create the prerequisites for release of a track reservation (RES). In addition, the risk assessment device MAXd can be set up to transmit movement authorities MA to trains.
Alternatively, the movement authority MA can also be communicated to the train via track-side signals (not shown). In Fig. 1, it is assumed that the transmission device OBS is arranged within the train, so that the transmission device OBS can also receive the movement authority MA.
According to the invention, the track reservation RES is created outside the 5IL4 area of the train control system according to the invention. This can be done by an operational device OP, as shown in Fig. 2.
A computer with business logic for motion control, in particular with decision support software (decision support system), can be used as the "operational device." Instead, however, a dispatcher can also take 310 over some of the tasks of the operational unit. The operational device OP is responsible for optimization of operations (optimization of train journeys). This optimization of operations takes place at SILO level with current parameters (train length, braking power, track properties, rail condition).
The operational device OP knows the national requirements and the operating rules. The system 315 state SYS (i.e., field element states, position, speed of trains, etc.) is transmitted from the risk assessment device MAXd to the operational device OR On this basis, the operational device OP can generate a track reservation RES individually for a specific train, i.e., individually define the extent of the track reservation RES, ascertain a track profile including a speed profile MP and put the points in the correct position for the track reservation RES.
-9-The operational device OP thus creates a track reservation RES individually for a selected train. In the embodiment shown in Fig. 2, a request A for the track reservation (request to release a previously created track reservation RES) to the risk assessment device MAXd, is also sent via the operational device OP.
The operational device communicates with a train management unit TIVS, which is responsible for planning train journeys. In particular, the system state SYS is transmitted to the train management unit TMS, since said state is required so that the train management unit TMS can intervene in terms of scheduling if necessary; e.g., for rerouting subsequent trains in the event of a disruption to a train on 330 the track.
The main task of the risk assessment device MAXd is to calculate a risk assessment for a requested track reservation RES on the basis of a previously established accident model AccM and on the basis of the train information lz and the field element information IFE. Using the risk assessment, the risk 335 assessment device MAXd decides whether the requested track reservation RES is released and the train in question receives the corresponding movement authority MA. The real-time risk assessment takes place at SIL4 level.
Fig. 3 shows the sequence of the method according to the invention: if the risk assessment device 340 MAXd receives a request A for a track reservation RES for a particular train, the risk assessment device MAXd calculates, in real time, a risk factor RF for the requested track reservation RES, or a plurality of partial risk factors for track reservation subareas of the track reservation RES, using the accident model AccM as well as the train information lz and field element information IEE available to said device. If the risk factor RF is below a previously defined acceptable limit value Lim, the risk 345 assessment device MAXd transmits commands K to the field elements FE in order to set the field element settings necessary for the track reservation RES. As soon as the necessary field element settings have been made (the track reservation RES is therefore "passable"), the risk assessment device issues the movement authority MA. If the ascertained risk factor RF is above the acceptable limit value Lim, no commands K and no movement authority MA are issued.
Instead, information 350 relating to the rejection of the request A can be transmitted to the operational unit OP so that said unit can create an alternative track reservation.
To create the accident model AccM which is used for the real-time risk assessment according to the invention, accident classes (for example derailment, collisions with other trains, collisions with 355 people/objects, accidents at level crossings) are defined on the basis of train and track properties.
The accident model is represented by a directed graph G of a graph model, for example by a Bayesian network. Fig. 4 shows a corresponding graph for the accident class "derailment" D. Accident class D represents a node (accident class node¨shown as oval) of the graph G.
The graph G also
The operational device communicates with a train management unit TIVS, which is responsible for planning train journeys. In particular, the system state SYS is transmitted to the train management unit TMS, since said state is required so that the train management unit TMS can intervene in terms of scheduling if necessary; e.g., for rerouting subsequent trains in the event of a disruption to a train on 330 the track.
The main task of the risk assessment device MAXd is to calculate a risk assessment for a requested track reservation RES on the basis of a previously established accident model AccM and on the basis of the train information lz and the field element information IFE. Using the risk assessment, the risk 335 assessment device MAXd decides whether the requested track reservation RES is released and the train in question receives the corresponding movement authority MA. The real-time risk assessment takes place at SIL4 level.
Fig. 3 shows the sequence of the method according to the invention: if the risk assessment device 340 MAXd receives a request A for a track reservation RES for a particular train, the risk assessment device MAXd calculates, in real time, a risk factor RF for the requested track reservation RES, or a plurality of partial risk factors for track reservation subareas of the track reservation RES, using the accident model AccM as well as the train information lz and field element information IEE available to said device. If the risk factor RF is below a previously defined acceptable limit value Lim, the risk 345 assessment device MAXd transmits commands K to the field elements FE in order to set the field element settings necessary for the track reservation RES. As soon as the necessary field element settings have been made (the track reservation RES is therefore "passable"), the risk assessment device issues the movement authority MA. If the ascertained risk factor RF is above the acceptable limit value Lim, no commands K and no movement authority MA are issued.
Instead, information 350 relating to the rejection of the request A can be transmitted to the operational unit OP so that said unit can create an alternative track reservation.
To create the accident model AccM which is used for the real-time risk assessment according to the invention, accident classes (for example derailment, collisions with other trains, collisions with 355 people/objects, accidents at level crossings) are defined on the basis of train and track properties.
The accident model is represented by a directed graph G of a graph model, for example by a Bayesian network. Fig. 4 shows a corresponding graph for the accident class "derailment" D. Accident class D represents a node (accident class node¨shown as oval) of the graph G.
The graph G also
- 10 -360 includes element nodes (shown as round), each element node representing an accident influencing factor (here: driver VD, driver decisions VDDE, vehicle-mounted system (supervision) S, speed V, infrastructure INF, train RS, environmental influences E).
The accident influencing factors taken into account in the present case have the following associated 365 geometric/physical parameters/error rates, which are taken into account when creating the probability tables:
Accident influencing factor (network node) Geometric/physical parameters/error rate ETCS mode Probability for different control modes (route monitoring by the driver (staff responsibility SIR), driving on sight (OS), full supervision (FS) Driver Error rate of the relevant driver Driver decision Probabilities of braking, accelerating, no action Train Error rate of the train, speed, axle load, curve radius, superelevation in curves Environment Probability of landslide, avalanche, rock fall Speed Braking performance of the train, acceleration power of the train Infrastructure Error rate of the rail, type of train, speed of the train Supervision (OBU) Probability of supervision mode (SIR, OS, FS); depends on the availability rates of the onboard system The edges of the graph G (shown by arrows) indicate which accident influencing factors influence other accident influencing factors. A probability table is stored for each node, the probability table not 370 containing any "trained data," but rather track data, train data as well as error rates of elements and their conditional probabilities (depending on other accident class nodes).
The graph forms a "probability network," which depends on the topology of the railway system (i.e., on the geometric description of the infrastructure and the train). A change in the train or track properties 375 (e.g., maximum permitted speed) also changes the probability table. The calculation of the probabilities stored in the probability table is deterministic.
Fig. 5 shows a speed profile MP of a train of a train length LT within a 260 m long track reservation RES. The track reservation RES is divided into track reservation subareas, with a subnetwork in the 380 form of a subgraph, analogous to Fig. 4, being created for each track reservation subarea. For a track reservation RES, a large number of subgraphs/subnetworks are created which together form a graph for the track reservation RES to be assessed. A subnetwork is shown by way of example in Fig. 5 with nodes filled in black. The individual subnetworks are shown in Fig. 5 at the respective track sections (distance d) within the track reservation RES. Subnetworks are thus created and instantiated
The accident influencing factors taken into account in the present case have the following associated 365 geometric/physical parameters/error rates, which are taken into account when creating the probability tables:
Accident influencing factor (network node) Geometric/physical parameters/error rate ETCS mode Probability for different control modes (route monitoring by the driver (staff responsibility SIR), driving on sight (OS), full supervision (FS) Driver Error rate of the relevant driver Driver decision Probabilities of braking, accelerating, no action Train Error rate of the train, speed, axle load, curve radius, superelevation in curves Environment Probability of landslide, avalanche, rock fall Speed Braking performance of the train, acceleration power of the train Infrastructure Error rate of the rail, type of train, speed of the train Supervision (OBU) Probability of supervision mode (SIR, OS, FS); depends on the availability rates of the onboard system The edges of the graph G (shown by arrows) indicate which accident influencing factors influence other accident influencing factors. A probability table is stored for each node, the probability table not 370 containing any "trained data," but rather track data, train data as well as error rates of elements and their conditional probabilities (depending on other accident class nodes).
The graph forms a "probability network," which depends on the topology of the railway system (i.e., on the geometric description of the infrastructure and the train). A change in the train or track properties 375 (e.g., maximum permitted speed) also changes the probability table. The calculation of the probabilities stored in the probability table is deterministic.
Fig. 5 shows a speed profile MP of a train of a train length LT within a 260 m long track reservation RES. The track reservation RES is divided into track reservation subareas, with a subnetwork in the 380 form of a subgraph, analogous to Fig. 4, being created for each track reservation subarea. For a track reservation RES, a large number of subgraphs/subnetworks are created which together form a graph for the track reservation RES to be assessed. A subnetwork is shown by way of example in Fig. 5 with nodes filled in black. The individual subnetworks are shown in Fig. 5 at the respective track sections (distance d) within the track reservation RES. Subnetworks are thus created and instantiated
- 11 -385 (instantiation = calculation of the specific probability tables for individual nodes) for a plurality of track reservation subareas along the course of the reservation area. The track reservation subareas are defined by changes on the track (e.g., change in the point position, change in the maximum permissible speed). As a result of these changes on the track, probabilities of the accident influencing factors change and thus the influence of the individual accident influencing factors on the accident 390 probability of the accident class to which the accident influencing factors are linked changes. In areas in which the train is to be braked, the subnetworks are therefore lined up closer together. In addition, the subnetworks are interconnected (Dynamic Bayesian Network) so that a change in one subnetwork can also lead to changes in the other subnetworks. An accident influencing factor of a reservation subarea thus influences the corresponding accident influencing factor of a subsequent reservation 395 subarea.
The probability table for a particular accident influencing factor therefore differs for different reservation subareas, so that the probability tables for the different reservation subareas and thus also the risk factors have to be calculated separately. The subnetworks can differ in the number and type of nodes am and/or in the probability tables stored for the nodes. The subdivision into reservation subareas is preferably carried out in such a way that the length of a reservation subarea is at most as long as the length LT of the train for which the track reservation RES applies.
The probability of the nodes can be updated/recalculated, for example as soon as a current position 405 report of the train is available. A position report includes, e.g., position data, train data, speed information, and information regarding train integrity. For this purpose, the data of the new position report is entered in the graph and the data is re-entered for all reservation subareas (lying ahead relative to the current train position). This means that, for example, the specific reported speed is entered in a speed node that previously only had a probability of a certain speed. Of course, this 410 influences the probabilities of the subsequent nodes. These probabilities are calculated using the probability tables. A node can either represent a probability (e.g., for a particular speed) or have a specific value (e.g., a particular speed) entered.
Before the train starts to brake due to the approaching end of the track reservation RES, there is the 415 possibility that a new/extended track reservation is requested in order to avoid braking the train. Such a request can be made, for example, by a train-mounted apparatus (on-board unit).
By individually generating track reservations and performing real-time risk analyses, it is possible to significantly increase the throughput of the track: if there is, for example, on the track to be traveled by 420 train A, another train B within an area which, according to the prior art, belongs to a particular route, train A should not enter this area as long as train B is within the route.
According to the invention, however, an individual track reservation can still be defined for train A. The length of the track reservation is then defined so that it ends before train B is reached. In the speed profile of the track reservation for train A, train A must be braked accordingly. According to the invention, the reservation
The probability table for a particular accident influencing factor therefore differs for different reservation subareas, so that the probability tables for the different reservation subareas and thus also the risk factors have to be calculated separately. The subnetworks can differ in the number and type of nodes am and/or in the probability tables stored for the nodes. The subdivision into reservation subareas is preferably carried out in such a way that the length of a reservation subarea is at most as long as the length LT of the train for which the track reservation RES applies.
The probability of the nodes can be updated/recalculated, for example as soon as a current position 405 report of the train is available. A position report includes, e.g., position data, train data, speed information, and information regarding train integrity. For this purpose, the data of the new position report is entered in the graph and the data is re-entered for all reservation subareas (lying ahead relative to the current train position). This means that, for example, the specific reported speed is entered in a speed node that previously only had a probability of a certain speed. Of course, this 410 influences the probabilities of the subsequent nodes. These probabilities are calculated using the probability tables. A node can either represent a probability (e.g., for a particular speed) or have a specific value (e.g., a particular speed) entered.
Before the train starts to brake due to the approaching end of the track reservation RES, there is the 415 possibility that a new/extended track reservation is requested in order to avoid braking the train. Such a request can be made, for example, by a train-mounted apparatus (on-board unit).
By individually generating track reservations and performing real-time risk analyses, it is possible to significantly increase the throughput of the track: if there is, for example, on the track to be traveled by 420 train A, another train B within an area which, according to the prior art, belongs to a particular route, train A should not enter this area as long as train B is within the route.
According to the invention, however, an individual track reservation can still be defined for train A. The length of the track reservation is then defined so that it ends before train B is reached. In the speed profile of the track reservation for train A, train A must be braked accordingly. According to the invention, the reservation
- 12 -425 area according to the invention then does not extend as far as the route according to the prior art.
However, train A can at least travel slowly within the track reservation.
Before reaching the braking curve stored in the track reservation at the latest, a new track reservation is requested by the operational unit (since the reason for the planned braking presumably no longer exists¨e.g., train B has continued on).
430 The invention allows a holistic view of all relevant elements of the SIL4 safety logic, allowing the risk to be assessed for each train individually and in real time, in particular as to whether the current situation will not lead to a hazard and whether safety measures need to be taken. As a mathematical approach, a Bayesian network is used which is generated dynamically depending on the current situation. The geometric description of the infrastructure and the train is used for the structure of this 435 network and its probability tables. The train dynamics are calculated with the aid of known physical laws. The safety concept according to the invention adapts dynamically to the traffic situation and the commands from the operators, calculates all individual risks and ensures maximum safety integrity (SIL4) with, at the same time, the highest throughput for all relevant elements within the control area, before issuing commands to field elements and movement authorities to trains.
The approach 440 according to the invention is a generic solution that does not require any specific project planning and that significantly simplifies the approval process.
However, train A can at least travel slowly within the track reservation.
Before reaching the braking curve stored in the track reservation at the latest, a new track reservation is requested by the operational unit (since the reason for the planned braking presumably no longer exists¨e.g., train B has continued on).
430 The invention allows a holistic view of all relevant elements of the SIL4 safety logic, allowing the risk to be assessed for each train individually and in real time, in particular as to whether the current situation will not lead to a hazard and whether safety measures need to be taken. As a mathematical approach, a Bayesian network is used which is generated dynamically depending on the current situation. The geometric description of the infrastructure and the train is used for the structure of this 435 network and its probability tables. The train dynamics are calculated with the aid of known physical laws. The safety concept according to the invention adapts dynamically to the traffic situation and the commands from the operators, calculates all individual risks and ensures maximum safety integrity (SIL4) with, at the same time, the highest throughput for all relevant elements within the control area, before issuing commands to field elements and movement authorities to trains.
The approach 440 according to the invention is a generic solution that does not require any specific project planning and that significantly simplifies the approval process.
- 13 -List of reference signs 445 A Request for release of a track reservation AccM Accident model Accident class "derailment"
FE Field elements O Directed graph 450 'FE Information lz Train information Commands to field elements Lim Limit value for risk assessment LT Train length 455 MA Movement authority MP Speed profile MAXd Risk assessment device OBS Transmission device OP Operational device 460 RES Track reservation RF Risk factor SYS System state TMS Train management unit VD Train detection device 465 D Accident influencing factor: reliability of the driver VDDE Accident influencing factor: driver decisions Accident influencing factor: hazards in the surroundings (e.g., landslide, avalanche, flood) INF Accident influencing factor: Damage to the infrastructure (e.g., broken rail) 470 RS Accident influencing factor: damage to the train Accident influencing factor: mode of the vehicle-mounted system V Accident influencing factor: expected or current speed of the train
FE Field elements O Directed graph 450 'FE Information lz Train information Commands to field elements Lim Limit value for risk assessment LT Train length 455 MA Movement authority MP Speed profile MAXd Risk assessment device OBS Transmission device OP Operational device 460 RES Track reservation RF Risk factor SYS System state TMS Train management unit VD Train detection device 465 D Accident influencing factor: reliability of the driver VDDE Accident influencing factor: driver decisions Accident influencing factor: hazards in the surroundings (e.g., landslide, avalanche, flood) INF Accident influencing factor: Damage to the infrastructure (e.g., broken rail) 470 RS Accident influencing factor: damage to the train Accident influencing factor: mode of the vehicle-mounted system V Accident influencing factor: expected or current speed of the train
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510 Eurailpress, 2011
Claims (15)
1. Method for controlling a train within a train control system, comprising the following method steps:
= creating an accident model (AccM), wherein accident classes and accident influencing factors are determined;
= ascertaining an individual track reservation (RES) for the train, comprising a track reservation area and a track profile;
= sending, to a risk assessment device (MAXd), a request (A) to release the ascertained track reservation (RES);
= carrying out a real-time risk assessment for the track reservation (RES) by means of the risk assessment device (MAXd) for at least some of the ascertained different accident classes, wherein a risk factor (RF) for the track reservation (RES) is ascertained, and as a result it is ascertained whether the risk factor (RF) is acceptable; and = releasing or rejecting the track reservation (RES) depending on the result of the risk assessment.
= creating an accident model (AccM), wherein accident classes and accident influencing factors are determined;
= ascertaining an individual track reservation (RES) for the train, comprising a track reservation area and a track profile;
= sending, to a risk assessment device (MAXd), a request (A) to release the ascertained track reservation (RES);
= carrying out a real-time risk assessment for the track reservation (RES) by means of the risk assessment device (MAXd) for at least some of the ascertained different accident classes, wherein a risk factor (RF) for the track reservation (RES) is ascertained, and as a result it is ascertained whether the risk factor (RF) is acceptable; and = releasing or rejecting the track reservation (RES) depending on the result of the risk assessment.
2. Method according to claim 1, characterized in that a current position of the train is determined in order to ascertain the track reservation (RES).
3. Method according to either claim 1 or claim 2, characterized in that the integrity of the train is determined in order to ascertain the track reservation (RES).
4. Method according to any of the preceding claims, characterized in that the real-time risk assessment is carried out exclusively on the basis of physical and/or geometric parameters and error probabilities of the accident influencing factors.
5. Method according to any of the preceding claims, characterized in that a probabilistic graph model is used for the real-time risk assessment, which model describes the previously created accident model (AccM), a graph (G) with nodes and edges being built/instantiated, conditional probabilities being stored for each node.
6. Method according to claim 5, characterized in that the nodes include accident class nodes and element nodes, an element node representing one of the physical and/or geometric parameters of one of the accident influencing factors and an accident class node representing one of the accident classes.
7. Method according to either claim 5 or claim 6, characterized in that the graph (G) is a directed and/or acyclic graph (G).
8. Method according to any of claims 5 to 7, characterized in that the nodes comprise accident class nodes and element nodes, an element node representing one of the accident influencing factors and an accident class node representing one of the accident classes.
9. Method according to any of claims 5 to 8, characterized in that the track reservation (RES) comprises a plurality of track reservation subareas, a subgraph being built/instantiated for each track reservation subarea, which subgraph represents a subnetwork.
10. Method according to any of claims 5 to 9, characterized in that position reports of the train are determined and transmitted to the risk assessment device (MAXd) in time intervals, and in that after each position report for track reservation subareas, data obtained from the position reports are entered into the graph (G).
11. Train control system for carrying out the method according to any of the preceding claims, comprising:
.cndot. a risk assessment device (MAXd) for creating a real-time risk assessment for a previously determined track reservation (RES) for a train using an accident model (AccM) and for transmitting commands (K) to field elements (FE); and .cndot. a transmission device (OBS) for transmitting train information (Iz) relating to the train, wherein the transmission device (OBS) has an interface to the risk assessment device (MAXd);
.cndot. a risk assessment device (MAXd) for creating a real-time risk assessment for a previously determined track reservation (RES) for a train using an accident model (AccM) and for transmitting commands (K) to field elements (FE); and .cndot. a transmission device (OBS) for transmitting train information (Iz) relating to the train, wherein the transmission device (OBS) has an interface to the risk assessment device (MAXd);
12. Train control system according to claim 11, characterized in that the risk assessment device (MAXd) has interfaces to field elements (FE).
13. Train control system according to either claim 11 or claim 12, characterized in that an operational device (OP) is provided for ascertaining the individual track reservation (RES) for the train, the track reservation (RES) comprising a track reservation area and a track profile, the operational device (OP) having an interface to the risk assessment device (MAXd).
14. Train control system according to any of claims 11 to 13, characterized in that the transmission device is an on-board unit of the train or a track-side device.
15. Train control system according to claim 14, characterized in that the on-board unit comprises a position-determining device.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| EP19193069.2A EP3782869B1 (en) | 2019-08-22 | 2019-08-22 | Train control system and method for controlling a train within a train control system |
| EP19193069.2 | 2019-08-22 | ||
| PCT/EP2020/072897 WO2021032638A1 (en) | 2019-08-22 | 2020-08-14 | Method for controlling a train within a train control system, and train control system |
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| CA3147820A1 true CA3147820A1 (en) | 2021-02-25 |
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| CA3147820A Pending CA3147820A1 (en) | 2019-08-22 | 2020-08-14 | Method for controlling a train within a train control system, and train control system |
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| EP (1) | EP3782869B1 (en) |
| KR (1) | KR20220044842A (en) |
| AU (1) | AU2020332749A1 (en) |
| CA (1) | CA3147820A1 (en) |
| DK (1) | DK3782869T3 (en) |
| ES (1) | ES2958734T3 (en) |
| IL (1) | IL289872A (en) |
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| WO (1) | WO2021032638A1 (en) |
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| EP4098508A1 (en) * | 2021-06-03 | 2022-12-07 | Siemens Mobility AG | Method for optimised entry into a target section by dynamically determining release speed |
| CN114355941A (en) * | 2022-01-04 | 2022-04-15 | 北京石油化工学院 | Vehicle path tracking method based on improved Stanley control |
| CN115729210A (en) * | 2022-11-17 | 2023-03-03 | 华侨大学 | Rail transit train control system danger analysis method and equipment based on communication |
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| US20140257659A1 (en) * | 2013-03-11 | 2014-09-11 | Honda Motor Co., Ltd. | Real time risk assessments using risk functions |
| DE102016203695A1 (en) * | 2016-03-07 | 2017-09-07 | Siemens Aktiengesellschaft | Railway installation and method for operating a railway installation |
| EP3323693A1 (en) * | 2016-11-21 | 2018-05-23 | Siemens Schweiz AG | Train-oriented route securing logic for train safety installations |
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2019
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2020
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2022
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| AU2020332749A1 (en) | 2022-03-10 |
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