EP3213974A1 - Method for controlling vehicles in case of a conflict situation and decision support system - Google Patents
Method for controlling vehicles in case of a conflict situation and decision support system Download PDFInfo
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- EP3213974A1 EP3213974A1 EP16158494.1A EP16158494A EP3213974A1 EP 3213974 A1 EP3213974 A1 EP 3213974A1 EP 16158494 A EP16158494 A EP 16158494A EP 3213974 A1 EP3213974 A1 EP 3213974A1
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- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000005457 optimization Methods 0.000 claims abstract description 29
- 230000009471 action Effects 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 16
- 230000002068 genetic effect Effects 0.000 claims abstract description 16
- 238000004088 simulation Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 2
- 230000002265 prevention Effects 0.000 claims description 2
- 230000008569 process Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000010899 nucleation Methods 0.000 description 2
- 230000002787 reinforcement Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
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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 vehicle train operation
Abstract
(a) determination of an actual operation state of the vehicle and/or of a planned route;
(b) classification of the conflict situation thereby determining an action space;
(c) searching a knowledge base comprising rated solutions which are assigned to an operation state equal or similar to the actual operation state;
in case one or more rated solutions are found which are assigned to an operation state which is similar to the actual operation state:
(d) selection of at least one rated solution from the knowledge base;
(e) optimization of the at least one selected solution applied to the actual operation state, wherein the optimization uses a genetic algorithm and results in an optimized solution;
(f) carrying out the optimized solution.
Description
- The invention concerns a method for controlling vehicles, in particular rail bound vehicles, in case of a conflict situation wherein an optimized solution is applied, the method comprising the following steps:
- (a) determination of an actual operation state of the vehicle and/or of a planned route for said vehicle;
- (b) classification of the conflict situation by assigning the actual operation state to a conflict class thereby determining an action space in dependence of the conflict class to which the actual operation state has been assigned, wherein the action space comprises allowed measures.
- A method for resolving conflicts in a track bound transportation system is known from
EP 1 500 567 A1 - During operation of a transportation system, in particular with rail bound vehicles, conflict situations may occur, e.g. due to non-working switches, blocked tracks, defect trains, etc. Conflict situations have to be solved as quickly as possible in order to continue the operation of a transportation system. In order to solve an original conflict situation, suitable measures are applied. Yet, by applying such measures subsequent conflicts may occur. A conflict situation is supposed to be solved if the original conflict all subsequent conflicts are solved. Thus solving a conflict includes applying measures in an ordered sequence (= solution)
- According to the method known from
EP 1 500 567 A1 - It is therefore an object of the invention to provide a method for controlling vehicles in case of a conflict situation which enables to find a suitable solution which solves the original conflict and all subsequent conflicts automatically with reduced time effort and compliance to the domain ruleset.
- This object is solved by a method according to
claim 1, a decision support system according to claim 13 and a computer program according to claim 14. - The method according to the invention comprises the following steps:
- (c) searching a knowledge base comprising rated solutions which are assigned to an operation state equal or similar to the actual operation state;
in case one or more rated solutions are found which are assigned to an operation state which is similar to the actual operation state: - (d) selection of at least one rated solution from the knowledge base, wherein the at least one selected solution exclusively comprises measures of the action space;
- (e) optimization of the at least one selected solution applied to the actual operation state, wherein the optimization uses a genetic algorithm and results in an optimized solution;
- (f) carrying out the optimized solution.
- A problem domain is the field of application of the method e.g. maintenance, schedule, etc.
- An operation state describes the traffic situation of a predetermined vehicle or status of a planned route (in case the operation state can be assigned to a conflict class the operation state is a conflict situation). Classification of the actual operation state is carried out by a classifier unit, which assigns the actual operation state to a corresponding conflict class.
- A solution space comprises all possible measures for the respective problem domain. Due to the classification the solution space can be reduced to an action space by applying domain rules stored in a classifier unit (inference engine). The action space comprises suitable measures according to the conflict class to which the actual operation state has been assigned. Hence the action space is a reduced solution space. The measures and conflict classes are defined in advance and may differ from one country to another.
- A Solution can comprise a single measure or multiple measures, in particular a series of measures, wherein the single measure or the multiple measures solve the conflict situation including subsequent conflicts.
- The knowledge base comprises rated solutions, i.e. solutions which have been found to work well for a special operation state (expert feedback). Preferably a fitness factor is assigned to the rated solutions. Selection of at least one rated solution from the knowledge base is carried out by a seeder unit. The knowledge base is preferably developed during course of the inventive method (self-learning mechanism).
- The selected rated solution is then optimized by means of a solver unit resulting in the optimized solution, which is proofed by a rule-based simulation according to the domain.
- The inventive method supports decision making concerning choosing solutions in order to solve conflicts. The required time effort is reduced by combining deterministic method steps (classification, applying domain rules) with a heuristic approach (genetic algorithm). Automatically computed solutions are obtained, in particular for trains and infrastructure conflicts according to rescheduling and planning train schedules regarding specific railway operational rules, thus benefitting of both heuristic and deterministic methods.
- The advantage of combining deterministic method steps with a heuristic approach compared to pure deterministically approach is that an optimized solution can be found with less time, because it is not fully determined by all possible parameter values and all conditions of the solution space. Moreover the self-learning effect of a genetic algorithm using continuous quality reinforcement by a solution ranking mechanism leads to find a good solution in less time.
- The advantage of combining deterministic method steps with a heuristic approach compared to pure heuristically approach is that the inherent randomness of optimized solutions is drilled to being more predictable and comprehendible, because it follows a predefined domain ruleset additionally.
- In contrast to the state of the art the inventive method does not need to calculate complete solution spaces, thereby reducing time effort.
- In case no rated solution is found, which is assigned to the actual operation state or an operation state which is similar to the actual operation state, the following steps are preferably carried out:
- (d') selection of possible measures from a solution space determined by the classification of the operation state;
- (e') optimization of a combination of the selected possible measures applied to the actual operation state, wherein the optimization uses a genetic algorithm and results in an optimized solution;
- (f) applying the optimized solution.
- In this case no rated solutions are available. Thus solutions are created by selecting possible measures from the solution space. The possible measures are combined to a solution which has to be optimized by an automatic calculation of the genetic algorithm.
- If the actual operation state is not equal to an operational state for which rated solutions are stored within the knowledge base, the selected (rated or non-rated) solution cannot be applied directly but has to be modified, i.e. parameters which do not fit to the actual operation state have to be changed. Parameters which are varied during an optimization process are called optimization parameters.
- In a highly preferred variant of the inventive method optimization of step (e) and (e') respectively comprises:
- (i) variation of at least one parameter of the selected solution by using the genetic algorithm resulting in a randomized solution;
- (ii) simulation of the randomized solution by applying domain rules by means of the simulator unit;
- (iii) ranking of the randomized solution, in particular by assigning a fitness factor to the randomized solution;
- (iv) repetition of steps (i) - (iii) for different parameters.
- First a genetic algorithm is applied, i.e. the optimization parameters are varied heuristically (random mutation). The solution for the varied parameter(s) is called "randomized solution". The simulation is carried out deterministically by applying domain rules. The following ranking is carried out e.g. by assigning a fitness factor to each randomized solution.
- Variation and simulation/ranking is repeated a predetermined number of times, in particular several thousand times. The ranking of each variation does influence the ongoing iterations to increase the quality of the solution (reinforcement learning). Finally one of the randomized solutions, in particular the randomized solution with the best fitness factor, is selected to be carried out/applied (optimized solution).
- In case the actual operation state is not equal but only similar to an operation state for which rated solutions are stored within the knowledge base (e.g. due to different numbers of closed tracks) the selected rated solution is used as a starting point for the optimization process.
- In case the actual operation state does not match any operation state for which rated solutions are stored within the knowledge base the actual operation state without rated solutions is used for the optimization process.
- In order to improve the knowledge base, it is preferred that the optimized solution (in particular including the corresponding ranking) is stored in the knowledge base. Thus the knowledge is provided for a self-learning system. In a preferred variant each conflict class comprises class definitions and the actual operation state comprises state features and that in step (b) the actual operation state is assigned to the conflict class with the closest match between class definitions and state features of the actual operation state. Class definitions are features, which have to be fulfilled by an operation state in order to get assigned to the respective conflict class, e.g. blocked track, slow zone, power shut down, train/infrastructure conflicts, time conflict. The classifier unit which compares the actual operation state with class definitions and assigns the actual operation state to a corresponding conflict class.
- It is preferred that the class definitions are weighted, and that in step (b) the actual operation state is assigned to the conflict class with the highest weight score. A weighting factor is assigned to each class definition. The values of the weighting factors of class definitions which correspond to state features are added. The actual operation state is assigned to the conflict class with the highest sum of weighting factors.
- In a highly preferred variant step e) is carried out using different optimization targets. A solution can be optimized with respect to different targets. e.g. minimal delay, minimal number of trains to be rerouted, best energy efficiency...). For each target a separate simulation is executed. Therefore the varied solutions may be rated differently in dependence of the optimization target.
- In case a rated solution is found, which is assigned to the actual operation state, it is preferred that said solution is selected and carried out. I.e. in case the actual operation state is equal to an operation state for which a rated solution is stored within the knowledge base, the respective rated solution is selected, proofed by the conflict simulation and carried out. I.e. no optimization is necessary provided that at least one simulation has been executed and has confirmed that the solution is free of conflicts.
- Solutions which are stored in the knowledge base are preferably rated. The rating may comprise for example the fitness factors which have been assigned to the solution during the ranking by means of the ranker unit.
- Additionally of alternatively the optimized solution may be statistically rated. E.g. the rating of a solution is higher the more often said solution has been selected or the rating of a solution is higher the better the assigned fitness factor is.
- Additionally of alternatively the optimized solution is rated by a dispatcher. A more individual rating is possible taking into account various criteria. Also other criteria for rating are possible.
- The invention also concerns a decision support system for executing the method as described before comprising a storage unit configured to store a knowledge base, a classifier unit configured to assign an actual operation state to a conflict class defining an assigned action space, a seeder unit configured to select rated solutions of the knowledge base and a solver unit configured to carry out parameter variation by applying a genetic algorithm on basis of the assigned action space, a simulator unit configured to store domain rules supporting the detection and/or prevention of conflicts during a simulation of the actual operation state and a ranker unit configured to rank the optimized solutions by applying different ranking pattern.
- The action space comprises types of measures which are allowed for the actual operation state.
- During optimization the genetic algorithm is applied to selected solution or the combination of selected possible measures. The rated solutions selected by the seeder unit are used as advanced starting points with a reduced action spaces.
- The inventive decision support system does not require special hardware or hardware distribution. The above listed units of the inventive decision support system are software-units which can be implemented with standard laptops and PCs. Due to a component based architecture the units of the inventive decision support system may run within one hardware unit or may be distributed to several hardware units.
- The invention further concerns a computer program product for executing the method as described before. The computer program product comprises the above described decision support system.
- Further advantages can be extracted from the description and the enclosed drawing. The features mentioned above and below can be used in accordance with the invention either individually or collectively in any combination. The embodiments mentioned are not to be understood as exhaustive enumeration but rather have exemplary character for the description of the invention.
- The invention is shown in the drawing.
- FIG. 1
- shows a diagram of a conflict resolution system according to the invention.
- FIG. 2
- shows a flow diagram according to the inventive method.
- FIG. 3
- shows a flow diagram concerning knowledge seeding for different scenarios.
- The structure of the inventive conflict resolution system is shown in
FIG. 1 . The method steps of the inventive method are shown inFIG. 2 . - A
classifier unit 1 receives the actual operation state of one or more vehicle(s) e.g. from an external system. First the actual operation state is analyzed by asimulator unit 2 to identify conflicts and problems that are used for conflict classification by theclassifier unit 1. - Class definitions are stored within the classifier unit. The classifier unit compares the problem statistic of the operation state with class definitions and assigns the actual operation state to a conflict class with the best fitting class definitions, wherein different class definitions may have different weight factors.
- Information concerning the actual operation state and conflict class is sent from the
classifier unit 1 to aseeder unit 3. Further theseeder unit 3 has access to theknowledgebase 4. Theseeder unit 3 searches theknowledge base 4 for rated solutions which comply with the previously determined action space and which are assigned to an operation state which is equal or similar to the actual operation state. -
FIG. 3 shows three possible scenarios for the following knowledge seeding: - If the
knowledge base 4 comprises a rated solution which is assigned to an operation state equal to the actual operation state, the corresponding rated solution is selected, proofed by the conflict simulation and applied. - If the
knowledge base 4 does not comprise any rated solution which is assigned to an operation state which is equal or similar to the actual operation state, a combination of selected possible measures applied the actual operation state is optimized with the predefined action space. The optimized solution is carried out. - If the
knowledge base 4 comprises one or more rated solution(s) which is/are assigned to an operation state similar to the actual operation state, the one or more of the corresponding rated solution(s) is/are selected and optimized, wherein the rated solution(s) is/are used as starting point for the optimization, as shown inFIG. 2 . - The optimization process is carried out by a
solver unit 5, thesimulator unit 2 and aranker unit 6. By using a rated solution from theknowledge base 4 as starting point computing time and required computing power can be reduced. Thesolver unit 5 determines parameter to be varied in order to find an optimized solution, i.e. the parameters are varied mutually. Variation of these parameters is done by applying a genetic algorithm. Randomized solutions are obtained herewith. The randomized solutions are simulated by applying domain rules by means of thesimulator unit 2 resulting in a polished solution, i.e. thesimulator unit 2 ensures that the optimized solution complies with the stored domain rules, which helps to detect and prevent conflicts (seeFIG. 2 ). A ranking is carried out by means of the ranker unit by applying a ranking pattern, e.g. the number of conflicts which are still left in the solution, the number of trains which reach their destination in time, .... The ranking is done in respect of an optimization target by applying a fitness factor to each polished solution. Different optimization targets can be used which may result in different fitness factors for the same randomized solution. The randomized solution with the best fitness factor for a selected optimization target is selected as optimized solution to be carried out. - The optimized solution (including its fitness factor(s)) is stored in the
knowledge base 4 in order to provide a self-learning system. - The inventive method provides a heuristic optimization of solutions by means of genetic algorithm taking into consideration domain rules thereby providing good solutions with low time effort.
- In the following table an example of a conflict situation and the elements/information used for applying the inventive method are shown:
actual operation state two trains A + B cannot enter track section due to a construction site relevant conflict class conflicted track section class definitions or relevant conflict class • blocked track section • non-electrified track section • track allocation conflict (train/train) • incompatibility conflict (infrastructure/train) problem domain Train cannot reach its destination action space • change train path • adapt train speed • extend train stop • insert additional train stop operation state of rated measure of knowledge base two trains A + B cannot enter track section of a track X due to a defective switch rated measure reduction of speed of the trains A+B and change train path to another track Y fix parameter number of trains parameter to be varied speed of trains A + B train path of trains A + B optimization target • minimal train delay • maximal train throughput • minimal allocation conflicts • train destination was reached -
- 1
- classifier unit
- 2
- simulator unit
- 3
- seeder unit
- 4
- knowledgebase
- 5
- solver unit
- 6
- ranker unit
Claims (12)
- Method for controlling vehicles, in particular rail borne vehicles, in case of a conflict situation, the method comprising the following steps:(a) determination of an actual operation state of the vehicle and/or of a planned route for said vehicle;(b) classification of the conflict situation by assigning the actual operation state to a conflict class thereby determining an action space in dependence of the conflict class to which the actual operation state has been assigned, wherein the action space comprises allowed measures;characterized in that the method further comprises the following steps:(c) searching a knowledge base comprising rated solutions which are assigned to an operation state equal or similar to the actual operation state;in case one or more rated solutions are found which are assigned to an operation state which is similar to the actual operation state:(d) selection of at least one rated solution from the knowledge base, wherein the at least one selected solution exclusively comprises measures of the action space;(e) optimization of the at least one selected solution applied to the actual operation state, wherein the optimization uses a genetic algorithm and results in an optimized solution;(f) carrying out the optimized solution.
- Method according to claim 1, characterized in that in case no rated solution is found, which is assigned to the actual operation state or an operation state which is similar to the actual operation state, the following steps are carried out:(d') selection of possible measures from a solution space determined by the classification of the operation state;(e') optimization of a combination of the selected possible measures applied to the actual operation state, wherein the optimization uses a genetic algorithm and results in an optimized solution;(f) applying the optimized solution.
- Method according to any one of the preceding claims, characterized in that optimization comprises:(i) variation of at least one parameter of the selected solution is varied by using the genetic algorithm resulting in a randomized solution;(ii) simulation of the randomized solution by applying domain rules by means of the simulator unit;(iii) ranking of the randomized solution, in particular by assigning a fitness factor to the randomized solution;(iv) repetition of steps (i) - (iii) for different parameters.
- Method according to any one of the preceding claims, characterized in that the optimized solution is stored in the knowledge base.
- Method according to any one of the preceding claims, characterized in that each conflict class comprises class definitions and the actual operation state comprises state features and that in step (b) the actual operation state is assigned to the conflict class with the closest match between class definitions and state features of the actual operation state.
- Method according to claim 6, characterized in that that the class definitions are weighted, and that in step (b) the actual operation state is assigned to the conflict class with highest weight score.
- Method according to any one of the preceding claims, characterized in that step e) is carried out using different optimization targets.
- Method according to claim 1, characterized in that in case a rated solution is found, which is assigned to an operation state equal to the actual operation state, said solution is selected, proofed for conflicts by a simulation and applied.
- Method according to one of the claims 1 through 9, characterized in that the solutions in the knowledge base are statistically rated.
- Method according to one of the claims 1 through 9, characterized in that the optimized solution is rated by a dispatcher.
- Decision support system for executing the method according to any one of the preceding claims comprising a storage unit configured to store a knowledge base, a classifier unit configured to assign an actual operation state to a conflict class defining an assigned action space, a seeder unit configured to select rated solutions of the knowledge base, a solver unit configured to carry out parameter variation by applying a genetic algorithm on basis of the assigned action space, a simulator unit configured to store domain rules supporting the detection and/or prevention of conflicts during a simulation of the actual operation state and a ranker unit configured to rank the optimized solutions by applying different ranking pattern.
- Computer program product for executing the method according to any one of the claims 1 through 12.
Priority Applications (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
ES16158494T ES2844725T3 (en) | 2016-03-03 | 2016-03-03 | Vehicle control method in case of conflict situation and decision support system |
EP16158494.1A EP3213974B1 (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
PT161584941T PT3213974T (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
HUE16158494A HUE053972T2 (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
PL16158494T PL3213974T3 (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
DK16158494.1T DK3213974T3 (en) | 2016-03-03 | 2016-03-03 | PROCEDURE FOR STEERING VEHICLES IN THE EVENT OF A CONFLICT SITUATION AND DECISION SUPPORT SYSTEM |
PCT/EP2017/054907 WO2017149076A1 (en) | 2016-03-03 | 2017-03-02 | Method for controlling vehicles in case of a conflict situation and decision support system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
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EP16158494.1A EP3213974B1 (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
Publications (2)
Publication Number | Publication Date |
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EP3213974A1 true EP3213974A1 (en) | 2017-09-06 |
EP3213974B1 EP3213974B1 (en) | 2020-10-28 |
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EP16158494.1A Active EP3213974B1 (en) | 2016-03-03 | 2016-03-03 | Method for controlling vehicles in case of a conflict situation and decision support system |
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EP (1) | EP3213974B1 (en) |
DK (1) | DK3213974T3 (en) |
ES (1) | ES2844725T3 (en) |
HU (1) | HUE053972T2 (en) |
PL (1) | PL3213974T3 (en) |
PT (1) | PT3213974T (en) |
WO (1) | WO2017149076A1 (en) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110275526A (en) * | 2019-05-16 | 2019-09-24 | 贵州大学 | A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA |
WO2020043397A1 (en) * | 2018-08-31 | 2020-03-05 | Siemens Mobility GmbH | Energy optimisation during operation of a rail vehicle fleet |
CN112172872A (en) * | 2020-08-25 | 2021-01-05 | 通号城市轨道交通技术有限公司 | Method and device for detecting train conflict, electronic equipment and storage medium |
CN112249100A (en) * | 2020-10-16 | 2021-01-22 | 通号城市轨道交通技术有限公司 | Method and device for detecting route selection conflict |
EP3831692A1 (en) | 2019-12-06 | 2021-06-09 | Thales Management & Services Deutschland GmbH | Control system for a traffic network and method for preparing and/or adapting such a control system |
CN114435433A (en) * | 2022-04-12 | 2022-05-06 | 卡斯柯信号(北京)有限公司 | Method and device for verifying automatic trigger route conflict |
CN114802370A (en) * | 2021-01-29 | 2022-07-29 | 西门子交通有限公司 | Method for training a control device of a rail vehicle, control device and rail vehicle |
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US4122523A (en) * | 1976-12-17 | 1978-10-24 | General Signal Corporation | Route conflict analysis system for control of railroads |
EP1500567A1 (en) | 2003-07-22 | 2005-01-26 | Alcatel | Method for resolving conflicts in a trackbound transportation system |
EP1764280A1 (en) * | 1994-09-01 | 2007-03-21 | Harris Corporation | Scheduling system and method |
CN103481918B (en) * | 2013-07-04 | 2015-12-02 | 文超 | A kind of high speed rail train operation method of adjustment based on feedback regulation |
-
2016
- 2016-03-03 EP EP16158494.1A patent/EP3213974B1/en active Active
- 2016-03-03 PL PL16158494T patent/PL3213974T3/en unknown
- 2016-03-03 HU HUE16158494A patent/HUE053972T2/en unknown
- 2016-03-03 ES ES16158494T patent/ES2844725T3/en active Active
- 2016-03-03 PT PT161584941T patent/PT3213974T/en unknown
- 2016-03-03 DK DK16158494.1T patent/DK3213974T3/en active
-
2017
- 2017-03-02 WO PCT/EP2017/054907 patent/WO2017149076A1/en active Application Filing
Patent Citations (4)
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US4122523A (en) * | 1976-12-17 | 1978-10-24 | General Signal Corporation | Route conflict analysis system for control of railroads |
EP1764280A1 (en) * | 1994-09-01 | 2007-03-21 | Harris Corporation | Scheduling system and method |
EP1500567A1 (en) | 2003-07-22 | 2005-01-26 | Alcatel | Method for resolving conflicts in a trackbound transportation system |
CN103481918B (en) * | 2013-07-04 | 2015-12-02 | 文超 | A kind of high speed rail train operation method of adjustment based on feedback regulation |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020043397A1 (en) * | 2018-08-31 | 2020-03-05 | Siemens Mobility GmbH | Energy optimisation during operation of a rail vehicle fleet |
US20210331725A1 (en) * | 2018-08-31 | 2021-10-28 | Siemens Mobility GmbH | Energy optimisation during operation of a rail vehicle fleet |
CN110275526A (en) * | 2019-05-16 | 2019-09-24 | 贵州大学 | A kind of method for planning path for mobile robot based on improved adaptive GA-IAGA |
EP3831692A1 (en) | 2019-12-06 | 2021-06-09 | Thales Management & Services Deutschland GmbH | Control system for a traffic network and method for preparing and/or adapting such a control system |
WO2021110586A1 (en) | 2019-12-06 | 2021-06-10 | Thales Management & Services Deutschland Gmbh | Control system for a traffic network and method for preparing and/or adapting such a control system |
CN112172872A (en) * | 2020-08-25 | 2021-01-05 | 通号城市轨道交通技术有限公司 | Method and device for detecting train conflict, electronic equipment and storage medium |
CN112249100A (en) * | 2020-10-16 | 2021-01-22 | 通号城市轨道交通技术有限公司 | Method and device for detecting route selection conflict |
CN114802370A (en) * | 2021-01-29 | 2022-07-29 | 西门子交通有限公司 | Method for training a control device of a rail vehicle, control device and rail vehicle |
CN114435433A (en) * | 2022-04-12 | 2022-05-06 | 卡斯柯信号(北京)有限公司 | Method and device for verifying automatic trigger route conflict |
Also Published As
Publication number | Publication date |
---|---|
HUE053972T2 (en) | 2021-08-30 |
PL3213974T3 (en) | 2021-04-19 |
WO2017149076A1 (en) | 2017-09-08 |
DK3213974T3 (en) | 2021-01-18 |
ES2844725T3 (en) | 2021-07-22 |
EP3213974B1 (en) | 2020-10-28 |
PT3213974T (en) | 2021-01-22 |
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