Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B61—RAILWAYS
  • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
  • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
  • B61L27/10—Operations, e.g. scheduling or time tables
  • B61L27/16—Trackside optimisation of vehicle or train operation

Definitions

• 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:
• 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.
• suitable measures are applied.
• subsequent conflicts may occur.
• a conflict situation is supposed to be solved if the original conflict all subsequent conflicts are solved.
• an empirical base is created by determining and simulating the complete solution space (all possible combination of all measures).
• the measures are assigned to conflict classes.
• An actual conflict situation is assigned to a conflict class.
• the relevant measures (action space) for the corresponding conflict class are looked up in the empirical basis and are combined to solutions wherein a solution tree is worked through.
• the solutions of the solution tree are simulated and rated.
• the at least one selected solution exclusively comprises measures of the action space
• 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).
• 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 inventive method does not need to calculate complete solution spaces, thereby reducing time effort.
• 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.
• optimization parameters 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.
• 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;
• a genetic algorithm is applied, i.e. the optimization parameters are varied heuristically (random mutation).
• the solution for the varied parameters) is called "randomized solution”.
• the simulation is carried out deter- ministically 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).
• one of the randomized solutions, in particular the randomized solution with the best fitness factor, is selected to be carried out/applied (optimized solution).
• the selected rated solution is used as a starting point for the optimization process.
• 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.
• 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.
• 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.
• a rated solution 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.
• 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.
• 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.
• 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 sup- port 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.
• 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.
• FIG. 1 The structure of the inventive conflict resolution system is shown in FIG. 1.
• FIG. 2 The method steps of the inventive method are shown in FIG. 2.
• a classifier unit 1 receives the actual operation state of one or more vehi- cle(s) e.g. from an external system. First the actual operation state is analyzed by a simulator unit 2 to identify conflicts and problems that are used for conflict classification by the classifier 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 a seeder unit 3. Further the seeder unit 3 has access to the knowledgebase 4. The seeder unit 3 searches the knowledge base 4 for rated solutions which comply with the previously determined ac ⁇ tion 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 :
• 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.
• 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.
• 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 soiution(s) is/are used as starting point for the optimization, as shown in FIG. 2.
• the optimization process is carried out by a solver unit 5, the simulator unit 2 and a ranker unit 6.
• the solver 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 the simulator unit 2 resulting in a pol- ished solution, i.e. the simulator unit 2 ensures that the optimized solution complies with the stored domain rules, which helps to detect and prevent conflicts (see FIG. 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 car- ried 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.

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• Engineering & Computer Science (AREA)
• Mechanical Engineering (AREA)
• Management, Administration, Business Operations System, And Electronic Commerce (AREA)
• Train Traffic Observation, Control, And Security (AREA)

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Citations (4)

• 2016
  • 2016-03-03 ES ES16158494T patent/ES2844725T3/es active Active
  • 2016-03-03 PT PT161584941T patent/PT3213974T/pt unknown
  • 2016-03-03 PL PL16158494T patent/PL3213974T3/pl unknown
  • 2016-03-03 EP EP16158494.1A patent/EP3213974B1/en active Active
  • 2016-03-03 DK DK16158494.1T patent/DK3213974T3/da active
  • 2016-03-03 HU HUE16158494A patent/HUE053972T2/hu unknown
• 2017
  • 2017-03-02 WO PCT/EP2017/054907 patent/WO2017149076A1/en not_active Ceased

Patent Citations (4)

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