EP4155162A1 - Procédé et dispositif pourvu de compteur d'essieux permettant de faire fonctionner un passage à niveau - Google Patents

Procédé et dispositif pourvu de compteur d'essieux permettant de faire fonctionner un passage à niveau Download PDF

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Publication number
EP4155162A1
EP4155162A1 EP21198362.2A EP21198362A EP4155162A1 EP 4155162 A1 EP4155162 A1 EP 4155162A1 EP 21198362 A EP21198362 A EP 21198362A EP 4155162 A1 EP4155162 A1 EP 4155162A1
Authority
EP
European Patent Office
Prior art keywords
determined
rail vehicle
level crossing
train
closing time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21198362.2A
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German (de)
English (en)
Inventor
Jens Braband
Stephan Griebel
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Mobility GmbH
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Siemens Mobility GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Siemens Mobility GmbH filed Critical Siemens Mobility GmbH
Priority to EP21198362.2A priority Critical patent/EP4155162A1/fr
Priority to US17/950,278 priority patent/US12454296B2/en
Publication of EP4155162A1 publication Critical patent/EP4155162A1/fr
Pending legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L29/00—Safety means for rail/road crossing traffic
    • B61L29/08—Operation of gates; Combined operation of gates and signals
    • B61L29/18—Operation by approaching rail vehicle or train
    • B61L29/22—Operation by approaching rail vehicle or train electrically
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L1/00—Devices along the route controlled by interaction with the vehicle or train
    • B61L1/16—Devices for counting axles; Devices for counting vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/021—Measuring and recording of train speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L25/00—Recording or indicating positions or identities of vehicles or trains or setting of track apparatus
    • B61L25/02—Indicating or recording positions or identities of vehicles or trains
    • B61L25/04—Indicating or recording train identities
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L29/00—Safety means for rail/road crossing traffic
    • B61L29/24—Means for warning road traffic that a gate is closed or closing, or that rail traffic is approaching, e.g. for visible or audible warning
    • B61L29/28—Means for warning road traffic that a gate is closed or closing, or that rail traffic is approaching, e.g. for visible or audible warning electrically operated
    • B61L29/32—Timing, e.g. advance warning of approaching train
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L2201/00—Control methods

Definitions

  • the invention relates to a computer program product and a provision device for this computer program product, the computer program product being equipped with program instructions for carrying out this method.
  • document EP 2 718 168 B1 relates to a method for operating a railway safety system with at least one route facility, taking into account when the rail vehicle enters the switch-on section
  • railway safety system detected speed measurement variable.
  • the measured speed variable is used to check whether a correction time for the forwarding of a message from the one route facility to an associated railway safety system must be set according to the measured speed variable.
  • a set correction time is then checked to determine whether it should remain effective as a function of at least one other influencing variable of the rail vehicle that determines the travel time.
  • axle counters can be used to recognize patterns of rail vehicles, to the effect that train types can be recognized on the basis of determined wheelbases.
  • a level crossing can also be operated with an optimized closing time if the train types are known, whereby the closing times for freight trains, for example, can be shifted to a later point in time.
  • the object of the invention is to detect characteristics of the train with sufficient certainty, if possible without additional sensors to be installed, so that a level crossing can be operated with an optimized closing time.
  • a method and a device suitable for using the method are to be specified.
  • the object of the invention consists in specifying a computer program product and a provision device for this computer program product, with which the aforementioned method can be carried out.
  • axle counter is used to determine the speed and acceleration of the rail vehicle, it is advantageously possible for the axle counter, which can also be designed as a double axle counter, to record several types of properties of the rail vehicle for the closing time of the level crossing are relevant can be used.
  • the axle counter alone is preferably used to determine measured values for all properties that are to be used to determine the closing time of the level crossing.
  • Another advantage is that the exact location of the axle counter is known and the measured values can therefore be assigned directly to this location.
  • the use of locating methods, from which parameters such as speed and acceleration can also be calculated, involves additional measurement uncertainties that can be avoided when carrying out the method according to the invention.
  • the rail vehicle can consist of individual vehicles or of a train, the latter occurring significantly more frequently in the approach area of level crossings.
  • Closing times within the meaning of the invention are understood to be the time at which the level crossing is closed.
  • the Closing times could thus also be referred to as the closing time.
  • there is talk of a closing period when it comes to the period in which the level crossing remains closed, starting with the closing time.
  • the opening time At the end of the closing period is the opening time, when the level crossing is opened again.
  • the great advantage of using the method for level crossings is that the closing times of the level crossing can advantageously be set individually depending on the properties of the approaching train. At least if the characteristics of the train can be reliably identified, in many cases the closing time can be postponed to a later point in time without lowering the safety standards in the operation of the level crossing.
  • the crossing traffic which in many cases does not have to wait as long at the level crossing, benefits.
  • “computer-aided” or “computer-implemented” can be understood to mean an implementation of the method in which at least one computer or processor executes at least one method step of the method.
  • Computers can be, for example, personal computers, servers, handheld computers, mobile phones and other communication devices that process computer-aided data, processors and other electronic devices for data processing, which can preferably also be combined to form a network.
  • a “processor” can be understood to mean, for example, a converter, a sensor for generating measurement signals, or an electronic circuit.
  • a processor can in particular be a main processor (Central Processing Unit, CPU), a microprocessor, a microcontroller, or a digital signal processor, possibly in combination with a memory unit for storing program instructions, etc.
  • CPU Central Processing Unit
  • a processor can also be understood to mean a virtualized processor or a soft CPU.
  • a “memory unit” can be understood to mean, for example, a computer-readable memory in the form of a random-access memory (RAM) or data memory (hard disk or data carrier).
  • RAM random-access memory
  • data memory hard disk or data carrier
  • the "interfaces" can be realized in terms of hardware, for example wired or as a radio connection, and/or software, for example as an interaction between individual program modules or program parts of one or more computer programs.
  • Cloud is to be understood as an environment for “cloud computing” (German computer cloud or data cloud). What is meant is an IT infrastructure that is made available via interfaces of a network such as the Internet. It usually includes storage space, computing power or software as a service, without these having to be installed on the local computer using the cloud.
  • the services offered as part of cloud computing cover the entire spectrum of information technology and include, among other things, infrastructure, platforms and software.
  • Program modules should be understood to mean individual functional units that enable the program flow according to the invention. These functional units can be implemented in a single computer program or in several computer programs that communicate with one another. The interfaces implemented here can be implemented in terms of software within a single processor or in terms of hardware if multiple processors are used.
  • the terms “create”, “determine”, “compute”, “generate”, “configure”, “modify” and the like preferably refer to processes that create and/or data change and/or convert the data into other data.
  • the data are available in particular as physical quantities, for example as electrical impulses or also as measured values.
  • the necessary instructions program commands are combined in a computer program as software.
  • the terms “send”, “receive”, “read in”, “read out”, “transmit” and the like relate to the interaction of individual hardware components and/or software components via interfaces.
  • a standard closing time is shifted at least once, in particular to a later closing time.
  • the advantage of using the standard closing time is that, on the one hand, safe operation of the level crossing can be ensured without exception and, on the other hand, the closing times can be flexibly adjusted if the characteristics of the approaching train can be determined with sufficient reliability.
  • the determined property of the speed is taken into account in this way that, taking into account the distance between the axle counter and the level crossing, it is determined whether the rail vehicle has reached the level crossing.
  • the arrival time of the rail vehicle at the level crossing can be calculated directly.
  • the closing time can be calculated directly. If a specific distance between the rail vehicle and the level crossing is required as a trigger criterion, the closing time can also be calculated from this. However, this calculation does not take into account any acceleration values determined by the axle counter (more on this below).
  • the determined property of the acceleration is taken into account in such a way that, taking into account the distance of the axle counter from the level crossing and the property of the acceleration, it is determined whether the rail vehicle has reached the level crossing.
  • the acceleration provides information on how the probable point in time when the rail vehicle passes the level crossing is influenced by the acceleration state of the train. A closing time that results from the sole consideration of the speed of the rail vehicle must therefore be corrected accordingly.
  • the most unfavorable case is that the rail vehicle is (further) accelerated until it reaches the level crossing. If the vehicle is accelerated negatively when leaving the axle counter (i.e. braked), this can also be taken into account, since this shifts the time of reaching the level crossing (compared to a constant speed) to a later point in time.
  • the most unfavorable case can be taken into account, that the operating state can change from braking to accelerating after exiting the axle counter.
  • passenger trains such as ICE or regional trains consist of fixed units that usually stay together, from which no cars are decoupled. Therefore, there are patterns that can be measured several times in a row by coupling these units and that are similar to each other. Passenger trains have a fixed "fingerprint", so to speak, which is only changed by measurement errors, etc.
  • Patterns are considered to be identical if all test criteria in the pattern comparison lead to the result that the test criteria match. Since the test criteria are based on measured values, a tolerance interval can be defined for the measurement, within which the test criterion can lie in order to be understood as identical.
  • Patterns are understood to be similar if an evaluation of the test criteria shows that they at least largely correspond to each other. It should be noted here that similarity also exists if the samples are identical.
  • the method according to the invention works in the technical sense. In operation, however, it must be determined where there is an optimum in terms of the strictness of the criteria in relation to safe operation.
  • a predominant part of the wheelbase sequence is present when the repeating pattern can be identified for more than 50% of the wheelbases. It can also preferably be defined that the limit at which a predominant part is assumed is to be more than 60%, particularly preferably more than 70%, 80% or 90%.
  • the advantage of using pattern recognition in train operation is that parameters of train operation, such as the closing time of the level crossing, can be flexibly adapted to the properties of the vehicles. In this way, for example, a greater route utilization can be achieved.
  • the maximum acceleration capability of the detected train type can be taken into account (see above for the consideration of speed and acceleration measurements). This means, for example, that a passenger train can accelerate faster than a freight train. If a freight train is detected, its lower acceleration capacity can be taken into account so that the level crossing can be closed later. This saves the crossing traffic unnecessary waiting times that would arise if closing times were calculated for the freight train in question, which take into account the high acceleration capacity of passenger trains.
  • a number of center distances at the beginning of the series and/or a number of center distances at the end of the series remain unconsidered.
  • the amount of the wheelbase is determined, with the rail vehicle being assigned the property of a passenger train as the first additional property, as long as the amount of the largest wheelbase occurring in the sample exceeds a specified limit value .
  • the limit value that allows reliable conclusions to be drawn about passenger trains also depends not least on the specifics of the respective train operation that is to be monitored. This limit value can thus be determined as a function of the route if it is known which passenger trains are running on the relevant route. It is important that the longest occurring wheelbase of the relevant towing vehicle is taken into account. However, in the event that train cars with different longest wheelbases are used, the shortest of the longest distances between the different train cars running on the route must be taken into account as a limit value.
  • this limit value can also differ from typical center distances of freight cars, so that the center distance can be used as a particularly reliable criterion for distinguishing between freight cars.
  • the center distance is used as an additional criterion for the patterns to be recognized (i.e. in addition), compliance with this difference is not obligatory.
  • the determined patterns of the wheelbases are compared with reference patterns of wheelbases and, in the event that the pattern matches a reference pattern, the rail vehicle is given a type of train linked to the reference pattern as a second additional one property is assigned.
  • the reference patterns can be stored in a memory device, for example.
  • a server can make the reference patterns available so that a comparison with the determined patterns is made possible.
  • the reference patterns are stored in a storage device that forms part of the axle counter. This creates the possibility of technically modifying the axle counters with a certain intelligence, in other words as autonomous or partially autonomous units.
  • the advantage of storing reference patterns in a storage device is that they are available at all times and can be called up without a time delay if required.
  • the memory devices can also store the various reference patterns in a route-specific manner, so that only specific reference patterns are made available to specific axle counters on specific route sections.
  • a parameter set suitable for the application is advantageously selected from a parameter set determined in this way. For example, one can omit the wheel diameter if it is more or less the same for all trains on the route.
  • Site-specific, representative data are now being collected or measured and classified for the relevant parameters, e.g. B. Passenger train, freight train. It is a finite number of integer or real values, e.g. For example, this could be the speed and the number of axles, to give a clear two-dimensional example here. i.e. in principle one obtains a classification task, as in the following figure 5 described.
  • the collection of further parameters in addition to the patterns to be compared makes the recognition of vehicle properties more robust against errors.
  • a higher degree of reliability can be achieved in the train detection, so that the train traffic can be controlled more effectively.
  • Which parameters are to be taken into account for a given control task for train traffic then depends on the circumstances of the individual case. They are to be selected in a suitable manner when designing the control process.
  • the criteria for the second test step and/or the further test steps are evaluated using a machine learning method.
  • Machine learning advantageously enables the ongoing processes to be optimized, i. H. the reliable detection of the properties mentioned, in particular train types, during operation. This allows the system to automatically adapt to changing operating conditions. For example, additional patterns can be created when a new type of passenger train is deployed on a particular route segment. For this purpose, e.g. B. neural networks or other devices with artificial intelligence.
  • artificial intelligence also abbreviated to KI below
  • machine learning also abbreviated to ML below
  • It is about the statistical learning of the parameterization of algorithms, preferably for complex applications.
  • ML the system recognizes and learns patterns and regularities in the recorded process data using previously entered learning data.
  • suitable algorithms ML can independently find solutions to emerging problems.
  • ML is broken down into three fields - supervised learning, unsupervised learning and reinforcement learning, with more specific applications such as regression and classification Structure recognition and prediction, data generation (sampling) or autonomous action.
  • supervised learning the system is trained through the relationship between the input and the associated output of known data, and in this way it learns approximately functional relationships. It depends on the availability of suitable and sufficient data, because if the system is trained with unsuitable (e.g. non-representative) data, it learns faulty functional relationships.
  • unsupervised learning the system is also trained with sample data, but only with input data and without any connection to a known output. It learns how data groups are to be formed and expanded, what is typical for the use case and where deviations or anomalies occur. This allows use cases to be described and error states to be discovered.
  • reinforcement learning the system learns through trial and error by proposing solutions to given problems and receiving a positive or negative evaluation of this suggestion via a feedback function. Depending on the reward mechanism, the AI system learns to perform corresponding functions.
  • ANN learns mainly by modifying the weights of the neurons.
  • An adaptation of the threshold value can be taken care of by an on-neuron.
  • ANN are able to learn complicated non-linear functions using a learning algorithm that attempts to determine all parameters of the function from existing input and desired output values by means of an iterative or recursive procedure.
  • ANN are a realization of the connectionist paradigm, since the function consists of many simple, similar parts. The behavior only becomes complex when they are added together.
  • probability densities for the properties are determined from the measurement data of a large number of measurements.
  • the probability densities makes it possible to define classification limits for the assignment of properties.
  • the method is advantageously very robust with regard to the classification limits, because with the comparatively low-dimensional problems according to the invention, the probability densities for the two classes can be estimated from the data (e.g. with density estimation of the measurement results) and thus also the error probabilities for an incorrect classification determine.
  • a data pool is used to determine the closing time, in which closing times are linked to the properties of the rail vehicles that can be determined, in particular types of train.
  • These closing times present in the data pool can in particular be used as standard closing times depending on the detected Type of train are used and modified according to the inventive method for the final determination of the closing time by this is shifted at least once, in particular to a later closing time.
  • the data pool can be determined deterministically and/or created and/or further developed using the machine learning methods already explained above during operation. As soon as the data is available in the data pool, it can advantageously be used with short access times. During operation, the data in the data pool can be further optimized so that train operations are increasingly streamlined.
  • a standard closing time for the level crossing is selected in the event that the property of the rail vehicle could not be determined.
  • the standard closing time for the level crossing should be understood to be that closing time which can reliably prevent any risk to crossing vehicle and passenger traffic, regardless of the properties of the trains running on the route.
  • Critical here are the slow-moving freight trains, which take the longest from the activation point of the track safety system to the level crossing and therefore require the longest closing time. This can thus be defined as the standard closing time.
  • a provision device for storing and/or providing the computer program product.
  • the provision device is, for example, a storage unit that stores and/or provides the computer program product.
  • the provision device is, for example, a network service, a computer system, a server system, in particular a distributed, for example cloud-based computer system and/or virtual computer system, which stores and/or provides the computer program product preferably in the form of a data stream.
  • the provision takes place in the form of a program data block as a file, in particular as a download file, or as a data stream, in particular as a download data stream, of the computer program product.
  • this provision can also be made, for example, as a partial download consisting of several parts.
  • Such a computer program product is read into a system, for example using the provision device, so that the method according to the invention is executed on a computer.
  • the described components of the embodiments each represent individual features of the invention to be considered independently of one another, which also develop the invention independently of one another and are therefore also to be regarded as part of the invention individually or in a combination other than the one shown. Furthermore, the components described can also be combined with the features of the invention described above.
  • FIG 1 a track system with a track GL, a control center LZ and a signal box SW is shown.
  • a vehicle FZ in the form of a train is driving towards a level crossing BU on the track GL.
  • a first axle counter AZ1 and a second axle counter AZ2 are installed on the track GL, which are set up in a manner known per se to count the axles of the vehicle FZ.
  • the use of two axle counters creates redundancy in order to increase the security (safety) of the method against failure.
  • the first axle counter AZ1 and the second axle counter AZ2 are so-called double axle counters, each with two axle counting sensors.
  • the axle counting sensors are arranged one after the other in the direction of travel of the trains so that they generate a measurement signal in quick succession.
  • This measurement signal can be used in a manner known per se to determine the direction of travel FR of the train and the speed v of the train.
  • the center distances L can also be determined from the speed v (also specified below with additional capital letters).
  • Shown in figure 1 is the wheelbase L of a bogie of the vehicle FZ shown. If the speed development from axis to axis of the crossing train is considered, an acceleration value a can also be derived from this consideration (more on this below).
  • the axle counter AZ1 is connected via a first interface S1 and the second axle counter AZ2 via a second interface S2 to the signal box SW, strictly speaking to a computer CP present in this signal box.
  • the computer CP has a third interface S3 for the level crossing BU.
  • the computer CP is connected to a storage unit SE via a sixth interface S6.
  • the signal box SW has a first antenna system A1, the control center LZ has a second antenna system A2 and the vehicle FZ has a third antenna system A3.
  • This enables both the signal box SW to communicate with the control center LZ via a fourth interface S4 and the vehicle FZ to communicate with the control center LZ via a fifth interface S5.
  • interface S5 is a radio interface.
  • the first interface S1, the second interface S2 and the third interface S3 can represent both wired and radio interfaces, with the antenna technology that would be required for forming radio interfaces not being shown for the latter case.
  • the axles of the vehicle FZ first pass the second axle counter AZ2 and then the first axle counter AZ1.
  • the measured values recorded can be transmitted to the computer CP via the first interface S1 and the second interface S2, the computer CP being set up to carry out the method according to the invention.
  • the computer CP can also take over the control of the level crossing BU directly. Another possibility is that the computer CP can be connected to another computer (in figure 1 not shown) is connected, which is used via a further interface to control the level crossing BU.
  • FIG 2 is one on the track GL as a vehicle FZ according to figure 1 moving passenger train PZ shown.
  • This passenger train PZ consists of a locomotive LK, several passenger cars PW and a power car TK at the end of the passenger train PZ opposite the locomotive LK.
  • center distances between the individual axles are shown schematically. It can be seen that in the passenger train PZ different wheelbases occur several times, so that the sequence of the wheelbases can be examined for the presence of patterns.
  • the center distances are marked with the capital letters A to G.
  • the sequence of center distances consists of FFEFFGABACABACABACADA.
  • FIG 4 shows how the method according to the invention can take place.
  • this is started in a first step START.
  • the closing time of the level crossing BU is set to the value of a standard closing time SZS.
  • a separate memory area is reserved for this in the memory device, to which a controller (e.g. the computer CP or a Figures 1 to 3 further computer not shown) of the level crossing can access to call up the currently stored closing time.
  • a measuring step MS follows by the relevant axle counters AZ1, AZ2 (cf. figure 1 ).
  • the axle counting sensors of the two axle counters are used to determine, among other things, the speed of the passing wheels (representing the axle), distances between the axles, the speed of the axles and the speed development from axle to axle, and thus the acceleration, with the Acceleration can be constant or can undergo an evolution.
  • This measurement step is followed by a first test step PS1, this first test step consists of an evaluation step for the speed ELVv and an evaluation step for the Acceleration ELVa, in which the effects of the speed v of the train and the acceleration state a of the train during the crossing via the axle counters are taken into account.
  • the standard closing time SZS which takes into account the worst case, can be corrected and a later closing time SZ1 can be output to the storage device SE.
  • the closing time as the second closing time SZ2 will in most cases have to be corrected to earlier closing times.
  • the braking effect must first be canceled before a positive acceleration can take place. This can also be taken into account by specifying a later closing time, which is output to the storage device SE as the second closing time SZ2.
  • a second test step PS2 in which the sequence of the center distances (as in Figure 2 and Figure 3 described) can be determined and checked. It is either possible to recognize patterns MT in the sequence of the center distances or not.
  • GZ a subsequent query step GZ, PZ? It is checked whether the sequence of wheelbases (by finding samples) can be used to conclude that it is a GZ freight train or a passenger train. If this is not the case, the second closing time SZ2 remains.
  • a further query step GZ? follows in the computer CP, whether it is is a freight train GZ. If this is the case, a third modified closing time SZ3 is transferred to the storage unit SE (replaces a previously stored closing time). If it is not a freight train or if there is no clear result, a second test step PS2 is carried out in the computer CP.
  • the third test step PS3 is used to determine the amounts of the center distances.
  • ⁇ GW can therefore be asked whether the determined amounts of the axle distances are smaller than a typical limit value for freight wagons GW. If this is the case, it is a freight train GZ, so that the third modified closing time SZ3 can be transferred to the storage unit SE (replaces a previously stored closing time). If this is not the case, a fourth test step PS4 is initiated in the computer CP.
  • reference patterns RMT are loaded from the memory unit SE.
  • the aforementioned modified closing times SZ3, SZ4, SZ5, SZ6 are closing times that are calculated taking into account the closing times determined in the first test step and are modified in this sense.
  • the insights gained in the first test step with regard to the speed and acceleration of the approaching train are also included in the modified closing times, in which the type of train is also taken into account.
  • they are refinements typical of a train, each one enable more and more precise determination of the closing time, whereby the safety (safety) of the operation of the level crossing is not impaired due to the additional knowledge.
  • the determined new pattern MT can also be transferred to the control center LZ via the interface S4.
  • driving data FD from the vehicle FZ can also be transferred to the control center LZ via the fifth interface S5.
  • a new sixth modified closing time SZ6 adapted to the determined train type can then be determined in a modification step MOD, and transmitted to the storage unit SE via an output step OUT.
  • This sixth modified closing time SZ6 can then be used as an individual closing time for the level crossing BU (replaces a previously saved closing time).
  • an output can be made in the memory unit SE such that the sixth modified closing time SZ6 is written in the memory unit SE as a supplement to the database together with the newly determined reference pattern RMT, which belongs to the vehicle FZ just analyzed.
  • this can be the standard closing time SZS, the first closing time SZ1, the second closing time SZ2, the third modified closing time SZ3 or the fourth modified closing time SZ4, the fifth modified closing time SZ5, the sixth modified closing time SZ6 (or further closing times, which are not according to the example figure 4 are described) act.
  • This closing time is now available in the separate memory area of the memory device SE in order to be able to be activated by controlling the level crossing BU (cf. figure 1 ), So the computer CP or another controller of the level crossing BU (not shown) to be handed over.
  • the level crossing BU can be operated with an individually determined closing time.
  • FIG 5 two parameters measured or determinable by the axle counter according to the invention are shown abstractly in a plane which could also be referred to as the xy plane and on which the measured value distribution MV of the measured values becomes clear. Accordingly, the speed GSW would be shown on the x-axis and the center distances A...H on the y-axis.
  • the z-axis is used to represent the (e.g. estimated) probability densities
  • site-specific, representative data is collected or measured and classified for the relevant parameters, e.g. B. Passenger train as normal distribution NV2 and freight train as normal distribution NV1, as already described above. It is a finite number of integer or real-valued measurement data from the axle counters, e.g. For example, this could be the speed and the center distance, to give a clear two-dimensional example here. That is, in principle one obtains a classification task as in figure 5 shown schematically.
  • the small ellipse is the first classification limit KG1 for freight trains and the large ellipse is the classification limit KG2 for passenger trains, then you could use the estimated distributions to determine the error probabilities calculate. If the misclassification probability for freight trains were too high, the classification limits would be changed. In the example according to figure 5 a smaller ellipse would then be obtained for the first classification limit KG1.
  • the classification errors are asymmetrical, ie the errors do not have the same meaning.
  • figure 6 shows the distance traveled by trains in a distance-time diagram.
  • the time t is shown on the x-axis and the distance traveled by the trains s on the y-axis.
  • the position of the first axle counter AZ1 and the level crossing BU is also shown on the y-axis, because the implementation of the method is about covering this stretch and estimating the time required for this.
  • the curves K1 to K4 show different profiles of a route progression of a train under consideration. Closing times SZS and SZ1 to SZ4 are calculated using these curves. These are plotted in the path-time diagram and have in the embodiment according to figure 6 always a constant time interval ZA to the planned arrival time of the vehicle at the level crossing BU. The arrival time is represented by dash-dotted vertical lines that intersect the x-axis at the respective arrival times.
  • the zero point of the time axis is at the point in time at which the rail vehicle leaves the relevant axle counter AZ1. Up to this point in time, the speed and acceleration were recorded during the crossing based on the axle count events, so that curve K0 is a real measured curve. Without taking this curve into account, the standard closing time SZS is provided, which is the worst-case scenario for the approach of the rail vehicle to the level crossing. This is the constant for the maximum speed permitted on the stretch of road, which is why curve K1 in the distance-time diagram is linear.
  • Curve K3 occurs when a freight train is detected in the second test step PS2 (cf. figure 4 ). This can only, as in figure 6 is shown, accelerate much more slowly, which is why the closing time SZ3 can be shifted to later points in time due to the third curve K3 compared to the second curve K2.
  • test step PS4 a comparison with a reference model RMT can be carried out in test step PS4 in order to determine the train type more precisely.
  • the fourth curve K4 can be assigned to it if it turns out that its acceleration capacity does not correspond to that of the fastest train running on the route (cf. curve K2).
  • the closing point in time SZ4 can also be shifted to a later point in time when the fourth curve K4 is taken into account.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
EP21198362.2A 2021-09-22 2021-09-22 Procédé et dispositif pourvu de compteur d'essieux permettant de faire fonctionner un passage à niveau Pending EP4155162A1 (fr)

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EP21198362.2A EP4155162A1 (fr) 2021-09-22 2021-09-22 Procédé et dispositif pourvu de compteur d'essieux permettant de faire fonctionner un passage à niveau
US17/950,278 US12454296B2 (en) 2021-09-22 2022-09-22 Method and apparatus with an axle counter for operating a railroad crossing, computer program product and delivery apparatus for the computer program product

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DE102024204664A1 (de) * 2024-05-21 2025-11-27 Siemens Mobility GmbH Einstufiges Auswerten eines Achszähler-Zeitverlaufs

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DE102024204664A1 (de) * 2024-05-21 2025-11-27 Siemens Mobility GmbH Einstufiges Auswerten eines Achszähler-Zeitverlaufs

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