SYSTEM AND METHOD FOR TRAFFIC CONTROL IN RAILWAYS

Abstract

The present invention relates to a system and method for traffic control in railways. The present invention also relates to a corresponding use. The present invention is particularly directed to the computation and prediction of optimal routes for a plurality of trains, and the optimization of train tracks capacity. The method comprises controlling traffic in railways, the method comprising sampling sensor data relevant to railway system via at least one sensor (200) and using at least one server (500) for receiving the sensor data from the at least one sensor (200), predicting the status of the railway infrastructure based on future rolling stock; and controlling the traffic of rolling stock on the basis of the future status.

Claims

1. A method for controlling traffic in railways, the method comprising sampling sensor data relevant to railway system via at least one sensor; using at least one server for receiving the sensor data from the at least one sensor; predicting the status of the railway infrastructure based on future rolling stock; and controlling the traffic of rolling stock on the basis of the future status.

2. The method according to claim 1 further comprising processing sensor data to generate processed sensor data; analyzing the processed sensor data to obtain information relevant to railway system; using the obtained relevant information for planning the routing of rolling stocks; and transmitting the route planning to at least one of the at least one server and/or at least one authorized user.

3. The method according to claim 2 wherein routing of rolling stocks is based on at least one of the following current or predicted relevant information of railways: technical condition of assets; degrading effect of rolling stocks; degrading effect of assets; traffic load information of rolling stocks; risks of traffic delay; unplanned maintenance and/or inspections; planned maintenance and/or inspections; maintenance effectiveness metrics; and weather information.

4. The method according to claim 2 wherein the analysis of relevant information obtained from the sensor data and/or the routing of rolling stocks are based on at least one analytical approach.

5. The method according to claim 4 wherein the method further comprises associating and/or arranging at least one sensor with at least one of rolling stock and/or railway infrastructure.

6. The method according to claim 5 wherein the method comprises using the at least one server providing at least one signal comprising parameters to define the route of rolling stocks; prediction of railway traffic; prediction of wear effect of rolling stocks on the railway infrastructure; contrast of the route planning with current traffic in railways; provision of feedback of current traffic in railways; provision of instruction for (semi) automatically controlling the traffic in railways; provision of instruction for (semi) automatically routing rolling stocks in railways; and wherein the at least one signal is based on at least one analytical approach.

7. A system for controlling traffic in railways, the system comprising at least one sensor configured to sample sensor data relevant to railway system; at least one server configured to receive the sensor data from the sensor; predict the future status of the railway infrastructure based on future rolling stock; and control the traffic of rolling stock on the basis of the future status.

8. The system according to the claim 7, the system further comprising at least one sensor data processing component configured to generate processed sensor data; at least one analyzing component configured to analyze the processed sensor data to generate a rolling stock routing plan; at least one transmitting component configured to transmit the route planning to at least one server and/or at least one authorized user through an interface.

9. The method according to claim 9 wherein the at least one analyzing component (400) generates a rolling stock routing plan based on at least one of the following current or predicted relevant information of railways: technical condition of assets; degrading effect of rolling stocks; degrading effect of assets; traffic load information of rolling stocks; risks of traffic delay; unplanned maintenance and/or inspections; planned maintenance and/or inspections; maintenance effectiveness metrics; and weather information.

10. The system according to claim 8 wherein the at least one analyzing component and/or the at least one server comprises optimizing the routing of rolling stocks based on at least one analytical approach.

11. The system according to claim 10 further comprising the association and/or arrangement at least one sensor with at least one of rolling stock and/or railway infrastructure.

12. The system according to claim 11 wherein the information sampled via at least one sensor provide information of at least one sensor data measurements.

13. The system according to claim 8 wherein the at least one server is configured to provide at least one signal comprising parameters to define the route of rolling stocks; monitoring of traffic of rolling stocks considering the future status of railway infrastructure; prediction of railway traffic considering the future status of railway infrastructure; prediction of wear effect of rolling stocks on the railway infrastructure; and wherein the at least one signal is based on at least one analytical.

14. The system according to claim 11 wherein the at least one sensor is configured to perform in a plurality of operation modes, and wherein the operation modes can be configured to monitor a plurality of sensor data relevant to railway system.

15. The system according to claim 8 wherein the server comprises an interface component configured to bidirectionally communicate the at least one server with at least one authorized user.

Description

[0122] The present invention will now be described with reference to the accompanying drawings which illustrate embodiments of the invention. These embodiments should only exemplify, but not limit, the present invention.

[0123] FIG. 1 depicts a schematic example of a set-up of a plurality of sensors to a railway infrastructure in accordance with the present invention;

[0124] FIG. 2 depicts a schematic of system for controlling traffic in railways according to embodiments of the present invention;

[0125] FIG. 3 depicts an exemplary application of the traffic control system according to embodiments of the present invention;

[0126] It is noted that not all the drawings carry all the reference sings. Instead, in some of the drawings, some of the reference sings have been omitted for sake of the brevity and simplicity of illustration. Embodiments of the present invention will now be described with reference to the accompanying drawings.

[0127] FIG. 1 schematically depicts a description of a system configured for a railway infrastructure. In simple terms, the system may comprise a railway section with the railway 1 itself, comprising rails 2 and sleepers 3. Instead of the sleepers 3 also a solid bed for the rails 2 can be provided.

[0128] Moreover, a further example of constitutional elements is conceptually represented a mast, conceptually identified by reference numeral 4. Such constitutional elements are usually arranged at or in the vicinity of railways. Furthermore, a tunnel is shown, conceptually identified by reference numeral 5. It is needless to say that other constructions, buildings etc. may be present and also used for the present invention as described before and below.

[0129] For instance, a first sensor 10 can be arranged on one or more of the sleepers. The sensor 10 can be an acceleration sensor and/or any other kind of railway specific sensor. Examples have been mentioned before.

[0130] Further, a second sensor 11 can also arranged on another sleeper distant from the first sensor 10. Although it seems just a small distance in the present example, those distances can range from the distance to the neighboring sleeper to one or more kilometers. Other sensors can be used for attachment to the sleepers as well. The sensors can further be of different kind—such as where the first sensor 10 may be an acceleration sensor, the second sensor 11 can be a magnetic sensor or any other combination suitable for the specific need. The variety of sensors are enumerated before.

[0131] Another sensor 20, which may be different or the same kind of sensor, can be attached, for example, to the mast 4 or any other structure. This may be a different kind of sensor, such as, for example, an optical, temperature, even acceleration sensor, etc. A further kind of sensor, for example sensor 30, can be arranged above the railway as at the beginning or within the tunnel 5. This could, for example, be a height sensor for determining the height of a train, an optical sensor, a doppler sensor etc. It will be understood that all those sensors mentioned here and/or before are just non-limiting examples.

[0132] Furthermore, the sensors can be configured to submit the sensor data via a communication network, such as a wireless communication network. As the communication network bears several advantages and disadvantages regarding availability, transmittal distance, costs etc. the transmittal of sensor data is optimized as described herein before and below. FIG. 2 schematically depicts a system 100 for controlling traffic in railways. The system 100 may comprise at least one data gathering component, identified with reference numeral 200. It will be understood that the data gathering component 200 may comprise a plurality of sensors, a sensor system or a plurality of sensor systems. Therefore, the gathering data component 200 may also be referred to as plurality of sensors 200, plurality of sensor systems 200, sensor system 200, sensors 200 or simply as sensor 200. The sensors 200 may be configured to sample information relevant to the traffic in railways, for instance, the vibration due to a rolling stock passing through a given track.

[0133] Furthermore, the system 100 may also comprise a processing component 300. The processing component 300 may comprise a standalone component configured to receive information from the sensors 200. In simple words, the processing component 300 may assume a configuration that allows it bidirectionally communicating with the sensor 200.

[0134] In one embodiment, the processing component 300 may also be integrated with at least one of the sensors 200. In order words, the processing component 300 may also comprise an imbedded module of the sensors 200.

[0135] In one embodiment of the present invention the processing component may communicate with an analyzing component, conceptually identified by reference numeral 400. The analyzing component 400 may be configured to process sensor data based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0136] Moreover, the analyzing component may communicate with a data transmitting component, conceptually identified with reference numeral 800. The data transmitting component 800 may comprise one or more modules configured to receive information from the analyzing component 400 and further send the received the information to a server, conceptually identified by reference numeral 500. The data transmitting component 800 may also be referred to as transmitter 800.

[0137] In another embodiment of the presentation invention, the sensor 200, the processing component 300, the analyzing component 400 and the data transmitting component 800 may comprise an integrated module configured to execute subsequently the tasks corresponding to each individual component. In simple words, in one embodiment the sensor 200, the processing component 300, the analyzing component 400 and the transmitter 800 may comprises modules of a single component.

[0138] The data transmitting component 800 may be configured to establish a bidirectional communication with the server 500. In other words, the server 500 may retrieve information from the data transmitting component 800, and further may provide information to the transmitter 800, for example, operation parameters. It will be understood that each component may receive a plurality of operation parameters, for instance, the processing component 300 may be commanded to execute a preprocessing of the data received from the sensors 200. Alternatively or additionally, the processing component 200 may be instructed to transmit the original data received from the sensors 200, i.e. the data coming from the sensors 200 can be transferred directly to the next component without executing any further task. It will be understood that the component may also be configured to perform a plurality of tasks at the same time, e.g. processing the data coming from the sensor 200 before transferring to the next component and transferring the data coming from the sensors 200 without any processing.

[0139] In one embodiment, the server 500 may comprise a cloud server, a remote server and/or a collection of different type of servers. Therefore, the server 500 may also be referred to as cloud server 500, remote server 500, or simple as servers 500. In another embodiment, the servers 500 may also converge in a central server.

[0140] It will be understood that the server 500 may also be in bidirectional communication with a storing component and an interface component, conceptually identified by reference numerals 600 and 700, respectively.

[0141] The storing component 600 may be configured to receive information from the server 500 for storage. In simple words, the storing component 600 may store information provided by the servers 500. The information provided by the server 500 may include, for example, but not limited to, data obtained by sensors 200, data processed by the processing component 300 and any additional data generated in the servers 500. It will be understood that the servers 500 may be granted access to the storing component 600 comprising, inter alia, the following permissions, reading the data allocated in the storing component 600, writing and overwriting the data stored in the storing component 600, control and modify the storage logic and the data distribution within the storing component 600.

[0142] In one embodiment of the present invention the server 500 may be configured transmit a signal to other component of the railway system based upon traffic information retrieved from sensors 200. For instance, a giving traffic data is provided by the server 500 and subsequently the server 500 generates a signal containing instructions, which are transmitted to the railway system for implementation. The set of instructions may comprise, inter alia, train switching from on track to another to allow another train to continue its route. Furthermore, the signal may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0143] The interface component 700 may comprise a bidirectionally communicated component configured to exchange information with the servers 500. In one embodiment, the interface component 700 may comprise a plurality of software interfaces with different levels, for instance, it may comprise the front end of a dedicated software running, controlling and/or improving railway traffic. In another embodiment, the interface component 700 may also comprise a physical terminal for providing access to the servers 500 to an authorized user. Furthermore, the interface component 700 may be configured to facilitate providing instructions to the server 500 and/or for requesting information from the server 500, such as, for example, a traffic data obtained by the sensor 200. Such requests and/or information set may be referred to as query.

[0144] The system 100 may be applied to control the traffic in a railway network. For instance, a railway network may consist of a plurality of tracks, which may also be referred to as permanent way. FIG. 3 depicts an example of a section 1000 of a railway network, on which trains A and B may be circulating through tracks, for example, 1 and 2, which may also be connected through a switch 3. The connecting switch 3 may assume a configuration that allow a passage from one track to any other track in the section of the network, for instance, a passage through the connecting switch 3 from track 1 to track 2 and/or vice versa. The activation of the switch 3 may be controlled by the server 500, which may provide operation instructions based on the traffic data obtained from the sensors 200.

[0145] In one embodiment, the sensors 200 may, inter alia, adopt a configuration that allows identifying trains, their speeds and their wear effect on the tracks. The data gathered by the sensors 200 may constitute the basis for the server 500 to generate instructions for the activation of the switches. In simple words, if a train is approaching this part of the network, the sensors 200 may retrieve data that may allow activating the switches in order to redirect the trains, for example, from track 1 to track 2, according to their speed and/or wear effect. The data gathered by the sensors 200 may be communicated to the server 500, which may subsequently transmit the information and the corresponding instructions to the nearest assets, for example, the nearest switch, which may consequently be activated to control the traffic on the tracks. Furthermore, in one embodiment of the present invention, the system 100 may calculate the wear effect of a particular approaching rolling stock on an individual switch of a given section of the network based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0146] In another embodiment of the present invention, the system 100 may determine that a particular rolling stock, for example train A, may have a higher wear effect on an already more worn switches 1.4 and 2.4 of passage 3 and therefore may reroute the rolling stock, for example, through switches 1.5 and 2.5 of passage 4. Furthermore, the system 100 ensures that the trajectory of another rolling stock, for example train B, is not affected.

[0147] In one embodiment of the present invention, the system 100 may also determine that a particular rolling stock may be less wearing if passing through a track with a certain speed, for example, passing through track 2, while other similar train type usually runs through track 1. This approach may be advantageous, as it may allow to reduce wear of track and/or switch by evaluating and selecting the optimal route for the trains based on their punctual circulation properties. Furthermore, the system 100 may predict a future status of the railway network and based on that may determine an optimal routing of rolling stocks using data analysis based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0148] For instance, when a rolling stock, for example train A, is approaching a switch, for example switch 1.4, which has been recently maintained, and on the other hand, another switch, for example switch 1.5, is closer to reach its maintenance cycle, the system 100 may determine to keep train A on track 1 until the rolling stock reaches the switch with the closer maintenance cycle to be execute. Subsequently, the system 100 may reroute the train A to track 2 through switch 1.5, instead of switch 1.4. Such an approach may be advantageous, as it may allow to maximize the life cycle of assets. In other words, it may allow the optimal use of railways considering the assets' health status, maintenance and/or inspections plans.

[0149] Furthermore, the system 100 may also be able of determining which routes must be kept to ensure the optimal performance of tracks and the traffic of railways. In more simple words, the system 100 may be capable to identify, based on the current condition of the switch engine, for example, of switch 2.4, if the conditions are optimal for retaining the position of the switch, i.e. if the conditions are the best to not move the switch. As a result, the system 100 may be able to determine if the routes of all coming rolling stocks through a section of the network, for example, switch 2.4, should either be kept in one a particular position. Additionally, the system 100 may be able to identify how long the routes must be kept based on the future conditions, i.e. the system 100 may maintain the route of a rolling stock unaltered as long as the conditions relevant to the railway (e.g. wear effect, speed) guarantees the optimal routing for a traffic status, or if the conditions makes it necessary the routes to be kept unchanged.

[0150] In more simple words, determinations of the system 100 may directly be used to control the traffic in railways as well as taking into consideration other rules of traffic control, such as, for example, but not limited to, stops at stations, speed limits and safety regulations. Additionally, the determinations of the system 100 may also be communicated to a common traffic control system, which may further take the data into consideration when controlling the traffic in a plurality of railway systems. Such a route planning may take into accounting past and current information relevant to railway systems, and the analysis for predicting future status may be based on at least one analytical approach, each approach comprising at least one of signal filter processing, pattern recognition, probabilistic modeling, Bayesian schemes, machine learning, supervised learning, unsupervised learning, reinforcement learning, statistical analytics, statistical models, principle component analysis, independent component analysis (ICA), dynamic time warping, maximum likelihood estimates, modeling, estimating, neural network, convolutional network, deep convolutional network, deep learning, ultra-deep learning, genetic algorithms, Markov models, and/or hidden Markov models.

[0151] While in the above, a preferred embodiment has been described with reference to the accompanying drawings, the skilled person will understand that this embodiment was provided for illustrative purpose only and should by no means be construed to limit the scope of the present invention, which is defined by the claims.

[0152] Whenever a relative term, such as “about”, “substantially” or “approximately” is used in this specification, such a term should also be construed to also include the exact term. That is, e.g., “substantially straight” should be construed to also include “(exactly) straight”.

[0153] Whenever steps were recited in the above or also in the appended claims, it should be noted that the order in which the steps are recited in this text may be accidental. That is, unless otherwise specified or unless clear to the skilled person, the order in which steps are recited may be accidental. That is, when the present document states, e.g., that a method comprises steps (A) and (B), this does not necessarily mean that step (A) precedes step (B), but it is also possible that step (A) is performed (at least partly) simultaneously with step (B) or that step (B) precedes step (A). Furthermore, when a step (X) is said to precede another step (Z), this does not imply that there is no step between steps (X) and (Z). That is, step (X) preceding step (Z) encompasses the situation that step (X) is performed directly before step (Z), but also the situation that (X) is performed before one or more steps (Y1), . . . , followed by step (Z). Corresponding considerations apply when terms like “after” or “before” are used.