Patent Application
20210122402
The invention lies in the field of traffic control. The invention is applicable in the field of traffic control in railways and therefore relates to a system and method for traffic control in railways. The invention is particularly directed to the computation and prediction of optimal routes for a plurality of trains. The invention further relates to the optimization of train tracks capacity.
Railroad, railway or rail transport has been developed for transferring goods and passengers on wheeled vehicles on rails, also known as tracks. In contrast to road transport, where vehicles run on a prepared flat surface, rail vehicles (rolling stock) are directionally guided by the tracks on which they run. Tracks commonly consist of steel rails, installed on ties or sleepers and ballast, on which the rolling stock, usually provided with metal wheels, moves. Other variations are also possible, such as slab track, where the rails are fastened to a concrete foundation resting on a subsurface.
Rolling stock in a rail transport system generally encounters lower frictional resistance than road vehicles, so passenger and freight cars (carriages and wagons) can be coupled into longer trains. Power is provided by locomotives, which either draw electric power from a railway electrification system or produce their own power, usually by diesel engines. Most tracks are accompanied by a signaling system. Railways are a safe land transport system when compared to other forms of transport. Additionally, railways are capable of high levels of passenger and cargo utilization and energy efficiency but are often less flexible and more capital-intensive than road transport, when lower traffic levels are considered.
The inspection of railway equipment is essential for the safe movement of trains. Many types of defect detectors are in use today. These devices utilize technologies that vary from a simplistic paddle and switch to infrared and laser scanning, and even ultrasonic audio analysis. Their use has avoided many rail accidents over the past decades.
Another essential aspect for the safe movement of trains in the control of traffic in railways. A plurality of factors and parameters play a role in the occurrence of different scenarios in railways, there the traffic in railways must keep up with intensive supervision in order to minimize incents, which may result in enormous economically and human loss. Additionally, a poor traffic control may also aggravate the effect of infrastructure failures on the traffic in railways and it may, for example, disrupt freight revenue operations and passenger services.
Unlike highways or road networks where capacity is disaggregated into unlinked trips over individual route segments, railway capacity is fundamentally considered a network system. As a result, many components can cause system disruptions. Traffic control of railways must acknowledge the vast array of a route's performance such as, for example, type of train service, number of tracks, types of train control, trains speeds, wear effect on tracks, pick hours in the railway network and priority of circulation.
Railway operations require careful monitoring and control of the traffic to ensure passenger safety and reliable service. Many sensors are used to monitor and obtain data from the traffic in railways, which may be used to ensure the integrity of the service and identify possible sources of malfunction. Such sensors allow for data collection and analysis and ensure safer traffic of railways. Various sensors can be placed directly on trains, on tracks or nearby, at train stations and/or on platforms, and generally in the overall vicinity of the railway. Measurements of such sensors may be used to further measurements, control, prediction and optimization of traffic control in railways. Several different attempts have been made to implement systems and methods for train traffic control.
For instance, U.S. Pat. No. 6,179,252 B1 discloses an invention related to an intelligent intersection control system that features an internal controller that receives digital messages containing detailed information items concerning, for example, the direction, speed, length and identity of a train. The controller generates appropriate commands that coordinate the functions of crossing safety devices. A controller is capable of receiving and using much more detailed train information than is possible with conventional warning systems. Railroad crossing warning features are capable of responding more flexibly to this more detailed train information. The controller also continuously adjusts the activation state for safety devices associated with the crossing. In particular embodiments, the control system provides and displays crossing status information including the amount of time remaining until a crossing is cleared of train traffic, the approach of a second train during blocking of the crossing by a first train, or a suggested alternate route for waiting road vehicles. The controller may also be used to actuate numerous standard crossing warning features, including crossing blocking arms, flashing lights, warning chimes and warning horns.
Furthermore, the U.S. Pat. No. 5,950,966 A discloses a system for controlling train movement that uses a distributed architecture. Wayside controllers receive signals from individual trains, including position information derived from a navigation system. The wayside controllers interface with a central train control network and coordinate local train movement including the issuance of incremental authorities.
Moreover, there have been inventions proposition an automatic vehicle control and location system. For example, U.S. Pat. No. 5,364,047 A discloses a signaling and traffic control system, which is capable of a vehicle determining its own absolute position along a guideway based on information received from the wayside using an inductive loop or beacon system in conjunction with the distance traveled according to the onboard tach generator(s), and report its position to a wayside control device, whereby the wayside control device reports to the vehicle, as part of its communications message, the location of the closest forward obstacle. Based upon this information, the vehicle controls itself safely based upon its characteristics as contained in a topographical database and a vehicle database. This train control system is a train-oriented block system (i.e., moving block). The system requires vital two-way data communication between the wayside and the vehicle, and between adjacent control sectors.
Additionally, U.S. Pat. No. 5,332,180 A discloses a railway traffic control system in which accurate vehicle information is effectively available in real-time to facilitate control of traffic flow. Unlike prior art methods of precisely monitoring train location, the current invention is dependent only on equipment onboard the vehicle and position updates provided by external benchmarks located along the track route. The system's dynamic motion capabilities can also be used to sense and store track rail signatures, as a function of rail distance, which can be routinely analyzed to assist in determining rail and road-bed conditions for preventative maintenance purposes. In presently preferred embodiments, the on-board vehicle information detection equipment comprises an inertial measurement unit providing dynamic vehicle motion information to a position processor. Depending on the amount and quality of a priori knowledge of the vehicle route, the inertial measurement unit may have as many as three gyroscopes and three accelerometers or as little as a single accelerometer. To minimize error between benchmarks, the processor preferably includes a recursive estimation filter to combine the a priori route information with movement attributes derived from the inertial measurement unit.
In light of the above, it is therefore an object of the present invention to overcome or at least to alleviated the shortcomings and disadvantages of the prior art. More particularly, it is an object of the present invention to provide a system and method for traffic control in railways. It is also a preferred object of the present invention to disclose a controlling, monitoring and processing system for sensor data relevant to traffic in a plurality of railways.
These objects are met by the present invention.
The present invention relates to a method and a system for controlling traffic in railways. In a first embodiment the method may comprise sampling sensor data relevant to railway system via at least one sensor.
The term sensor is intended to comprise at least one device, module, model and/or subsystem whose purpose is to detect parameters and/or changes in its environment and provides a respective signal to other devices. It will be understood that the term “different kind of sensor” is intended to mean sensors that are configured to measure different parameters or the same parameters with different technologies. An example for the latter is lasers or induction loops, both provided to measure speed.
Furthermore, the term railway system is intended to comprise railway infrastructure and rolling stocks. The term railway infrastructure is intended to comprise railway tracks, trackage, permanent ways, electrification systems, sleepers or crossties, tracks, rails, rail-based suspension railways, switches, frogs, point machines, crossings, interlockings, turnouts, masts, signaling equipment, electronic housings, buildings, tunnels, railway, stations and/or informational and computational network. It will also be understood that it basically comprises any position on a railway network where a sensor may be placed, and which may allow sampling sensor data containing information that may be directly or indirectly relevant to the railway traffic. Furthermore, the term rolling stock is intended to comprise any vehicle(s) moving on a railway, wheeled vehicles, powered and unpowered vehicles, such as for example, locomotives, railroad cars, coaches, wagons, construction site vehicles, draisines and/or trolleys.
The invention may further provide using at least one server which may be configured 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. It will be understood that the prediction and/or predicting is intended to mean predictive analytics that encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. It will also be understood that the term server may also refer to a computer program, and/or a device, and/or a plurality of each or both that may provide functionality for other programs, devices and/or components of the present invention. For instance, a server may provide various functionalities, which may be referred to as services, such as, for example, sharing data or resources among multiple clients, or performing computation and/or storage functions. It will further be understood that a single server may serve multiple clients, and a single client may use multiple servers. Furthermore, a client process may run on the same device or may connect over a network to a server on a different device, such as a remote server or a cloud. The server may have rather primitive functions, such as just transmitting rather short information to another level of infrastructure, or can have a more sophisticated structure, such as a storing, processing and transmitting unit.
In one embodiment of the present invention, the method may further comprise 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 server and/or at least one authorized user. The routing of rolling stocks may be based on at least one of the following current or predicted relevant information of railways: technical condition of assets (i.e. technical condition of railway infrastructure, which also may comprise the effect caused by the infrastructure on the rolling stocks), degrading effect of rolling stocks (i.e. the effect caused by the rolling stock on the infrastructure), traffic load information of rolling stocks, risks of traffic delay, unplanned and/or planned maintenance and/or inspections, maintenance effectiveness metrics; and weather information.
Furthermore, the method may comprise subjecting the relevant information, obtained from the sensor data, to analysis via 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.
Moreover, the method routing the rolling stocks is 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. It will be understood that this is not an exhaustive list, and it will also be understood that that, for example, a first analytical approach may differ from, for example, a second analytical approach. Further, it will be understood that the term analytical approach is intended to comprise any analytical tool that is used to analyze signals or data, and it may also be referred to as analytical method. These analytical methods can be applied alone or any combination thereof, sequentially and/or in parallel. Different analytical approaches can thus be different in the kind of one or more analytical method(s) and/or just the order of a plurality of analytical methods when even using the same methods but just in a different order.
In one embodiment of the present invention, the method may further comprise associating and/or arranging at least one sensor with at least one of rolling stock and/or railway infrastructure. It will be understood that this is not an exhaustive list of objects the sensors may be releasable or fixedly assembled to. It will be also understood that rail infrastructure may comprise a switch component, which may, inter alia, but limited to, comprise at least one of a sleeper, a frog, a point machine, switch frog, switch blade and/or an interlocking for particularly measuring point machine current at the interlocking.
The method may also comprise using the server for 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. Additionally or alternatively, the method may base at least one signal 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. Furthermore, the signal may be transmitted via wirelessly and/or hard-wired means. Moreover, the invention may provide the further step of associating, for example, a first and a second sensor data to obtain relevant information to railway traffic, which may be advantageous, as it may allow to determine if first sensor data has a directly or indirectly influence or has an impact to the second analytical data and/or vice versa.
The method may further comprise at least one of: contrasting the route planning with the current traffic in railways, providing and receiving feedback of current traffic in railways, and/or providing instruction for controlling the traffic in railways. Additionally or alternatively, the method may comprise the step of (semi)automated controlling routing of rolling stocks according to their wear effect on the railways. It will be understood that whenever the term (semi)automated is used, the automation of the step, process, and/or is a preferred, for example, the method may comprise the step of (semi)automated controlling routing of rolling stocks, where the method may comprise the step of preferably automated controlling routing of rolling stocks.
For instance, the invention may predict the future status of the traffic in railways by evaluating the health condition of any asset involved. For this, multiple sources to derive a health status that reflects the actual usage of the asset may be used. As an example, the stress and, hence, the wear of the frog (crossing point of two rails) is mainly the result of trains running over it and the temperature changes over time. The invention uses the continuously recorded and combined data to derive the stress and to accumulate it over time. In contrast to the state of the art, this stress is calculated taking into account the train type, speed, vibration power, temperature, direction of travel of each passing train which gives a reflects the wear much more accurate than a general estimated number of gross tons passing the asset, which may be advantageous, as it may provide information for optimizing the route planning of rolling stocks. It will be understood that the term optimizing and/or optimization is intended to comprise the (semi) automated selection of a best available element (with regard to some criterion) from some set of available alternatives. It can be the best value(s) of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains.
Moreover, the invention may use the sensor data for generating signals for processing and/or methods of machine learning and artificial intelligence (AI) to derive information like vertical movement, vibration, train speed, train type from multiple data sources. Thus, the invention may be able to plan the routing of rolling stock based on data relating, for example, train categories (high-speed, passenger, cargo trains) using vendor specific train “footprints” to aggregate an accurate usage statistic and detect specific attributes of a train (e.g. so called “flat wheels”) which induce higher wear and abrasion on the railroad infrastructure. The invention may associate identified rolling stocks to an optimal routing.
Furthermore, in one embodiment of the present invention, the method may also comprise using the information sampled via at least one sensor to retrieve information of at least one sensor data measurements, for example, but no limited to, length, mass, time, current, electric tension, temperature, humidity, luminous intensity and any parameters derived therefrom such as acceleration, vibration, speed, time, distance, illumination, images, gyroscopic information, acoustics, ultra-sound, air pressure, magnetism, electro-magnetism, position, optical sensor information, etc.
Even further, the method may comprise the step of (semi)automated controlling the traffic and/or routing of rolling stocks in railways. For this purpose, the method may use a continuous data transmission. In another embodiment, the method may also use a periodical data transmission.
In another embodiment of the present invention, the method may include the step of storing all data generated by the at least one server.
A system according to the present invention may particularly comprise at least one sensor configured to sample sensor data relevant to railway system. Furthermore, the system may also comprise 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.
The system may further comprise at least one sensor data processing component configured to generate processed sensor data and at least one analyzing component configured to analyze the processed sensor data to generate a rolling stock routing plan. In some instances, this may be advantageous, as it may allow to the analyzing component to associate the processed sensor data to the routing plan of rolling stocks on the basis of, for example, a first and a second sensor data, i.e. it may allow to the analyzing component to associate the traffic status of railways based on current, past or future status of rolling stocks and railway infrastructure.
The analyzing component may be anything that is configured to provide the associated data and may comprise local and/or remote components and/or sub-components. It will be understood that the term associated data is intended to comprise at least two data sets that influence the other. One data set can influence the other data set and/or they influence each other and/or influence the merged data and/or influence data derived from the merged data set. Just accumulated data is not intended to be comprised. Non-limiting examples can be one data set (e.g., comprising train specific data) merged with another data set (e.g., comprising vibration data) provide a result considering both data sets.
In one embodiment of the present invention, the system may also comprise 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.
Moreover, the at least one analyzing component of the system may generate a rolling stock routing plan based on at least one of the following current or predicted relevant information of railways: technical condition of assets, technical condition of infrastructure components, degrading effect of rolling stocks, traffic load information of rolling stocks, risks of traffic delay, unplanned and/or planned maintenance and/or inspections, maintenance effectiveness metrics; and weather information.
In another embodiment of the present invention, the system the at least one analyzing component of the system may further comprise 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.
Even further, in one embodiment of the present invention, the server of the system may be configured to optimize the routing of rolling stocks 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.
The system may further comprise the association and/or arrangement at least one sensor with at least one of rolling stock and/or railway infrastructure.
The system may retrieve relevant information from sampled sensor data via at least one sensor wherein such sampled data may provide information of at least one of sensor data measurements, for example, but no limited to, length, mass, time, current, electric tension, temperature, humidity, luminous intensity and any parameters derived therefrom such as acceleration, vibration, speed, time, distance, illumination, images, gyroscopic information, acoustics, ultra-sound, air pressure, magnetism, electro-magnetism, position, optical sensor information etc.
In one embodiment of the present invention, the system may even further comprise the server being configured to provide at least one signal comprising parameters to define the route of rolling stocks, prediction of railway traffic and prediction of wear effect of rolling stocks on the railway infrastructure. Additionally or alternatively, the at least one 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.
The system may further comprise the at least one sensor being 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.
In another embodiment of the present invention, the system may comprise an interface component configured to bidirectionally communicate the at least one server with at least one authorized user.
Additionally or alternatively, the system may comprise at least one server being configured to monitor and predict the traffic of rolling stocks in railways taking into consideration the future status of railway infrastructure maintenance, which further 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.
The system may also comprise at least one storing component configured to store all data generated by the at least one server.
Furthermore, the invention may also comprise the use of the method embodiments and/or the system embodiments in traffic control. Even further, the invention may comprise the use of the method embodiments and/or the system embodiments for controlling traffic in railways.
The present technology is also defined by the following numbered embodiments.
Below, method embodiments will be discussed. These embodiments are abbreviated by the letter “M” followed by a number. When reference is herein made to a method embodiment, those embodiments are meant.
M1. A method for controlling traffic in railways.
M2. The method according to the preceding embodiment comprising sampling sensor data relevant to railway system via at least one sensor (200).
M3. The method according to any of preceding embodiments further comprising using at least one server (500).
M4. The method according to any of preceding embodiments wherein the at least one server comprises
M5. The method according to embodiment M1 or M4 wherein the method further comprises
M6. The method according to the preceding embodiment wherein the routing of rolling stocks is based on at least one of the following current or predicted relevant information of railways:
M7. The method according to any of the preceding embodiments and with features of embodiment M5 wherein the relevant information obtained from the sensor data is subjected to analysis via at least one analytical approach.
M8. The method according to any of the preceding embodiments and with features of embodiment M5 wherein the routing of rolling stocks is based on at least one analytical approach.
M9. The method according to any of the preceding two embodiments wherein the method further comprises associating and/or arranging at least one sensor with at least one of rolling stock and/or railway infrastructure.
M10. The method according to any of the preceding embodiments wherein the method further comprises using the server (500) for providing at least one signal comprising
wherein the at least one signal is based on at least one analytical approach.
M11. The method according to any of the preceding embodiments further wherein the method further comprises at least one of
M12. The method according to any of preceding embodiments further comprising using the sensor data for generating signals for processing and/or methods of machine learning and artificial intelligence (AI) to derive information like vertical movement, vibration, train speed, train type from multiple data sources.
M13. The method according to any of the preceding embodiments wherein further comprising the step of (semi)automated controlling the traffic and/or routing of rolling stocks in railways.
M14. The method according to any of the preceding embodiments wherein the step of (semi)automated controlling routing of rolling stocks are based on the wear effect of rolling stocks on the railways.
M15. The method according to any of the preceding embodiments comprises using the information sampled via at least one sensor to retrieve information of at least one sensor data measurements.
M16. The method according to any of the preceding embodiments and with features of embodiment M7 wherein the data transmission is continuous.
M17. The method according to any of the embodiments M1 to M14 and with features of embodiment M7 wherein the data transmission is periodical.
M18. The method according to any of the preceding embodiments wherein the method further comprises storing all data generated by the at least one server.
Below, system embodiments will be discussed. These embodiments are abbreviated by the letter “S” followed by a number. When reference is herein made to a system embodiment, those embodiments are meant.
S1. A system for controlling traffic (100) in railways.
S2. The system according to the preceding embodiment comprising at least one sensor (200) configured to sample sensor data relevant to railway system.
S3. The system according to embodiment S1 or S2, the system further comprising at least one server (500) configured to
S4. The system according to according to embodiment S2 or S3 wherein the system further comprises at least one sensor data processing component (300) configured to generate processed sensor data.
S5. The system according to according to the preceding embodiment wherein the system further comprises at least one analyzing component (400) configured to analyze the processed sensor data to generate a rolling stock routing plan.
S6. The system according to according to the preceding embodiment wherein the system further comprises at least one transmitting component (800) configured to transmit the route planning to at least one server (500) and/or at least one authorized user through an interface (700).
S7. The system according to the preceding embodiment 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:
S8. The system according to any of the two preceding embodiments wherein the at least one analyzing component (400) further comprises at least one analytical approach.
S9. The system according to any of the preceding system embodiments wherein the server (500) is configured to optimize the routing of rolling stocks based on at least one analytical approach.
S10. The system according to any of the preceding system embodiments further comprising the association and/or arrangement at least one sensor (200) with at least one of rolling stock and/or railway infrastructure.
S11. The system according to any of the preceding system embodiments wherein the information sampled via at least one sensor (200) provide information of at least one sensor data measurements.
S12. The system according to any of the preceding system embodiments wherein the server (500) is configured to provide at least one signal comprising parameters to define the route of rolling stocks;
wherein the at least one signal is based on at least one analytical approach.
S13. The system according to any of the preceding system embodiments wherein the at least one sensor (200) 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.
S14. The system according to any of the preceding system embodiments wherein the server (500) comprises an interface component (700) configured to bidirectionally communicate the at least one server with at least one authorized user.
S15. The system according to any of the preceding system embodiments wherein the server (500) further comprises monitoring and predicting the traffic of rolling stocks in railways considering the future status of railway infrastructure maintenance based on at least one analytical approach.
S16. The system any of the preceding system embodiments wherein the system further comprises at least one storing component (600) configured to store all data generated by the at least one server (500).
Below, use embodiments will be discussed. These embodiments are abbreviated by the letter “U” followed by a number. When reference is herein made to a use embodiment, those embodiments are meant.
U1. Use of the method according to any of the preceding method embodiments and/or the system according to any of the preceding system embodiments in traffic control.
U2. Use of the method according to any of the preceding method embodiments and/or the system according to any of the preceding system embodiments for controlling traffic in railways.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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”.
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.
| Number | Date | Country | Kind |
|---|---|---|---|
| 18180359.4 | Jun 2018 | EP | regional |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/065833 | 6/17/2019 | WO | 00 |
