EP3814192A1 - Système et procédé de gestion du trafic dans des voies ferrées - Google Patents

Système et procédé de gestion du trafic dans des voies ferrées

Info

Publication number
EP3814192A1
EP3814192A1 EP19731956.9A EP19731956A EP3814192A1 EP 3814192 A1 EP3814192 A1 EP 3814192A1 EP 19731956 A EP19731956 A EP 19731956A EP 3814192 A1 EP3814192 A1 EP 3814192A1
Authority
EP
European Patent Office
Prior art keywords
traffic
sensor
railway
railways
sensor data
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.)
Withdrawn
Application number
EP19731956.9A
Other languages
German (de)
English (en)
Inventor
Thomas Böhm
Vlad LATA
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.)
Konux GmbH
Original Assignee
Konux 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.)
Filing date
Publication date
Application filed by Konux GmbH filed Critical Konux GmbH
Publication of EP3814192A1 publication Critical patent/EP3814192A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0081—On-board diagnosis or maintenance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/045—Rail wear
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/10—Operations, e.g. scheduling or time tables
    • B61L27/16—Trackside optimisation of vehicle or train operation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L2201/00—Control methods

Definitions

  • 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.
  • Rail, railway or rail transport has been developed for transferring goods and passengers on wheeled vehicles on rails, also known as tracks.
  • rail vehicles rolling stock
  • 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.
  • railway capacity is fundamentally considered a network system.
  • 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.
  • 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.
  • US 6179252 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.
  • 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.
  • the US 5950966 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.
  • US 5364047 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.
  • US 5332180 A discloses a railway traffic control system in which accurate vehicle information is effectively available in real-time to facilitate control of traffic flow.
  • 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.
  • the on-board vehicle information detection equipment comprises an inertial measurement unit providing dynamic vehicle motion information to a position processor.
  • the inertial measurement unit may have as many as three gyroscopes and three accelerometers or as little as a single accelerometer.
  • the processor preferably includes a recursive estimation filter to combine the a priori route information with movement attributes derived from the inertial measurement unit. Summary
  • 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.
  • the present invention relates to a method and a system for controlling traffic in railways.
  • the method may comprise sampling sensor data relevant to railway system via at least one sensor.
  • 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.
  • 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.
  • railway system is intended to comprise railway infrastructure and rolling stocks.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • 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.
  • ICA independent component analysis
  • 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.
  • the method may further comprise associating and/or arranging at least one sensor with at least one of rolling stock and/or railway infrastructure.
  • 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.
  • 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.
  • the signal may be transmitted via wirelessly and/or hard-wired means.
  • 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.
  • 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.
  • the stress and, hence, the wear of the frog crossing point of two rails
  • the invention uses the continuously recorded and combined data to derive the stress and to accumulate it over time.
  • 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.
  • 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.
  • 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.
  • AI machine learning and artificial intelligence
  • 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.
  • 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.
  • 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.
  • the method may comprise the step of (semi)automated controlling the traffic and/or routing of rolling stocks in railways.
  • the method may use a continuous data transmission.
  • the method may also use a periodical data transmission.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • 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.
  • 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.
  • ICA independent component analysis
  • the system may also comprise at least one storing component configured to store all data generated by the at least one server.
  • 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.
  • the method according to the preceding embodiment comprising sampling sensor data relevant to railway system via at least one sensor (200) .
  • the at least one signal is based on at least one analytical approach.
  • 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.
  • the system according to the preceding embodiment comprising at least one sensor (200) configured to sample sensor data relevant to railway system.
  • system further comprises at least one analyzing component (400) configured to analyze the processed sensor data to generate a rolling stock routing plan.
  • 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).
  • 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).
  • server (500) is configured to optimize the routing of rolling stocks based on at least one analytical approach.
  • the at least one signal is based on at least one analytical approach.
  • the server (500) comprises an interface component (700) configured to bidirectionally communicate the at least one server with at least one authorized user. 515. 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.
  • 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).
  • 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
  • Fig. 2 depicts a schematic of system for controlling traffic in railways according to embodiments of the present invention
  • Fig. 3 depicts an exemplary application of the traffic control system according to embodiments of the present invention
  • Fig . 1 schematically depicts a description of a system configu red for a railway infrastructure.
  • the system may comprise a railway section with the railway 1 itself, comprising rails 2 and sleepers 3.
  • the sleepers 3 instead of the sleepers 3 also a solid bed for the rails 2 can be provided .
  • 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.
  • 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.
  • a second sensor 11 can also arranged on a nother sleeper d istant 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 ca n further be of different kind - such as where the first sensor 10 may be an acceleration sensor, the second sensor 11 ca n 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 a rranged 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 a ll those sensors mentioned here and/or before are just non-limiting examples.
  • 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.
  • 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.
  • 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.
  • the processing component 300 may assume a configuration that allows it bidirectionally communicating with the sensor 200.
  • the processing component 300 may also be integrated with at least one of the sensors 200.
  • the processing component 300 may also comprise an imbedded module of the sensors 200.
  • 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.
  • ICA independent component analysis
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • the interface component 700 may comprise a bidirectionally communicated component configured to exchange information with the servers 500.
  • 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.
  • the interface component 700 may also comprise a physical terminal for providing access to the servers 500 to an authorized user.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • 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.
  • 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.
  • 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 .
  • 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.
  • 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.
  • 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.
  • ICA independent component analysis
  • 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 (Yl), ..., followed by step (Z).
  • 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 (Yl), ..., followed by step (Z).

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un système et un procédé pour gérer le trafic dans des voies ferrées. La présente invention concerne également une utilisation correspondante. La présente invention concerne en particulier le calcul et la prédiction de trajets optimaux pour une pluralité de trains et l'optimisation de la capacité des voies de train. Le procédé consiste à commander le trafic dans les chemins de fer, le procédé consistant à échantillonner des données de capteur pertinentes pour un système de chemin de fer par l'intermédiaire d'au moins un capteur (200) et utiliser au moins un serveur (500) pour recevoir les données de capteur en provenance du ou des capteurs (200), prédire l'état de l'infrastructure de chemin de fer sur la base du futur équipement roulant, et commander le trafic d'équipement roulant sur la base de l'état futur.
EP19731956.9A 2018-06-28 2019-06-17 Système et procédé de gestion du trafic dans des voies ferrées Withdrawn EP3814192A1 (fr)

Applications Claiming Priority (2)

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EP18180359 2018-06-28
PCT/EP2019/065833 WO2020002018A1 (fr) 2018-06-28 2019-06-17 Système et procédé de gestion du trafic dans des voies ferrées

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