WO2005095175A1 - Systeme d'exploitation de trains - Google Patents

Systeme d'exploitation de trains Download PDF

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Publication number
WO2005095175A1
WO2005095175A1 PCT/GB2005/000596 GB2005000596W WO2005095175A1 WO 2005095175 A1 WO2005095175 A1 WO 2005095175A1 GB 2005000596 W GB2005000596 W GB 2005000596W WO 2005095175 A1 WO2005095175 A1 WO 2005095175A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
low adhesion
railway
train
predictions
Prior art date
Application number
PCT/GB2005/000596
Other languages
English (en)
Inventor
John David Tunley
Stuart Duncan Brown
William Poole
Ian Hope Mitchell
Original Assignee
Aea Technology Plc
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
Priority claimed from GB0406920A external-priority patent/GB0406920D0/en
Priority claimed from GB0416264A external-priority patent/GB0416264D0/en
Application filed by Aea Technology Plc filed Critical Aea Technology Plc
Priority to EP05708395A priority Critical patent/EP1730009A1/fr
Publication of WO2005095175A1 publication Critical patent/WO2005095175A1/fr

Links

Classifications

    • 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 vehicle trains
    • B61L23/04—Control, warning, or like safety means along the route or between vehicles or vehicle trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • 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 vehicle train operation
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L2205/00—Communication or navigation systems for railway traffic
    • B61L2205/04—Satellite based navigation systems, e.g. GPS

Definitions

  • This invention relates to a methocd and an apparatus for managing the operation of trains so as to minimise the effects of low adhesion.
  • the coefficient of friction between steel wheels and steel rails can vary over a very w ⁇ -de range, typically between 0.02 and 0.4, for example being at its highest with clean dry rails, and being low where there are damp leaves on the rails.
  • the problems associated with low friction have been recognised for well over 100 years, and to a limited extent can be overcome by sanding, carried out by the driver of a train. Nevertheless, in low adhesion conditions, a driver has to drive more cautiously, and this causes delays which can effect other trains on the network.
  • a method of managing the operation of trains on a railway comprising the steps of: a) obtaining data on a multiplicity of factors which affect the risk of low adhesion, at lea_st some of the data being real-time data, and incorporating geographical information; b) processing the data so as to g nerate predictions as to which geographical parts of the railway have low adhesion; c) supplying these predictions to a train-control signalling centre; d) the signalling centre rescheduling the movement of trains in accordance with the predictions so as to minimise the effect of low adhesion on operation of the railway; and e) transmitting data to each operating train such that the driver is warned when that train enters a geographical part of the railway predicted to have low adhesion.
  • the method comprises transmitting data to each train which includes the locations of all geographical parts of the railway predicted to have low adhesion.
  • This warning message transmission system may also be used to provide the driver with warnings of other location-specific information, for example adverse weather conditions in a local area, temporary and permanent speed restrictions, sites identified as having degraded track quality, the expected location of track workers, and warnings of trespassers or vandals on or near the line.
  • speed restrictions a warning is preferably displayed in the driver's cab indicating what the speed limit is; and a further notification is provided to the driver when the train has passed through trie speed restriction.
  • the signalling centre controls signals on the railway to achieve the rescheduled movement of the trains.
  • the drivers may be trained to apply no more than 70% braking force, but to apply the brakes earlier, if the adhesion is predicted to be low.
  • the method also comprises displaying which geographical parts of the railway are predicted to have low adhesion on a route diagram in the train-control signalling centre.
  • Alternatively or additionally such information may be displayed on a computer screen, and incorporate a drop-down menu of additional information about that part of the track, at least some of this additional information being the data on the factors which affect the risk of low adhesion.
  • the processing of the data to generate the predictions is carried out using a self-learning computer system, for example a neural network.
  • the processing generates predictions as to the values of adhesion, categorised into at least three categories: low, medium and high. However, it might make predictions in only two categories, or in more than three categories. As an extreme example, the predictions might predict the numerical value of the coefficient of friction.
  • the invention also provides an apparatus for performing such a method, that is to say an apparatus comprising: a) means for obtaining data on a multiplicity of factors which affect the risk of low adhesion, at least some of the data-providing means being sensors providing real-time data, and the data also incorporating geographical information; b) means to process the data so as to generate predictions as to which geographical parts of the railway have low adhesion; c) means to transmit these predictions to a train- control signalling centre; d) means in the signalling centre to reschedule the movement of trains in accordance with the predictions so as to minimise the effect of low adhesion on operation of the railway; and e) means to transmit data to each operating train such that the driver is warned when that train enters a geographical part of the railway predicted to have low adhesion.
  • Figure 1 shows a diagrammatic view of part of a railway network incorporating an apparatus of the present invention
  • Figure 2 shows a diagrammatic view of part of the apparatus of the present invention.
  • part of a railway network is shown diagrammatically, incorporating railway lines 10 and railway stations 12. At some places the railway line passes over a bridge 14; at other places it passes through a cutting 16; and at other places 18 trees grow alongside the line. The number of tracks on each line are not indicated, and the junctions are not shown in any detail.
  • a number of trains 20 are shown on the lines 10, and the movement of the trains 20 is controlled by signals (not shown) , operated from a signalling centre 22.
  • Adhesion between the wheels of the trains 20 and the rails is essential to safe operation of the railway, and the value of this adhesion at any particular position along a line depends on a number of parameters . For example it depends upon the presence of any moisture on the railhead; it is affected by rain and snow; and it depends upon the presence of any contaminants on the railhead, such as industrial contaminants or dead leaves. Furthermore, if a train 20 is found to slide or slip, this is an indication that that particular part of the line has low adhesion.
  • the signalling centre 22 incorporates a neural network computer 24 which is provided with data on these parameters.
  • the computer 24 can receive data input from a keyboard 26, from remote sensors via one or more aerials 28, and from direct-wired sensors 30 (via electrical or optical cables) .
  • moisture sensors 30 may be installed at different locations around the network, and data may be received' from each train 20 (via the aerial 28) if low adhesion is found as a result of the train 20 sliding or slipping.
  • sensors are also provided to detect rainfall and other relevant weather parameters.
  • Geographical data can be input into the computer by an operator, for example via the keyboard 26 (and/or a mouse) , firstly as to the routes followed by the lines 10, and also as to the location of geographical features which may affect adhesion such as cuttings 16 (where dead leaves may collect) , and places where trees 18 grow alongside the track.
  • data is also input into the computer 24 about the density of vegetation near the track, the type of vegetation, and information on wind speeds and directions, and leaf fall. For example this may be input by an operator, or information may be provided by automatic sensors detecting parameters relating to leaves such as leaf colour.
  • dynamic factors which must be measured in real-time, such as weather conditions, observations of trains slipping, moisture on the railhead, leaf mobility etc
  • quasi-static factors which must be regularly reviewed and updated, such as tree density, type and size, the presence of overhanging branches, the distance of the vegetation from the track, etc, and also the presence of other sources of contamination such as dust from furnaces or quarries
  • static factors such as altitude, track gradient, line speed, braking or traction site (e.g. approaching or departing from a station 12), and the nature of the track cross-section such as cuttings 16 or embankments.
  • Data on such static factors may be provided from existing databases of geographical information; data on some of the dynamic factors may be obtained from external organisations, for example data on weather conditions from the Meteorological Office, and data 'on leaf fall from agricultural advisory bodies .
  • the computer 24 is arranged to assess all the data relevant to a particular section of track (for example a section of length 100 m) , and to determine whether that section is likely to have low adhesion, medium adhesion, or high adhesion.
  • the computer 24 acts as a neural network, and is self-learning. Initially it is provided with such data on a historical basis, and learns from this historical data how best to predict the likely value of adhesion. The weights given to the different data by the neural network may be different for different sections of track. Subsequently, during use of the system, the computer 24 carries out this assessment for each different section of the network; these assessments may be carried out sequentially, or possibly a number may be carried out in parallel. Consequently the computer 24 produces output data 34 providing an indication of the likely adhesion level for each section of each line 10.
  • This output data 34 is supplied to a traffic scheduling computer 36 in the signalling centre 22.
  • the computer 36 is associated with a display panel 38 for an operator, showing the location of trains 20 on the lines 10; the display panel 38 identifies, for example in different colours, those sections of the lines 10 which are predicted as having low or medium adhesion. The operator can therefore reschedule the traffic to take this information into account.
  • the computer 36 reschedules all the movements of the trains 20 to optimise traffic flow, taking into account the predictions of low adhesion. For example this may entail letting through a train 20 on high adhesion, and restricting the speed of another train 20 on a low adhesion section of line. The computer 36 then operates the signals 40 accordingly.
  • the computer 36 might merely advise the operator, by proposing a new schedule; or might provide a new schedule to a separate route setting system (not shown) which operates the signals accordingly.
  • the computer 36 preferably also displays the information about track adhesion on a computer screen for an adhesion manager, and each section of track identified as having low or medium adhesion may display further information (using a drop-down menu) about the factors which are relevant to the risk of reduced adhesion on that section.
  • each train 20 incorporates a GPS instrument which determines its location, and the GPS instrument is provided (via the aerial 42) with the coordinates of the sections of track where the adhesion is low or medium.
  • the GPS instrument provides a warning whenever the train 20 is within a preset distance along the line of such a section, this distance taking into account driver reaction time and an allowance for the adhesion conditions and their effect on a vehicle retardation.
  • the GPS instrument may also be arranged to provide another warning when the train actually enters the track section of low adhesion.
  • the instrument would also be arranged to notify the driver when the train has left the low adhesion section.
  • this computer 36 can transmit a warning signal to a train 20 that is approaching a track section of lower adhesion.
  • the warning is given when the train 20 is far enough away to be able to brake, and so should take the train's speed into account. Consequently the driver can adjust the way he operates the brakes, or accelerates, to take this predicted value of adhesion into account.
  • each such computer 24 performing the adhesion predictions described above for the adjacent sections of track.
  • Each computer 24 would then provide its output data 34 to the scheduling computer 36 at the signalling centre 22, either by radio transmission or through a cable.
  • adhesion predictions may be carried out for sections a different length, preferably between 20 m and 5 km.
  • the computer 36 may also be used to supply data to the trains 20 to provide the locations of other track-related events, not relating specifically to adhesion, such as speed restriction information, and adverse weather events such as floods and snow.
  • the train 20 preferably includes a display in the cab to show the driver the speed limit.

Abstract

Les effets d'une faible adhérence sur le fonctionnement de trains sur une voie sont réduits au minimum par obtention de données sur une multiplicité de facteurs influant sur le risque de faible adhérence, et traitement des données de manière à générer des prédictions des sections de ligne présentant une faible adhérence. Ces prédictions sont fournies à un centre de signalisation de commande de trains, lequel commande le mouvement des trains selon les prédictions. Un message d'avertissement est également adressé au conducteur de n'importe quel train sur le point de pénétrer dans une partie de la voie dont la prédiction indique une faible adhérence. Les sections de ligne dont la prédiction indique une faible adhérence peuvent être affichées sur un schéma de route dans le centre de signalisation de commande des trains.
PCT/GB2005/000596 2004-03-27 2005-02-18 Systeme d'exploitation de trains WO2005095175A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP05708395A EP1730009A1 (fr) 2004-03-27 2005-02-18 Systeme d'exploitation de trains

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
GB0406920.9 2004-03-27
GB0406920A GB0406920D0 (en) 2004-03-27 2004-03-27 Train operating system
GB0416264A GB0416264D0 (en) 2004-07-21 2004-07-21 Train operating system
GB0416264.0 2004-07-21

Publications (1)

Publication Number Publication Date
WO2005095175A1 true WO2005095175A1 (fr) 2005-10-13

Family

ID=34960684

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2005/000596 WO2005095175A1 (fr) 2004-03-27 2005-02-18 Systeme d'exploitation de trains

Country Status (2)

Country Link
EP (1) EP1730009A1 (fr)
WO (1) WO2005095175A1 (fr)

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103246956A (zh) * 2013-05-06 2013-08-14 北京交通大学 随机需求条件下高速铁路网络中旅客列车灵活调度的方法
WO2016112358A1 (fr) * 2015-01-08 2016-07-14 Smartdrive Systems, Inc. Système et procédé pour la synthèse d'information d'événements de véhicule ferroviaire
US9487222B2 (en) 2015-01-08 2016-11-08 Smartdrive Systems, Inc. System and method for aggregation display and analysis of rail vehicle event information
US9663127B2 (en) 2014-10-28 2017-05-30 Smartdrive Systems, Inc. Rail vehicle event detection and recording system
US9679424B2 (en) 2007-05-08 2017-06-13 Smartdrive Systems, Inc. Distributed vehicle event recorder systems having a portable memory data transfer system
US9691195B2 (en) 2006-03-16 2017-06-27 Smartdrive Systems, Inc. Vehicle event recorder systems and networks having integrated cellular wireless communications systems
US9908546B2 (en) 2015-01-12 2018-03-06 Smartdrive Systems, Inc. Rail vehicle event triggering system and method
US9942526B2 (en) 2006-03-16 2018-04-10 Smartdrive Systems, Inc. Vehicle event recorders with integrated web server
US10019858B2 (en) 2013-10-16 2018-07-10 Smartdrive Systems, Inc. Vehicle event playback apparatus and methods
US10053032B2 (en) 2006-11-07 2018-08-21 Smartdrive Systems, Inc. Power management systems for automotive video event recorders
US10249105B2 (en) 2014-02-21 2019-04-02 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US10339732B2 (en) 2006-11-07 2019-07-02 Smartdrive Systems, Inc. Vehicle operator performance history recording, scoring and reporting systems
US10471828B2 (en) 2006-11-09 2019-11-12 Smartdrive Systems, Inc. Vehicle exception event management systems
DE102019204371A1 (de) * 2019-03-28 2020-10-01 Siemens Mobility GmbH Verfahren zur automatischen Zugkontrolle mit Schlupferfassung
US10930093B2 (en) 2015-04-01 2021-02-23 Smartdrive Systems, Inc. Vehicle event recording system and method
US11260878B2 (en) 2013-11-11 2022-03-01 Smartdrive Systems, Inc. Vehicle fuel consumption monitor and feedback systems
WO2022043964A1 (fr) * 2020-08-31 2022-03-03 Faiveley Transport Italia S.P.A. Système de freinage pour au moins un véhicule ferroviaire et architecture de signalisation ferroviaire
DE102022203117A1 (de) 2022-03-30 2023-10-05 Siemens Mobility GmbH Eisenbahngleisanlage und Verfahren zu deren Betrieb
GB2621327A (en) * 2022-08-04 2024-02-14 Hitachi Rail Ltd Rail network management system, and train for operation on a rail network

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Cited By (29)

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Publication number Priority date Publication date Assignee Title
US9691195B2 (en) 2006-03-16 2017-06-27 Smartdrive Systems, Inc. Vehicle event recorder systems and networks having integrated cellular wireless communications systems
US10404951B2 (en) 2006-03-16 2019-09-03 Smartdrive Systems, Inc. Vehicle event recorders with integrated web server
US9942526B2 (en) 2006-03-16 2018-04-10 Smartdrive Systems, Inc. Vehicle event recorders with integrated web server
US10682969B2 (en) 2006-11-07 2020-06-16 Smartdrive Systems, Inc. Power management systems for automotive video event recorders
US10339732B2 (en) 2006-11-07 2019-07-02 Smartdrive Systems, Inc. Vehicle operator performance history recording, scoring and reporting systems
US10053032B2 (en) 2006-11-07 2018-08-21 Smartdrive Systems, Inc. Power management systems for automotive video event recorders
US11623517B2 (en) 2006-11-09 2023-04-11 SmartDriven Systems, Inc. Vehicle exception event management systems
US10471828B2 (en) 2006-11-09 2019-11-12 Smartdrive Systems, Inc. Vehicle exception event management systems
US9679424B2 (en) 2007-05-08 2017-06-13 Smartdrive Systems, Inc. Distributed vehicle event recorder systems having a portable memory data transfer system
CN103246956A (zh) * 2013-05-06 2013-08-14 北京交通大学 随机需求条件下高速铁路网络中旅客列车灵活调度的方法
US10019858B2 (en) 2013-10-16 2018-07-10 Smartdrive Systems, Inc. Vehicle event playback apparatus and methods
US10818112B2 (en) 2013-10-16 2020-10-27 Smartdrive Systems, Inc. Vehicle event playback apparatus and methods
US11260878B2 (en) 2013-11-11 2022-03-01 Smartdrive Systems, Inc. Vehicle fuel consumption monitor and feedback systems
US11884255B2 (en) 2013-11-11 2024-01-30 Smartdrive Systems, Inc. Vehicle fuel consumption monitor and feedback systems
US11250649B2 (en) 2014-02-21 2022-02-15 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US10497187B2 (en) 2014-02-21 2019-12-03 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US11734964B2 (en) 2014-02-21 2023-08-22 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US10249105B2 (en) 2014-02-21 2019-04-02 Smartdrive Systems, Inc. System and method to detect execution of driving maneuvers
US9663127B2 (en) 2014-10-28 2017-05-30 Smartdrive Systems, Inc. Rail vehicle event detection and recording system
US9487222B2 (en) 2015-01-08 2016-11-08 Smartdrive Systems, Inc. System and method for aggregation display and analysis of rail vehicle event information
US9902410B2 (en) 2015-01-08 2018-02-27 Smartdrive Systems, Inc. System and method for synthesizing rail vehicle event information
US9981674B1 (en) 2015-01-08 2018-05-29 Smartdrive Systems, Inc. System and method for aggregation display and analysis of rail vehicle event information
WO2016112358A1 (fr) * 2015-01-08 2016-07-14 Smartdrive Systems, Inc. Système et procédé pour la synthèse d'information d'événements de véhicule ferroviaire
US9908546B2 (en) 2015-01-12 2018-03-06 Smartdrive Systems, Inc. Rail vehicle event triggering system and method
US10930093B2 (en) 2015-04-01 2021-02-23 Smartdrive Systems, Inc. Vehicle event recording system and method
DE102019204371A1 (de) * 2019-03-28 2020-10-01 Siemens Mobility GmbH Verfahren zur automatischen Zugkontrolle mit Schlupferfassung
WO2022043964A1 (fr) * 2020-08-31 2022-03-03 Faiveley Transport Italia S.P.A. Système de freinage pour au moins un véhicule ferroviaire et architecture de signalisation ferroviaire
DE102022203117A1 (de) 2022-03-30 2023-10-05 Siemens Mobility GmbH Eisenbahngleisanlage und Verfahren zu deren Betrieb
GB2621327A (en) * 2022-08-04 2024-02-14 Hitachi Rail Ltd Rail network management system, and train for operation on a rail network

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