EP2873037A1 - Method and device for increasing the accuracy of a timetable creator for rail vehicles - Google Patents

Method and device for increasing the accuracy of a timetable creator for rail vehicles

Info

Publication number
EP2873037A1
EP2873037A1 EP20120753718 EP12753718A EP2873037A1 EP 2873037 A1 EP2873037 A1 EP 2873037A1 EP 20120753718 EP20120753718 EP 20120753718 EP 12753718 A EP12753718 A EP 12753718A EP 2873037 A1 EP2873037 A1 EP 2873037A1
Authority
EP
European Patent Office
Prior art keywords
means
characterized
optimized
minimum
preceding
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP20120753718
Other languages
German (de)
French (fr)
Inventor
Martin Harborth
Karl-Heinz Michel
Rene NOWAK
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens Mobility GmbH
Original Assignee
Siemens AG
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 Siemens AG filed Critical Siemens AG
Priority to PCT/EP2012/066832 priority Critical patent/WO2014032714A1/en
Publication of EP2873037A1 publication Critical patent/EP2873037A1/en
Application status is Pending legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models

Abstract

The invention relates to a method and a device for increasing the accuracy of a timetable creator (1) for rail vehicles (4), wherein running curves between stopping points are calculated and used for an automatic train control system (3). To minimize the manual effort and time expended, it is provided according to the invention that the running curves are optimized on the basis of statistically evaluated measured values of minimal running times between the stopping points. An automatic running curve optimizer (5) serves for this purpose.

Description

description

Method and apparatus for increasing the accuracy of a scheduling device for rail vehicles

The invention relates to a method and apparatus for increasing the accuracy of a scheduling device for rail vehicles, in which travel curves between the holding points calculated verwen- for an automatic train control system will be det.

For automatic Zugregulierung - Automatic Train regulation ATR - using modern train control and minimum and maximum driving times are required in addition to the nominal travel times between two breakpoints to specify exact shortened or lengthened journey times in case of Verspätun ¬ gen or Verfrühungen by the scheduling device, and take into account a forecast to. The controlled by the scheduling device ATR enables light to maintain or restore a clock and / or scheduled compliant driving operation, in particular in case of malfunctions, for example, door or signal interference. For this, the ATR affects the retention times of Schienenfahrzeu ¬ ge with the aim to divide the passenger load evenly on the vehicles.

The maximum driving times can be maintained usually by each train, whereas the minimum achievable travel times on various factors depend. The relevant in the automatic train control minimum travel times are calculated based on a model of Zugfahrdynamik in the scheduling device and transferred to an operational online timetable means for controlling the ATR.

The computation of the travel curves appropriately optimized based on driving dynamics models, which are also implemented in the scheduling means in both the rail vehicles which are equipped for example with Trainguard MT, as. However, these dynamics models are usually not quite identical. But even at absolutely identical ¬-Nazi models, the actual minimum travel times of rail vehicles often differ due to specific vehicle and route properties from the calculated by the scheduling unit values. Also daily time-dependent factors such as different levels of passenger traffic may affect the real minimum travel times.

To counteract these inaccuracies, is partially attempts nachzuj ustieren the minimum travel time in the driving directions Stel ¬ averaging means by trial and error. The disadvantage is mainly been achieved with the temporal and manu ¬ elle effort, which is also heavily dependent on experience.

The invention has for its object to provide a generic method and a corresponding device which allow a more convenient and accurate readjustment of the driving ¬ curves in the schedule preparing means.

According to the invention the object is achieved with a method in which the traveling curve to be optimized on the basis of evaluated values ​​statistically minimum travel times between the holding points.

The object is also achieved by an apparatus according to claim 8, which comprises means for traveling curve Opti ¬ optimization by statistical analysis of measured minimum traveling times between the holding points, said means for driving curve optimization with the driving directions Stel averaging means is connected such that the value calculated by the optimized travel curves be replaced, wherein the scheduling means ert automatic train control ansteu via an online schedule means which transmits, if necessary, the measurement values ​​of the minimum Fahrzei th on the online schedule means to the means for driving curve optimization.

The automated into account the measured mi- nimalen travel times that are used in the scheduling means, and thus also in the automatic train control system, the accuracy of the forecast, and the fact ba ¬ sierende train control system can be significantly improved. Due to better forecast the timetable potentials can be better utilized for the purpose of energy in the optimum manner. In addition, a higher acceptance of the automatic train control is achieved at the drivers.

The readjustment of the scheduling device takes place by means of automatic statistical evaluation of Spitz rides in the device for driving curve optimization. The Spitz drive leads to the minimum travel time between two stops in such a way that the rail vehicle as much as possible be ¬ be accelerated and braked in time before the specified holding neutralization point. Since the traveling curve has a peak in this case, at the start of the braking operation, this mode of operation is referred to as Spitz ride.

In the online timetable device can be configured online that all or certain rail vehicles during a specific time period, such as two hours are pointed drive. Spitz rides can during a test operation, but preferably in real driving conditions can be provided. During configured for Spitz rides time period the device for driving curve optimization records all travel times between two stops in the second. After the example, two hours continuous measurement phase can be switched back to normal operation with optimized travel times. The passengers hardly notice under real operating conditions that a measuring period expires, as well as Spitz rides through the automatic train control be such that the ride comfort for passengers is given. For example, the travel curve specifies a particular braking. The measurement phase is normally scheduled compliant because an ATR - Automatic Train Regulation - train control automatically specifies the holding time, while in normal operation, the holding and the travel times can be controlled. In addition to the data acquisition and statistical evaluation of measured values in the device for driving curve optimization is imple ¬ mented. This is preferably carried out after completion of the measurement phase and downshift to normal operation. The statis ¬ tables evaluation is performed by the average mini ¬ male travel time between two breakpoints in disregard of "outliers" are calculated. This average minimum travel times are automatically forwarded to the scheduling device and taken into account there for increasing the accuracy of the schedules. The new timetable provides after handing over to the online schedule means for a more accurate prediction and for a more precise control of the automatic train control.

The fully automatic evaluation of the measurement phase in which the rail cars are forced to Spitz rides, a readjustment of the relevant parameters of the train by means of direct feedback to the scheduling device is possible.

According to claim 2 it is provided that the travel curves are vehicle-type-dependent optimized. This vehicle-type-dependent filtering of the measured values ​​can take place when the device for driving curve optimization is known to which vehicle type the respective measured value belongs. Usually these Infor ¬ mation is available to the physical vehicle number. In this way, the average minimum travel time can be calculated in the device for travel curve Opti ¬ ming after completion of the test series for all vehicle types and breakpoint couples.

Additionally or alternatively, the travel curves can also periodic time periods optimized ¬ the claim. 3 In particular, daily time-dependent overload situations, ie phases with very high passenger volumes can be considered in this way, so that the schedule can be more precisely adapted to the actual needs, while the delay probability is minimized. The automatic optimization also enables a particularly rapid adjustment to the timetable to changing track conditions, new types of vehicles or altered overload situations because any time automatically new measurement series can be created.

According to claim 4 it is provided that in addition to the minimum travel time average and / or maximum Fahrzei ¬ th and / or minimum and / or average and / or ma ¬ ximum holding times and / or minimum and / or durchschnittli ¬ surface and / or maximum speed be used for travel curve optimization. This expansion of the database, the accuracy of the scheduling process can be further improved.

Preferably 5 readings for various road maps for the traveling curve optimization are used according to claim. In this way, the data base can be increased again and so ¬ improved the accuracy of the timetable to be created.

Conversely, already vorhande ¬ ne optimized driving curves for different schedules may be used according to claim 6, however. Remeasurments and any loss of time is avoided.

The evaluation of the measured values, in particular the differences between the original and optimized schedule data is carried out preferably according to claim 7 in graphical form. Thus, almost recognizable at a glance the optimization effect.

The invention is explained in more detail below with reference to a figurative representation. The figure shows a schematic representation of a Fahrpla ¬ nerstellungssystems with substantially peripheral components. Primary of the schedule from a schedule creation device 1 is created based on vehicle dynamics models. Up the necessary timetable data are transferred via an operational online timetable device 2 to an automatic train control system 3 to operate different rail vehicles. 4 However, the actual operating conditions are different due to specific vehicle and route properties frequently fig from the vehicle dynamics models so that a readjustment of the timetable is needed. For this purpose, a device for driving optimization curve 5, the measured values ​​of Spitz rides the rail vehicle 4 is automatically evaluates. The pointed driving ¬ th, i.e. tests, with the maximum permitted overall speed between two holding points at the maximum acceleration and for passengers excellent braking ability, during a certain time period, for example two hours detected by the automatic train control system 3 and the operative Online 2 -Fahrplaneinrichtung forwarded to the input direction to the travel curve optimization. 5 The

Tests can also occur during normal driving, as the passengers the Spitz rides little from the

can distinguish normal operation often somewhat slower rides.

are measured and evaluated at least the minimum travel time in Spitz ride. After completion of the series of measurements he ¬ averages the means for travel curve optimization 5 is a statistical mean value for each section between successive support points. The statistical mean refers to at least the minimum traveling time. Before ¬ Preferably, however, other parameters are considered, such as speeds. In addition, separate measurement series can be recorded or selected from the totality of the measured values ​​for different vehicle types and / or different time peri- odic passenger flows. The statistically evaluated results of the measurement means for driving curve optimization 5 pressurize the Fahrplanerstel- averaging means 1 in such a manner that the schedule is specified.

Claims

claims
1. A method for increasing the accuracy of a scheduling means (1) for rail vehicles (4), said drive curves calculated between holding points and are used for an auto matic train control system (3),
characterized in that
the drive curves based on statistically of evaluated measured values ​​mi nimaler travel times between the stations to be optimized.
2. The method of claim 1,
characterized in that
the drive curves are fahrzeutypabhängig optimized.
3. The method according to any one of the preceding claims, characterized in that
the drive curves for periodic time intervals optimized ¬ to.
4. The method according to any one of the preceding claims, characterized in that
in addition to the minimum average traveling time and / or maximum travel time and / or minimum and / or average and / or maximum holding times and / or minimum and / or average and / or maximum Geschwin speeds are used for the running curve optimization.
5. The method according to any one of the preceding claims, characterized in that
Readings for various timetables for the Fahrkurvenoptimie- tion be used.
6. The method according to any one of the preceding claims, characterized in that
optimized drive curves for different timetables are used.
7. The method according to any one of the preceding claims,
characterized in that
the calculated and optimized drive curves are analyzed graphically.
8. Apparatus for carrying out the method according to one of the preceding claims,
characterized in that
means for driving optimization curve (5) by means of statistical evaluation of measured minimum travel times between the stations with the scheduling means
is (1) such that the calculated to be replaced by the optimized travel curves, wherein the scheduling means (1) via an online schedule means
(2) controls the automatic train control system (3) which transmits, if necessary, the measurement values ​​of the minimum travel time via the online schedule means (2) to the means for driving curve optimization (5).
EP20120753718 2012-08-30 2012-08-30 Method and device for increasing the accuracy of a timetable creator for rail vehicles Pending EP2873037A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2012/066832 WO2014032714A1 (en) 2012-08-30 2012-08-30 Method and device for increasing the accuracy of a timetable creator for rail vehicles

Publications (1)

Publication Number Publication Date
EP2873037A1 true EP2873037A1 (en) 2015-05-20

Family

ID=46785414

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20120753718 Pending EP2873037A1 (en) 2012-08-30 2012-08-30 Method and device for increasing the accuracy of a timetable creator for rail vehicles

Country Status (3)

Country Link
EP (1) EP2873037A1 (en)
CN (1) CN104603802A (en)
WO (1) WO2014032714A1 (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102016215767A1 (en) 2016-08-23 2018-03-01 Siemens Aktiengesellschaft Predicting the movement of trains

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE1237612B (en) * 1963-08-06 1967-03-30 Licentia Gmbh Process for the elimination of the starting current for electric rail vehicles
SE9902577L (en) * 1999-07-06 2000-10-30 Daimler Chrysler Ag Communications and control system with several segments of rail vehicles
DE19946224C2 (en) * 1999-09-22 2001-08-30 Siemens Ag Apparatus and method for conserving driving power for rail vehicles
DE102005051077A1 (en) * 2005-10-25 2007-04-26 Siemens Ag A method for detecting and taking into account of side wind loads in a drive located in the rail vehicle and its appropriately designed end wagons
US8630757B2 (en) * 2006-03-20 2014-01-14 General Electric Company System and method for optimizing parameters of multiple rail vehicles operating over multiple intersecting railroad networks
CN100536456C (en) * 2007-01-29 2009-09-02 北京交通大学 Communication-based interconnected and intercommunicated I-CBIT train operation control system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO2014032714A1 *

Also Published As

Publication number Publication date
CN104603802A (en) 2015-05-06
WO2014032714A1 (en) 2014-03-06

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