EP3437958A1 - Évaluation d'un temps de conduite prévisionnel d'un véhicule ferroviaire - Google Patents

Évaluation d'un temps de conduite prévisionnel d'un véhicule ferroviaire Download PDF

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Publication number
EP3437958A1
EP3437958A1 EP18186042.0A EP18186042A EP3437958A1 EP 3437958 A1 EP3437958 A1 EP 3437958A1 EP 18186042 A EP18186042 A EP 18186042A EP 3437958 A1 EP3437958 A1 EP 3437958A1
Authority
EP
European Patent Office
Prior art keywords
route
context information
travel time
rail vehicle
rki
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Withdrawn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
EP18186042.0A
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German (de)
English (en)
Inventor
Steven Alexander Calder
Hamed Ketabdar
Navid Nourani-Vatani
Andrew Palmer
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
Publication of EP3437958A1 publication Critical patent/EP3437958A1/fr
Withdrawn legal-status Critical Current

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    • 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
    • B61L27/10—Operations, e.g. scheduling or time tables
    • B61L27/12—Preparing schedules

Definitions

  • the invention relates to a method for estimating a probable travel time of a rail vehicle. Moreover, the invention relates to a driving time estimating device. Furthermore, the invention relates to a traffic system.
  • timetables for example timetables, for public transport systems can be created so that passengers can estimate the time they need to reach a particular destination.
  • an expected arrival time and / or departure time of a vehicle is determined on the basis of an expected driving speed and transmitted via a communication device to a user.
  • This object is achieved by a method for estimating a probable travel time of a rail vehicle according to claim 1, a driving time estimating device according to claim 5 and a traffic system according to claim 6.
  • context information is compared with historical reference context information about the route or a route comparable to the route from a database, the reference context information additionally having historical travel time information about the assigned route.
  • a route comparable to the current route is a route with comparable route parameters. For example, the length and the topography of the route are comparable, there are similar traffic densities, etc. It may be assumed in such a case that with a similar context, for example with a similarly fast vehicle under similar traffic conditions, a similar travel time is achieved.
  • an estimated travel time for the route to be traveled is determined based on the comparison of the context information with the historical reference context information. For example, the estimated travel time may be determined on the basis of reference data records selected using the comparison and the current context information. In this case, an estimation method trained by the reference context information stored in the database is used, wherein the training of the estimation method comprises a machine learning method.
  • railway operators can advantageously create more exact timetables.
  • the method may also be advantageously used to update timetables in response to a changing context and thus to provide passengers with more reliable and accurate information and to fine-tune the driving experience.
  • the use of a machine learning method is particularly advantageous if a route section is passed through for the first time and no historical data from this route section is available yet.
  • the travel time can be based on reference data from other routes having similar characteristics as the current route.
  • context data or reference data can be obtained and evaluated in real time from the currently already traveled part of the traveled section of the route.
  • the two approaches can also be combined to provide a more accurate estimate of travel time.
  • the estimated travel time can be estimated on a broad database of reference data records. so that due to statistical effects, the reliability and accuracy of the estimation result is increased.
  • the training of the estimation method comprises a machine learning method.
  • examples can be learned and these are generalized after completion of the learning phase. That is, the examples are not simply learned by heart, but patterns and laws are identified in the learning data. This allows unknown data to be assessed afterwards.
  • Part of the driving time estimation device according to the invention is also a comparison unit, which is set up to compare the context information with historical reference context information about the route or a route comparable to the route from a database.
  • the reference context information additionally has historical travel time information about the route to be traveled.
  • the driving time estimating device also includes an estimation unit for determining the estimated travel time for the route to be traveled on the basis of the comparison of the context information with the historical reference context information using an estimation method trained by the reference context information stored in the database by a machine learning method.
  • the driving time estimating device shares the advantages of the method according to the invention for estimating an expected journey time of a rail vehicle.
  • the traffic system according to the invention comprises the time estimator according to the invention.
  • the time estimating device according to the invention shares the advantages of the time estimating device according to the invention.
  • Some components of the time estimating device can for the most part be designed in the form of software components. This concerns in particular parts of the context determination unit, the comparison unit and the estimation unit. In principle, however, these components can also be partly realized, in particular in the case of particularly fast calculations, in the form of software-supported hardware, for example FPGAs or the like.
  • the required interfaces for example, if it is only about a transfer of data from other software components, be designed as software interfaces. However, they can also be configured as hardware-based interfaces, which are controlled by suitable software.
  • the calculations can also take place outside the rail vehicle in an external central computer unit, for example as the central computer of a data network. Current context information collected by vehicle sensors may optionally be transmitted by the rail vehicle to this external central computer unit. Conversely, travel time information determined by the central computer unit can also be transmitted to individual rail vehicles.
  • a largely software-based implementation has the advantage that already existing in a traffic control system existing computer systems can be easily retrofitted by a software update to work in the inventive way.
  • the problem is also solved by a corresponding computer program product with a computer program, which directly into a memory device of such computer system is loadable, with program sections to perform all the steps of the inventive method when the computer program is executed in the computer system.
  • Such a computer program product may contain, in addition to the computer program, additional components, e.g. a documentation and / or additional components, also hardware components, such as e.g. Hardware keys (dongles, etc.) for using the software include
  • a computer-readable medium for example a memory stick, a hard disk or another portable or permanently installed data carrier can be used, on which the computer program readable and executable by a computer unit are stored.
  • the computer unit may e.g. for this purpose have one or more cooperating microprocessors or the like.
  • the measures described improve the effectiveness of rail transport and increase passenger comfort.
  • a reference data record with reference context information is identified on the basis of the comparison which comes closest to the determined context information and the estimated travel time is determined on the basis of the historical travel time information assigned to the selected reference data record.
  • a historical reference data set is selected, which most closely corresponds to the current scenario. The similarity of the historical reference data record can be determined by comparing the individual historical context information associated with it with the corresponding context information of the current traffic situation. The better the historical reference data set corresponds to the current scenario, the easier it is to transfer the travel time information of the historical reference data record to the current situation.
  • the information mentioned can have an influence on the speed of a rail vehicle, it is advantageous to take into account at least a part of it in the travel time determination. If reference data sets for the same route or a route with comparable route parameters whose context information corresponds to the current context situation of the rail vehicle are found, it is possible to directly estimate a travel time for a route currently being traveled on the basis of travel times of the reference data records.
  • the determination of the probable travel time is based on a reference data set stored in the database estimation method.
  • a flow chart 100 is shown which illustrates a method for estimating a probable travel time t F of a rail vehicle according to an embodiment of the invention.
  • a route FS with start and destination is first determined by a user.
  • parameter data of the route can also be obtained from a database or from an on-board computer unit.
  • a route length SL of the travel distance FS to be traveled by the rail vehicle is determined.
  • current context information KI which relate to current driving time-relevant influencing factors for a route to be covered by the rail vehicle, are determined.
  • step 1.IV current weather information, information about the current traffic situation, information about daytime, regularly occurring restrictions and information about the driving profile of the driver are determined, for example via the Internet.
  • step 1.IV the context information KI and the determined route information FS, LS and additionally historical reference context information RKI and route information RFS, RLS from a database are then used to estimate an anticipated travel time t F.
  • t F There are a variety of methods available for estimating travel time. For example, monitored or unmonitored machine learning techniques, nearest neighbor comparisons, or simulation based approaches may be used.
  • FIG. 2 a block diagram is shown which illustrates a time estimator 20 according to an embodiment of the invention.
  • the time estimator 20 may, for example, be centrally located in a traffic control center.
  • the time estimator 20 includes a communication interface 21 for communication with external units and users. Via the communication interface 21, the position determining device 20 receives, for example, information FS about a current route, in particular start and destination, and possibly additional information, such as the route length SL, from a user.
  • the input data FS, SL are forwarded to a context determination unit 22, which searches on the basis of these data SL, FS suitable context information KI to the current route. For this purpose, corresponding context data KI, for example weather data, are searched for and acquired on the Internet via the communication interface 21.
  • the determined context information KI and further data FS, SL characterizing the relevant route FS are transmitted to a comparison unit 23.
  • the comparison unit 23 compares the determined context information KI with historical reference context information RKI.
  • the historical reference context information RKI is retrieved from an external database DB, and a reference data record NRD with reference context information RKI which is most similar to the context information KI of the current route is finally identified as a suitable reference data record NRD and transmitted to an estimation unit 24.
  • the estimation unit 24 determines, based on the selected reference data set NRD, an anticipated travel time t F for the selected travel route FS.
  • the determined travel time t F can finally be output via the communication interface 21 to a user or a traffic control device.
  • the estimation process may alternatively be based on a plurality of reference data sets NRD, these being for training the estimation unit 24, which then estimates a probable travel time t F on the basis of a trained model and the detected current context information KI of the current route FS.
  • the traffic system 30 comprises a plurality of rail vehicles 31a, 31b, 31c and a travel time estimation device 20 of the rail vehicles 31a, 31b, 31c data, in particular current context data KI, but also route parameters FS, LS over different routes over a communication interface 21 to the Driving time estimation device 20 transmitted.
  • the driving time estimating device 20 determines estimated travel times t F for the individual vehicles 31a, 31b, 31c and transmits these data t F to these vehicles 31 a, 31 b, 31 c as well as a traffic control device 32.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)
EP18186042.0A 2017-08-03 2018-07-27 Évaluation d'un temps de conduite prévisionnel d'un véhicule ferroviaire Withdrawn EP3437958A1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102017213512.3A DE102017213512A1 (de) 2017-08-03 2017-08-03 Abschätzen einer voraussichtlichen Fahrzeit eines Schienenfahrzeugs

Publications (1)

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EP3437958A1 true EP3437958A1 (fr) 2019-02-06

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EP (1) EP3437958A1 (fr)
DE (1) DE102017213512A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111554118A (zh) * 2020-04-24 2020-08-18 深圳职业技术学院 一种公交车到站时间的动态预测方法及系统
CN115019507A (zh) * 2022-06-06 2022-09-06 上海旷途科技有限公司 城市路网行程时间可靠性实时估计方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6631322B1 (en) * 2002-12-06 2003-10-07 General Electric Co. Method and apparatus for vehicle management
DE102009023704A1 (de) * 2009-06-03 2010-10-28 Voith Patent Gmbh Verfahren zur Information von Fahrpersonal in einem Schienenfahrzeug
US20150232097A1 (en) * 2006-03-20 2015-08-20 General Electric Company Energy management system and method for vehicle systems
WO2018036759A1 (fr) * 2016-08-23 2018-03-01 Siemens Aktiengesellschaft Prévision de parcours de train

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19633525A1 (de) * 1996-08-09 1998-02-12 Siemens Ag Informationssystem für Benutzer öffentlicher Fahrzeuge
DE102005051077A1 (de) * 2005-10-25 2007-04-26 Siemens Ag Verfahren zum Erfassen und Berücksichtigen von Seitenwindbelastungen bei einem in Fahrt befindlichen Schienenfahrzeug und dessen entsprechend ausgeführter Endwagen
DE102016212603A1 (de) * 2016-07-11 2018-01-11 Siemens Aktiengesellschaft Verfahren und Vorrichtung zum Ermitteln einer Fahrtverlauf-Information

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6631322B1 (en) * 2002-12-06 2003-10-07 General Electric Co. Method and apparatus for vehicle management
US20150232097A1 (en) * 2006-03-20 2015-08-20 General Electric Company Energy management system and method for vehicle systems
DE102009023704A1 (de) * 2009-06-03 2010-10-28 Voith Patent Gmbh Verfahren zur Information von Fahrpersonal in einem Schienenfahrzeug
WO2018036759A1 (fr) * 2016-08-23 2018-03-01 Siemens Aktiengesellschaft Prévision de parcours de train

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111554118A (zh) * 2020-04-24 2020-08-18 深圳职业技术学院 一种公交车到站时间的动态预测方法及系统
CN115019507A (zh) * 2022-06-06 2022-09-06 上海旷途科技有限公司 城市路网行程时间可靠性实时估计方法
CN115019507B (zh) * 2022-06-06 2023-12-01 上海旷途科技有限公司 城市路网行程时间可靠性实时估计方法

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