EP0848677A2 - Procede de regulation de vehicules guides - Google Patents

Procede de regulation de vehicules guides

Info

Publication number
EP0848677A2
EP0848677A2 EP96934350A EP96934350A EP0848677A2 EP 0848677 A2 EP0848677 A2 EP 0848677A2 EP 96934350 A EP96934350 A EP 96934350A EP 96934350 A EP96934350 A EP 96934350A EP 0848677 A2 EP0848677 A2 EP 0848677A2
Authority
EP
European Patent Office
Prior art keywords
vehicles
vehicle
stop
constant
passenger
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Withdrawn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
EP96934350A
Other languages
German (de)
English (en)
Inventor
Ferdinand HERGERT-MÜCKUSCH
Andreas Schief
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 AG
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 EP0848677A2 publication Critical patent/EP0848677A2/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • 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
    • B61L27/14—Following schedules
    • 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

Definitions

  • the invention relates to a network of track-bound vehicles that travel predetermined routes.
  • An optimal regulation of the vehicles must take into account several general conditions, for example that in a network that is heavily used by vehicles, the passengers typically do not arrive at the respective holding parts at the intended departure times of the vehicles, but rather the next accessible vehicle take in the desired direction.
  • the travel times of the vehicles between the stops are also subject to random fluctuations, for example caused by deviations from normal operation within the vehicle. half of the vehicle, on the route or in any existing signaling technology.
  • a method for determining a minimum of a multi-dimensional function is known, for example, as a gradient descent method.
  • An efficient method for determining the gradient of a high-dimensional function is known as a back-propagation algorithm (D. Rumelhart et al, Parallel Distributed Processing, Bradford Books, MIT Press, Cambridge, Massachusetts, ISBN 0-262-68053-X, p 318 to 362, 1987).
  • a deterministic model for regulating track-bound vehicles is also known (V. Van Breusegem et al, Traffic Modeling and State Feedback Control for Metro Lines, IEEE Transactions on Automatic Control, Vol. 36, No. 7, p. 770 bis 784, July 1991).
  • the methods which use local heuristics for regulating track-bound vehicles are subject to some restrictions and thus have some disadvantages. These methods are all based on a local approach, ie the control instruction for a vehicle is only determined on the basis of information about the location of direct predecessors and successor trains. Information about vehicles further away is not taken into account when regulating the vehicles. Furthermore, no actually optimal solution for regulating the vehicles is determined, since the methods are based exclusively on heuristic approaches. Furthermore, the applicability of these methods is restricted to simple route networks with only one line.
  • the deterministic method for regulating the track-bound vehicles also does not offer an optimal solution for regulating the vehicles, since uncertainties such as irregular delays determined by random effects such as e.g. B. the delays of boarding and alighting processes or random delays in the travel times of the vehicles between two stops are not taken into account to a sufficient extent.
  • the invention is based on the problem of specifying a method which enables a globally optimal regulation of all track-bound vehicles which travel in a predetermined route network.
  • This procedure achieves a globally optimal regulation, which can even be adapted to the respective regulation problem in an application-specific manner.
  • Different aspects of the control which are to be emphasized in the optimization in particular, can be taken into account by weighting the summands within the target function.
  • Global optimization is made possible by taking all vehicles into account, i.e. the predicted delay times of all vehicles. It is therefore no longer exclusively dependent on the respective predecessor vehicle of the vehicle to be controlled or the direct successor vehicle.
  • Figure 1 is a flowchart illustrating individual process steps of the method
  • FIG. 2 shows a sketch in which a route network for explaining the first exemplary embodiment of the method is shown
  • FIG. 3 is a sketch describing a driving matrix that results from the route network shown in FIG. 2;
  • FIG. 4 shows a sketch describing a route network of a second exemplary embodiment
  • FIG. 5 shows a travel matrix which results from the route network shown in FIG. 4;
  • FIG. 6 shows a sketch in which various options for determining a passenger constant are shown
  • FIG. 7 shows a sketch in which various options for regulating the track-bound vehicles are described. The invention is further explained with reference to FIGS. 1 to 7.
  • target times ⁇ £ which specify a target departure time of a vehicle F n from a stop k, are read in.
  • This step is only carried out when regulating track-bound vehicles F n which have to meet a predetermined schedule, to which target times ⁇ £ for the departure time of the vehicle F n from a stop k are assigned.
  • a first index n is a natural number in the range from 1 to m and uniquely identifies each track-bound vehicle F n that are provided in the route network.
  • Each stop k is uniquely identified by a value between 1 and 1.
  • m denotes the number of vehicles F n and 1 the number of stops k.
  • a current time T is read in for the vehicle F n to which no target time is assigned, that is to say for which there is no predetermined schedule.
  • the target times T ⁇ or the current time T are saved by the computer that has read the data.
  • step 7 actual actual times, which provide information, for example, about actual arrival times and departure times of the vehicles F n , are read in by the computer performing the method and stored in a memory of the computer.
  • a driving matrix FM is created on the basis of the given route network and possible requirements with regard to the sequence in which the vehicles F n travel on individual route sections of the route network.
  • a determination order EO is determined from the driving matrix FM 2.
  • the determination rules EO determine the order in which the forecasts for the individual vehicles F n and the individual stops k, which are determined below, are created.
  • Predicted delay times are calculated for all vehicles F n and for all stops k at which vehicles F n will stop determined 3.
  • a target function ⁇ is set up 4, which is minimized with a gradient descent method 5.
  • new control values M £ are determined for each vehicle F n and each stop k when the target function ⁇ is minimized.
  • the new control values M are used to control the individual vehicles F n 6.
  • FIGS. 2 and 3 and FIGS. 4 and 5 each show an exemplary embodiment which describes in a simplified form the setting up of the driving matrix FM and the determination order EO.
  • the driving matrix FM is made up of cells, with each cell representing a double-indexed object. Each cell is indicated by the vehicle F n and by the respective stop k.
  • each cell i.e. each object:
  • the cell state is new in the state if the vehicle F n has not yet arrived at the respective stop k.
  • the state has arrived if the vehicle F n has arrived at the respective stop k, but has not yet left.
  • the state has passed if the vehicle F n has left the stop k,
  • FIG. 3 describes the driving matrix FM for the route network shown in FIG. 2.
  • One row of the driving matrix FM corresponds to the driving route of a vehicle F n and one column of the driving matrix FM corresponds to one stop k.
  • the right neighbor of a cell thus corresponds to the next stop k + 1 of the respective vehicle F n . This is symbolized by an arrow starting from the stop k to the next stop k + 1 of the vehicle F n .
  • the lower neighbor of a cell in a column stands for a stop of the following vehicle F n + ⁇ in the stop k. In the exemplary embodiment shown in FIG. 3, this means that the fact that vehicle F2 regularly follows vehicle Fi is indicated by an arrow from the cell le of the vehicle Fi is indicated on the respective cell of the same column in the row of the vehicle F2.
  • FIGS. 4 and 5 A second exemplary embodiment with a somewhat more complex route network, which is shown in FIG. 4, is described in FIGS. 4 and 5.
  • the driving matrix FM resulting from the route network and the timetables is shown in FIG. 5.
  • the rules for forming the driving matrix FM and the causal dependencies correspond to those described above.
  • the procedure for forming the driving matrix FM can be expanded as desired from any given route network to any number of stops k and any number of vehicles F n .
  • the determination rules EO for the cells of the driving matrix FM in the current forecast period are formed in such a way that as much known information as possible, for example the actual arrival and departure times of the vehicles F n entered from the stops k when determining prognoses Departure times E (Z V ) must be taken into account.
  • the determination order EO is thus a total order that is compatible with the semi-order specified by the arrows from the driving matrix FM.
  • Z ⁇ _ ⁇ denotes the departure time of the vehicle F n at the previous stop k-1, F £ a random travel time of the vehicle F n between the previous stop k-1 and the stop k, and H £ a stop time of the drive ⁇ stuff F n at the stop k.
  • Departure times z £ L denote the departure times of the last vehicle n preceding the vehicle F n of a line from the set L 'at the stop k, with which the passengers were able to travel in order to get to their respective destination.
  • a first possibility is to estimate the passenger density c £ at the beginning of the process on the basis of empirical values and to assume it to be constant 61.
  • Another possibility is to change the passenger density CJ? to be determined empirically during operation of the route network 62.
  • a third possibility consists in determining the passenger density c £ based on the driving behavior of the respective vehicle F n , by inferring from the driving behavior of the vehicle F n the total mass and thus also the payload of the vehicle, from which for each Stop k can be concluded on the passenger density C ⁇ 63.
  • a further possibility for forming the stopping time H £ is, for example, taking into account the opening and closing times of the doors, as well as taking into account the time that the disembarking passengers of the vehicle F n require at the stop k.
  • H £ to + t s + C £ H P £ + c £ A ⁇ £ A (5).
  • An entry constant c £ and the exit constant C can also be formed in the three different types 61, 62, 63 described above, as can the passenger density C ⁇ (see FIG. 6).
  • Regulation 70 of the vehicle F n can take place in various ways, as shown for example in FIG. 7.
  • the vehicle F n can change its speed during travel in accordance with the respective control value M ⁇ be changed. It can therefore be braked and accelerated to a certain extent 71.
  • the predicted departure times E (Z V ) can be determined
  • the determination rules EO must be updated as a function of the newly arrived process information. For example, all new actually known arrival and departure times of the vehicles F n must be entered in the elements belonging to the individual cells be assigned to the FM driving matrix.
  • E (.) denotes a statistical expected value for the respective quantities listed in brackets.
  • the target function ⁇ is set depending on the specific requirements placed on the respective control system:
  • the weighting of the individual summands depends on the specific application and is specified at the beginning of the process taking into account the specific applications.
  • first weight factors ⁇ ⁇ describe the influence of the predicted delay times E (V £), a second weighting factor p describes the type of influence of the predicted delay times E ⁇ V ⁇ 1 ),
  • a third weighting factor ß weights the influence of an expected maximum delay maxE ⁇ V ⁇ 1 ) on the target function n, k tion ⁇ , that is, the influence that a single, namely the maximum, delay of a vehicle on the entire
  • a fifth weight factor ⁇ describes the type of influence of the expected distance of each
  • a sixth weight factor ⁇ denotes the influence of the
  • Control values for the target function ⁇ that is to say the sixth weight factor ⁇ can, by appropriate dimensioning, prevent new control values M ⁇ being determined to a great extent, although hardly any control is required.
  • the term for taking further optimization criteria into account can include, for example, aspects of peak load avoidance, explicitly specified follow-up times or energy-saving measures.
  • An example is considered with a total of 13 vehicles Fn and any number of stops, where in In this example, a forecast of the individual times for 30 stops for each vehicle F n is determined.
  • the first weight factor ⁇ ⁇ is, for example, the value
  • the second weight factor p is also assigned the value 1, for example.
  • the third weight factor ⁇ results, for example, from a product of the total number of vehicles Fn and the number of stops for which a prognosis is to be determined.
  • the third weight factor ß is:
  • the third weight factor ß is advantageously z in this case.
  • the first weighting factors become ⁇ ⁇ , for example. assigned the value 1 or the value 0.
  • the second weight factor p is also z. B. the value 1 assigned.
  • the target times T ⁇ for example, to assign the current time and the predicted delay time to be determined on this assumption.
  • the third weight factor ⁇ is also determined, for example, in the manner described above.
  • the third weight factor ß for the example that no schedule is given also results in:
  • the fourth weighting factor ⁇ £ is e.g. B. assigned the numerical value 800,000.
  • the fifth weight factor ⁇ is z. For example, the value 0.02 is assigned, the fifth weighting factor ⁇ resulting, for example, from the reciprocal of an average time interval between the vehicles F n from one another.
  • a value in the range between 1 and 20 is assigned to the sixth weight factor ⁇ , for example, a larger value being advantageously chosen for the sixth weight factor ⁇ if it is to be expected that the system will not experience excessive faults. However, if it is to be expected that faults will occur, a smaller value for the sixth weighting factor ⁇ is advantageously chosen.
  • the target function ⁇ is minimized with a gradient descent method, advantageously in the reverse order of the determination order EO.
  • the gradient descent method delivers new control values M £, which for the control of the vehicles F n in a further one
  • Step can be used.
  • a gradient descent method that is, as a method for calculating the gradient, this can be done, for example, in (D. Rumelhart, Parallel Distributed Processing, Bradford Book ⁇ , MIT Pre ⁇ , Cambridge, Ma ⁇ achu ⁇ etts, ISBN 0-262-68053-X, p. 318 to 362, 1987) can be used.
  • the framework conditions can consist, for example, in the determination of train sequences, in accordance with the optimization of the regulations of the vehicles F n, for example at intersection points of the route network, in that, contrary to the original order, instructions of the vehicles F n as to how they have to travel the route sections be changed.
  • Connection relationships can also be included in the regulations of vehicles F n . This could lead, for example, to a vehicle F n waiting for a delayed vehicle that also approaches stop k to wait for it
  • a framework condition in the avoidance of tunnel stops that is to say of additional stops in a tunnel, can be taken into account in which, for example, the vehicle F n is stopped in a stop before a tunnel if a stop inside the tunnel would otherwise be unavoidable.
  • the controller reports possible conflicts, which it has determined by determining the predicted delay times E (V £ J), to a control center, which then possibly changes target times ⁇ £ or driving sequences of the individual vehicles F n , communicates this to the controller, which then creates a new driving matrix FM and a new determination order EO on the basis of the data newly transmitted by the control center, and then in turn determines new control values M £ therefrom.
  • the objective function ⁇ can additionally be provided to smooth the non-differentiable parts of the objective function ⁇ with a smoothing function that reflects the course of the non-differentiable part of the objective function ⁇ approximates.
  • all functions can be used which have smoothing properties and which approximate the indistinguishable position sufficiently precisely for the application.
  • Another way of dealing with the problem of the non-differentiability of the objective function ⁇ is to use a one-sided differential quotient of the objective function ⁇ at these points.

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Train Traffic Observation, Control, And Security (AREA)

Abstract

Les temps de retard prévisibles (E(V<n>k)) sont calculés pour les véhicules guidés (Fn) sur la base des itinéraires et trajets prédéterminés. On utilise un procédé de descente de gradient pour réduire une fonction cible ( PSI ), laquelle quantifie différents aspects des causes de retard ou des aspects qui conduisent à un besoin de régulation des différents véhicules (Fn). Le procédé de descente de gradient fournit des valeurs de régulation (M<n>k) qui permettent de réguler les différents véhicules (Fn).
EP96934350A 1995-09-07 1996-08-08 Procede de regulation de vehicules guides Withdrawn EP0848677A2 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE19533128 1995-09-07
DE19533128 1995-09-07
PCT/DE1996/001496 WO1997009217A2 (fr) 1995-09-07 1996-08-08 Procede de regulation de vehicules guides

Publications (1)

Publication Number Publication Date
EP0848677A2 true EP0848677A2 (fr) 1998-06-24

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
EP96934350A Withdrawn EP0848677A2 (fr) 1995-09-07 1996-08-08 Procede de regulation de vehicules guides

Country Status (5)

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US (1) US6141607A (fr)
EP (1) EP0848677A2 (fr)
AR (1) AR003517A1 (fr)
AU (1) AU7277396A (fr)
WO (1) WO1997009217A2 (fr)

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EP1573578B1 (fr) * 2002-12-20 2010-03-17 Ansaldo STS USA, Inc. Procede et systeme d'optimisation dynamique d'une planification de trafic
US8612071B2 (en) * 2009-10-23 2013-12-17 Integrated Transportation Technologies, L.L.C. Synchronized express and local trains for urban commuter rail systems
DE102011078449A1 (de) * 2011-06-30 2012-08-23 Siemens Ag Verfahren zur Fahrkurvenoptimierung für Schienenfahrzeuge
DE102011078447A1 (de) * 2011-06-30 2012-08-23 Siemens Aktiengesellschaft Verfahren zur Fahrkurvenoptimierung für Schienenfahrzeuge
DE102011078451A1 (de) * 2011-06-30 2012-08-23 Siemens Ag Verfahren zur Fahrkurvenoptimierung für Schienenfahrzeuge
DE102011081993A1 (de) * 2011-09-01 2013-03-07 Siemens Aktiengesellschaft Haltezeitberechnungsmodul
DE102011081995A1 (de) * 2011-09-01 2012-10-25 Siemens Ag Fahrtoptimierungsmodul
DE102011121162A1 (de) * 2011-12-14 2013-06-20 Siemens Aktiengesellschaft Verfahren zum optimierten Betreiben eines elektrisch angetriebenen Schienenfahrzeugs auf einer vorgegebenen Strecke
US20150286936A1 (en) * 2012-10-17 2015-10-08 Hitachi, Ltd. Transportation analysis system
FR3003224B1 (fr) * 2013-03-15 2015-04-17 Alstom Transport Sa Procede de gestion du trafic le long d'une ligne de metro automatique ; systeme associe
JP6116512B2 (ja) * 2014-03-25 2017-04-19 株式会社日立製作所 自動列車運転システム、列車運転支援システム及び列車運行管理システム
US10279823B2 (en) * 2016-08-08 2019-05-07 General Electric Company System for controlling or monitoring a vehicle system along a route

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US4179739A (en) * 1978-02-13 1979-12-18 Virnot Alain D Memory controlled process for railraod traffic management
GB2263993B (en) * 1992-02-06 1995-03-22 Westinghouse Brake & Signal Regulating a railway vehicle
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US5623413A (en) * 1994-09-01 1997-04-22 Harris Corporation Scheduling system and method
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Also Published As

Publication number Publication date
WO1997009217A3 (fr) 1997-04-03
US6141607A (en) 2000-10-31
AR003517A1 (es) 1998-08-05
WO1997009217A2 (fr) 1997-03-13
AU7277396A (en) 1997-03-27

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