EP1032927B1 - Method for predicting a parameter representing the state of a system, especially a traffic parameter representing the state of a traffic network - Google Patents
Method for predicting a parameter representing the state of a system, especially a traffic parameter representing the state of a traffic network Download PDFInfo
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- EP1032927B1 EP1032927B1 EP98958804A EP98958804A EP1032927B1 EP 1032927 B1 EP1032927 B1 EP 1032927B1 EP 98958804 A EP98958804 A EP 98958804A EP 98958804 A EP98958804 A EP 98958804A EP 1032927 B1 EP1032927 B1 EP 1032927B1
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- the invention relates to a method for predicting the state of a system representing parameters, in particular one the state of a Traffic network representing traffic parameters.
- a forecast of a traffic network condition Traffic parameters for a future time can be considered This parameter takes place over time.
- the periodic courses of the Traffic parameters also called aisle lines, can be derived from traffic data this traffic parameter at different times through statistical Compression can be obtained.
- a curve (i.e. a course) of one Traffic parameters for example, the course during the time of day certain day of the week, during a week and / or during the year. History of traffic parameters compressed and stored as curve lines can be provided with selection characteristics so that a forecast can be made using Compare, for example, the current situation with at least one Selection feature of at least one curve is possible. It is problematic Among other things, that the current situation regarding a selection parameter for a Hydrograph not sufficiently reliable on the future to be forecast The course of the traffic parameter represented by this curve can be concluded.
- the object of the present invention is the most efficient optimization of forecasts, in particular traffic forecasts.
- the object is achieved by the method of independent claim 1.
- a method according to the invention optimizes forecasts of parameters, in particular traffic parameters.
- a parameter of a system in particular a traffic parameter of a traffic network, can be predicted with high quality on the basis of data relating to a second parameter of the system and at least one corridor. This is particularly advantageous in cases in which the future course of a traffic parameter to be predicted can be better concluded on the basis of current values of another traffic parameter than on the basis of the current values of the first traffic parameter.
- Such a method could also be used as coupled melting (statistical Condensing actual courses of parameters) and probabilistic Selection.
- To move lines (courses) of traffic parameters received actual courses are examined and together with Selection characteristics (for example, actual values or trends for certain Times). It also examines the dependencies between different traffic parameters exist to make a forecast of a first one Allow parameters based on data to a second parameter.
- the fixed or time-dependent strength of couplings is preferred in each case At least two parameters are examined and saved.
- a reduction in the weighting of a coupling is also particularly advantageous between two or more parameters over time, so outdated couplings automatically weaken and / or suppress. In particular, this can take place if the volume of curve data becomes very large over time.
- a probabilistic selection of a curve can be to make a prognosis of a parameter for a future point in time based on data on a other parameters at the current time for the selection of a curve Forecast the likelihood of taking that into account due to a measurement of the second parameter, a certain curve shows a good prognosis for the first Allows parameters.
- This likelihood can be referred to as the baseline Key figure for the coupling of two quantities can be stored and for forecasting be retrieved.
- the probability can be in addition to the coupling strength of two Parameters also the distance of the measurement of the second parameter from one to the other
- the prognosis used the curve of the second parameter or another Take the curve into account.
- a self-correction of the chart base is preferably carried out by carrying one Error curve, in which deviations from predicted courses of actual courses for the correction of hydrographs are taken into account.
- a continuous correction of the key figures is also necessary for self-correction Coupling strength appropriate for at least two parameters; in particular large deviations from actual values to predicted values can to weaken, slight deviations of the actual values from the Predicted values can lead to strengthening of a coupling.
- the method can in particular be implemented as a program in a traffic control center become;
- a database with corridors can be found (Courses of traffic-related or other parameters) and / or a Database with key figures for coupling at least two parameters include.
- traffic data 1 of floating cars FCD
- Traffic data 2 from above-ground detectors SES data
- traffic data 3 of induction loop data VIZ
- one of the two in box 4 curves 5, 6 of a traffic parameter shown as examples.
- step 5 the courses (5,6 etc.) of FCD data 1, SES data 2, VIZ data 3 coupled melted, so taking into account couplings Chart lines, chart-related selection features and couplings between Figures representing traffic parameters are statistically condensed and in one Forecast database stored.
- the course of the number of Cars in a route section on a working day the course of the number of Cars on a section at the weekend, the history of the number of trucks a section on a working day, the history of trucks on a section a Sunday each with its own curve (chronological progression on one Day of the week at one position) are statistically summarized and with Selection characteristics. Selection characteristics can, for example the number of cars at a given time, the number of trucks at one certain time etc.
- Selection characteristics are at least one in each case or possibly also assigned to several curve lines. If currently a selection feature or several selection characteristics of a curve are fulfilled, for example if currently (early in the morning) the number of trucks is above a certain value it can be concluded that a certain curve (truck / working day) is current is being followed. This can be a forecast for the data measured associated traffic parameters at a future time or according to the invention for a traffic parameter not assigned to the measured data future time will be created. That curve of a traffic parameter, which is most likely to be the future based on measured current data Represents the course of a traffic parameter, which is also selected as probabilistic selection can be called.
- Figure 2 shows an example of a probabilistic selection. Lying in the early morning Traffic data on the current number of cars and the current number of trucks in ahead of a section of road. The number of cars in a section of road for a future point in time, namely late in the morning, is to be forecast. by virtue of the current (early morning) number of cars, this is not possible because the Movement lines of cars hardly work on weekdays and early in the morning on weekends differ. On the other hand, the number clearly differs early in the morning of trucks in a curve for a weekday and a curve for one Sunday course.
- the coupling can be taken into account in binary or quantized form. If several The most likely pathway can be selected.
- the method was developed to forecast traffic parameters.
- another parameter can also be predicted according to the invention. For example from the morning car flow to the midday pollutant concentration be closed etc.
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Abstract
Description
Die Erfindung betrifft ein Verfahren zur Prognose eines den Zustand eines Systems repräsentierenden Parameters, insbesondere eines den Zustand eines Verkehrsnetzes repräsentierenden Verkehrsparameters.The invention relates to a method for predicting the state of a system representing parameters, in particular one the state of a Traffic network representing traffic parameters.
Eine Prognose eines den Zustand eines Verkehrsnetzes betreffenden Verkehrsparameters für einen künftigen Zeitpunkt kann unter Berücksichtigung zeitperiodischer Verläufe dieses Parameters erfolgen. Die periodischen Verläufe des Verkehrsparameters, auch bezeichnet als Ganglinien, können aus Verkehrsdaten zu diesem Verkehrsparameter zu unterschiedlichen Zeitpunkten durch statistische Verdichtung gewonnen werden. Eine Ganglinie (also ein Verlauf) eines Verkehrsparameters kann beispielsweise der Verlauf während der Tageszeit eines bestimmten Wochentages, während einer Woche oder/und während des Jahres sein. Als Ganglinien komprimierte und gespeicherte Verläufe von Verkehrsparametern können mit Selektionsmerkmalen versehen werden, so daß eine Prognose durch Vergleich beispielsweise der aktuellen Situation mit mindestens einem Selektionsmerkmal mindestens einer Ganglinie möglich ist. Problematisch ist dabei u.a., daß die aktuelle Situation hinsichtlich eines Selektionsparameters zu einer Ganglinie nicht hinreichend zuverlässig auf den künftigen, zu prognostizierenden Verlauf des durch diese Ganglinie repräsentierten Verkehrsparameters schließen läßt.A forecast of a traffic network condition Traffic parameters for a future time can be considered This parameter takes place over time. The periodic courses of the Traffic parameters, also called aisle lines, can be derived from traffic data this traffic parameter at different times through statistical Compression can be obtained. A curve (i.e. a course) of one Traffic parameters, for example, the course during the time of day certain day of the week, during a week and / or during the year. History of traffic parameters compressed and stored as curve lines can be provided with selection characteristics so that a forecast can be made using Compare, for example, the current situation with at least one Selection feature of at least one curve is possible. It is problematic Among other things, that the current situation regarding a selection parameter for a Hydrograph not sufficiently reliable on the future to be forecast The course of the traffic parameter represented by this curve can be concluded.
Nächstliegender Stand der Technik ist die Literaturstelle "IOKIBE T ET AL: "TRAFFIC PREDICTION METHOD BY FUZZY LOGIC" PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, SAN FRANCISCO", MAR. 28 - APR. 1, 1993, Bd. 2, Nr. CONF. 2, 29. März 1993, Seiten 673-678. Daraus ist ein Verfahren zur Prognose eines den Zustand eines Systems repräsentierenden Parameters bekannt, welches sich wie die Anmeldung auch Ganglinien bedient, die ermittelte Verläufe des Parameters zu verschiedenen Tagen in der Vergangenheit beinhalten. Die Ganglinien werden dabei mittels eines Fuzzy-Logic Verfahrens jeweils an aktuelle Abweichungen des Parameters angepaßt.. D1 arbeitet allerdings mit nur einem Parameter, dem aktuellen gezählten Verkehr als ganzes ("Traffic counter", Fig. 2). The closest prior art is the literature reference "IOKIBE T ET AL:" TRAFFIC PREDICTION METHOD BY FUZZY LOGIC "PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, SAN FRANCISCO ", MAR. 28 - APR. 1, 1993, Vol. 2, No. CONF. 2, March 29, 1993, pages 673-678 Method for forecasting a parameter which represents the state of a system and which is known just like the registration also serves the curves, the determined course of the parameter on different days in of the past. The flow lines are displayed using a fuzzy logic process current deviations of the parameter adjusted. D1 works with only one parameter, however current traffic counted as a whole ("Traffic counter", Fig. 2).
Aufgabe der vorliegenden Erfindung ist eine möglichst effiziente Optimierung von
Prognosen, insbesondere Verkehrsprognosen. Die Aufgabe wird durch das Verfahren
des unabhängigen Anspruchs 1 gelöst.
Ein erfindungsgemäßes Verfahren optimiert Prognosen von Parametern, insbesondere
Verkehrsparametern. Dabei kann ein Parameter eines Systems, insbesondere ein
Verkehrsparameter eines Verkehrsnetzes, aufgrund von einen zweiten Parameter des
Systems betreffenden Daten und mindestens einer Ganglinie qualitativ hochwertig
prognostiziert werden. Dies ist insbesondere in Fällen von Vorteil, in welchen auf den
künftigen zu prognostizierenden Verlauf eines Verkehrsparameters aufgrund aktueller
Werte eines anderen Verkehrsparameters besser zu schließen ist als aufgrund der
aktuellen Werte des ersteren Verkehrsparameters. Wenn beispielsweise am frühen
Morgen eine Prognose für die PKW-Reisezeiten am späten Morgen erstellt werden
soll, sind die aktuellen PKW-Reisezeiten ungeeignet für eine Prognose der künftigen
PKW-Reisezeiten, da frühmorgens wochentags wie am Wochenende noch kaum
PKWs fahren, was jedoch keine Aussage über am späten Morgen fahrende PKWs ist.
Aus einem starken LKW-Fluß am frühen Morgen kann jedoch beispielsweise
abgelesen werden, daß heute ein werktag-ähnlicher Verkehr stattfinden wird, so daß
eine Prognose von PKW-Reisezeiten am späten Morgen aufgrund des LKW-Flusses
am frühen Morgen besser möglich ist als aufgrund von PKW-Reisezeiten am frühen
Morgen.The object of the present invention is the most efficient optimization of forecasts, in particular traffic forecasts. The object is achieved by the method of
A method according to the invention optimizes forecasts of parameters, in particular traffic parameters. A parameter of a system, in particular a traffic parameter of a traffic network, can be predicted with high quality on the basis of data relating to a second parameter of the system and at least one corridor. This is particularly advantageous in cases in which the future course of a traffic parameter to be predicted can be better concluded on the basis of current values of another traffic parameter than on the basis of the current values of the first traffic parameter. If, for example, a forecast for the car travel times in the late morning is to be made in the early morning, the current car travel times are unsuitable for a forecast of the future car travel times, since hardly any cars drive early in the morning on weekdays or on weekends, but this is not a statement about cars driving late in the morning. However, it can be seen from a strong truck flow in the early morning, for example, that traffic similar to a working day will take place today, so that a forecast of car travel times in the late morning is better possible due to the truck flow in the early morning than due to Car travel times in the early morning.
Ein derartiges Verfahren könnte auch als gekoppeltes Einschmelzen (statistisches Verdichten von tatsächlichen Verläufen von Parametern) und probabilistische Selektion bezeichnet werden. Um Ganglinien (Verläufe) von Verkehrsparametern zu erhalten, werden tatsächliche Verläufe untersucht und zusammen mit Selektionsmerkmalen (beispielsweise Istwerte oder Verläufe zu bestimmten Zeitpunkten) gespeichert. Ferner wird untersucht, welche Abhängigkeiten zwischen verschiedenen Verkehrsparametem bestehen, um eine Prognose eines ersten Parameters aufgrund von Daten zu einem zweiten Parameter zu ermöglichen. Vorzugsweise wird dabei die feste oder zeitabhängige Stärke von Kopplungen jeweils mindestens zweier Parameter untersucht und mit abgespeichert. Auch ist eine Aktualisierung der die Kopplungsstärke mindestens zweier Parameter repräsentierenden Kennzahl aufgrund aktueller tatsächlicher Verläufe der Parameter und/oder der Qualität von Prognosen möglich. In den gespeicherten Daten zu Ganglinien sind Kopplungen verschiedener Parameter mit berücksichtigt. Such a method could also be used as coupled melting (statistical Condensing actual courses of parameters) and probabilistic Selection. To move lines (courses) of traffic parameters received, actual courses are examined and together with Selection characteristics (for example, actual values or trends for certain Times). It also examines the dependencies between different traffic parameters exist to make a forecast of a first one Allow parameters based on data to a second parameter. The fixed or time-dependent strength of couplings is preferred in each case At least two parameters are examined and saved. Is also one Update the coupling strength of at least two parameters representative key figure based on current actual trends of the parameters and / or the quality of forecasts. In the stored data too Flow lines are the coupling of various parameters.
Vorteilhaft ist dabei ferner eine Berücksichtigung und Speicherung der Varianz (oder Variabilität) der zu einer Ganglinie verdichteten Verläufe eines Parameters und die Berücksichtigung der Varianz (oder Variabilität) bei der Prognose eines Parameters.It is also advantageous to take into account and store the variance (or Variability) of the curves of a parameter condensed into a curve and the Consideration of variance (or variability) when predicting a parameter.
Vorteilhaft ist ferner insbesondere eine Verringerung der Gewichtung einer Kopplung zwischen zwei oder mehr Parametern im Laufe der Zeit, um so veraltete Kopplungen automatisch zu schwächen und/oder zu unterdrücken. Dies kann insbesondere erfolgen, falls die Ganglinien-Datenmenge im Laufe der Zeit sehr groß wird.A reduction in the weighting of a coupling is also particularly advantageous between two or more parameters over time, so outdated couplings automatically weaken and / or suppress. In particular, this can take place if the volume of curve data becomes very large over time.
Eine probabilistische Selektion einer Ganglinie kann darin bestehen, zur Prognose eines Parameters für einen künftigen Zeitpunkt aufgrund von Daten zu einem anderen Parameter zum aktuellen Zeitpunkt für die Selektion einer Ganglinie zur Prognose die Wahrscheinlichkeit zu berücksichtigen, daß aufgrund einer Messung des zweiten Parameters eine bestimmte Ganglinie eine gute Prognose für den ersten Parameter ermöglicht. Diese Wahrscheinlichkeit kann zur Ganglinienbasis als Kennzahl für die Kopplung zweier Größen abgespeichert sein und zur Prognose abgerufen werden. Die Wahrscheinlichkeit kann neben der Kopplungsstärke von zwei Parametern auch den Abstand der Messung des zweiten Parameters von einer zur Prognose verwendeten Ganglinie des zweiten Parameters oder einer anderen Ganglinie berücksichtigen.A probabilistic selection of a curve can be to make a prognosis of a parameter for a future point in time based on data on a other parameters at the current time for the selection of a curve Forecast the likelihood of taking that into account due to a measurement of the second parameter, a certain curve shows a good prognosis for the first Allows parameters. This likelihood can be referred to as the baseline Key figure for the coupling of two quantities can be stored and for forecasting be retrieved. The probability can be in addition to the coupling strength of two Parameters also the distance of the measurement of the second parameter from one to the other The prognosis used the curve of the second parameter or another Take the curve into account.
Eine Selbstkorrektur der Ganglinienbasis erfolgt vorzugsweise durch Mitführen einer Fehlerganglinie, in welcher Abweichungen von prognostizierten Verläufen von tatsächlichen Verläufen zur Korrektur von Ganglinien berücksichtigt werden.A self-correction of the chart base is preferably carried out by carrying one Error curve, in which deviations from predicted courses of actual courses for the correction of hydrographs are taken into account.
Zur Selbstkorrektur ist ferner eine laufende Korrektur der Kennzahlen zur Kopplungsstärke jeweils mindestens zweier Parameter zweckmäßig; insbesondere große Abweichungen von tatsächlichen Werten zu prognostizierten Werten können zur Abschwächung, geringe Abweichungen der tatsächlichen Werte von den prognostizierten Werten können zur Stärkung einer Kopplung führen.A continuous correction of the key figures is also necessary for self-correction Coupling strength appropriate for at least two parameters; in particular large deviations from actual values to predicted values can to weaken, slight deviations of the actual values from the Predicted values can lead to strengthening of a coupling.
Zweckmäßig ist insbesondere eine Realisierung als neuronales Netz. Implementation as a neural network is particularly expedient.
Das Verfahren kann insbesondere als Programm in einer Verkehrszentrale realisiert werden; in der Verkehrszentrale kann insbesondere eine Datenbank mit Ganglinien (Verläufen von verkehrstechnischen oder anderen Parametern) und/oder eine Datenbank mit Kennzahlen zur Kopplung jeweils mindestens zweier Parameter umfassen.The method can in particular be implemented as a program in a traffic control center become; In the traffic control center, in particular, a database with corridors can be found (Courses of traffic-related or other parameters) and / or a Database with key figures for coupling at least two parameters include.
Weitere Merkmale und Vorteile der Erfindung ergeben sich aus der nachfolgenden Beschreibung eines Ausführungsbeispiels anhand der Zeichnung. Dabei zeigt:
- Fig. 1
- als Blockschaltbild die statistische Verdichtung (Einschmelzen) von Verkehrsdaten zu Ganglinien und Kopplungskennzahlen für eine Prognosedatenbank sowie eine probabilistische Selektion zur Erstellung von Verkehrsprognosen,
- Fig. 2
- ein Beispiel einer Prognose eines Parameters aufgrund von aktuellen Daten zu einem anderen Parameter.
- Fig. 1
- as a block diagram the statistical compression (melting) of traffic data into corridors and coupling indicators for a forecast database as well as a probabilistic selection for the creation of traffic forecasts,
- Fig. 2
- an example of a forecast of a parameter based on current data for another parameter.
Im in Figur 1 gezeigten Beispiel werden Verkehrsdaten 1 von Floating-cars (FCD),
Verkehrsdaten 2 von above-ground-Detektoren (SES-Daten) und Verkehrsdaten 3
von Induktionsschleifen-Daten (VIZ) an mehreren Orten zu mehreren Zeitpunkten
gemessen, wobei sich an einem Ort beispielsweise einer der beiden im Kasten 4
beispielhaft dargestellten Verläufe 5, 6 eines Verkehrsparameters ergeben kann.In the example shown in FIG. 1,
Im Schritt 5 werden die Verläufe (5,6 usw.) von FCD-Daten 1, SES-Daten 2, VIZ-Daten
3 gekoppelt eingeschmolzen, also unter Berücksichtigung von Kopplungen zu
Ganglinien, ganglinienbezogenen Selektionsmerkmalen und Kopplungen zwischen
Verkehrsparametern repräsentierenden Kennzahlen statistisch verdichtet und in einer
Prognose-Datenbank abgelegt. Beispielsweise kann der Verlauf der Anzahl von
PKWs in einem Streckenabschnitt an einem Werktag, der Verlauf der Anzahl von
PKWs an einem Abschnitt am Wochenende, der Verlauf der Anzahl von LKWs an
einem Abschnitt an einem Werktag, der Verlauf von LKWs an einem Abschnitt an
einem Sonntag jeweils zu einer eigenen Ganglinie (zeitlicher Verlauf an einem
Wochentag an einer Position) statistisch verdichtet werden und mit
Selektionsmerkmalen versehen werden. Selektionsmerkmale können beispielsweise
die Anzahl von PKWs zu einer bestimmen Uhrzeit, die Anzahl von LKWs zu einer
bestimmten Uhrzeit etc. sein. Selektionsmerkmale sind jeweils zumindestens einer
oder evtl. auch mehreren Ganglinien zugeordnet. Wenn akutell ein Selektionsmerkmal
oder mehrere Selektionsmerkmale einer Ganglinie erfüllt sind, beispielsweise wenn
aktuell (frühmorgens) die Anzahl der LKWs über einem bestimmten Wert liegt, kann
darauf geschlossen werden, daß eine bestimmte Ganglinie (LKW/werktag) aktuell
verfolgt wird. Hieraus kann eine Prognose für den zu den gemessenen Daten
gehörenden Verkehrsparameter zu einem künftigen Zeitpunkt oder erfindungsgemäß
für einen nicht zu den gemessenen Daten zugeordneten Verkehrsparameter zu einem
künftigen Zeitpunkt erstellt werden. Diejenige Ganglinie eines Verkehrsparameters,
welche aufgrund gemessener aktueller Daten am wahrscheinlichsten den künftigen
Verlauf eines Verkehrsparameters repräsentiert, wird ausgewählt, was auch als
probabilistische Selektion bezeichnet werden kann.In
Figur 2 zeigt ein Beispiel einer probabilistischen Selektion. Am frühen Morgen liegen Verkehrsdaten zur aktuellen Anzahl von PKWs und zur aktuellen Anzahl von LKWs in einem Straßenabschnitt vor. Die Anzahl von PKWs in einem Straßenabschnitt für einen künftigen Zeitpunkt, nämlich spätmorgens, soll prognostiziert werden. Aufgrund der aktuellen (frühmorgens) Anzahl von PKWs ist dies nicht möglich, da sich die Ganglinien von PKWs werktags und am Wochenende frühmorgens kaum unterscheiden. Hingegen unterscheiden sich bereits frühmorgens deutlich die Anzahl von LKWs in einer Ganglinie für einen Werktags-Verlauf und einer Ganglinie für einen Sonntags-Verlauf. Aufgrund der Zahl von LKWs frühmorgens auf einer Werktags-Ganglinie kann deshalb darauf geschlossen werden, daß sich die Anzahl der PKWs auf einer Werktags-Ganglinie weiterentwickeln wird und daß deshalb spätmorgens die Anzahl der PKWs auf der PKW-Ganglinie für spätmorgens zutreffend ist. Die Kopplung der PKW-Werktags-Ganglinie und der LKW-Werktags-Ganglinie betrifft somit insbesondere das gemeinsame Merkmal "werktags". Dieses Kopplungs-Merkmal muß jedoch nicht grundsätzlich zur Prognose bekannt sein.Figure 2 shows an example of a probabilistic selection. Lying in the early morning Traffic data on the current number of cars and the current number of trucks in ahead of a section of road. The number of cars in a section of road for a future point in time, namely late in the morning, is to be forecast. by virtue of the current (early morning) number of cars, this is not possible because the Movement lines of cars hardly work on weekdays and early in the morning on weekends differ. On the other hand, the number clearly differs early in the morning of trucks in a curve for a weekday and a curve for one Sunday course. Due to the number of trucks on a workday aisle early in the morning can therefore be concluded that the number of cars will continue to develop on a workday schedule and that is why the Number of cars on the car gangway for late morning is applicable. The Coupling of the car workday aisle and the truck workday aisle affects hence in particular the common feature "working days". This coupling feature However, it does not necessarily have to be known for the forecast.
Die Kopplung kann binär oder quantisiert berücksichtigt werden. Wenn mehrere Ganglinien in Frage kommen, kann die wahrscheinlichste ausgewählt werden.The coupling can be taken into account in binary or quantized form. If several The most likely pathway can be selected.
Das Verfahren wurde zur Prognose von Verkehrsparametern entwickelt. Jedoch ist auch ein anderer Parameter erfindungsgemäß prognostizierbar. Beispielsweise kann aus dem morgendlichen PKW-Fluß auf die mittägliche Schadstoff-Konzentration geschlossen werden etc.The method was developed to forecast traffic parameters. However is another parameter can also be predicted according to the invention. For example from the morning car flow to the midday pollutant concentration be closed etc.
Claims (14)
- Method of predicting a first parameter representing the state of a system,
data relating to a second parameter of the system being assigned to at least one of several time variation curves representing a progression in time of the first parameter representing the state of the system,
and the first parameter being forecast for a time in the future on the basis of the data and of the at least one assigned progress line. - Method as in Claim 1,
characterised in that
the system is a traffic network,
the parameters are traffic parameters representing the state of the traffic network,
the data are traffic data relating to a traffic parameter for at least one point in time. - Method as in Claim 1 or 2,
characterised in that
several time variation curves are taken into consideration for the forecast. - Method as in one of the preceding Claims,
characterised in that,
for each assignment of data to a time variation curve, an index number is determined, representing the probability of the correct assignment and/or the accuracy of the approximation to reality of the assignment of the data to this time variation curve. - Method as in Claim 4,
characterised in that,
based on the index number, the weighting of several time variation curves is established in producing the forecast. - Method as in one of the preceding Claims,
characterised in that
an index number (= level of linkage) is established for two parameters on the basis of previous progressions in time of the parameters. - Method as in one of the preceding Claims,
characterised in that
an index number for two parameters is repeatedly updated on the basis each time of up-to-date data on the two parameters. - Method as in Claim 7,
characterised in that,
when an index number is updated, greater consideration is given to more up-to-date data than to older data. - Method as in one of the preceding Claims,
characterised in that
the variance (= variability) of the progressions of parameters compacted into a time variation curve is determined, and is taken into consideration when an index number and/or a forecast is established. - Method as in one of the preceding Claims,
characterised in that
curves whose age since their most recent update is above a threshold value are deleted. - Method as in one of the preceding Claims,
characterised in that
the index number (= level of linkage) is quantified on a more than binary basis, and represents the degree of linkage of two parameters. - Method as in one of the preceding Claims,
characterised in that
the method operates in a neural network. - Method as in one of the preceding Claims,
characterised in that
a time variation curve is determined on the basis of the specific progression in time of a parameter. - Method as in one of the preceding Claims,
characterised in that
data relating to the forecast prepared in a central traffic unit are transmitted to a road user by radio, particularly mobile radio communications, particularly GSM SMS (text messaging).
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19753034A DE19753034A1 (en) | 1997-11-18 | 1997-11-18 | Method for forecasting a parameter representing the state of a system, in particular a traffic parameter representing the state of a traffic network, and device for carrying out the method |
DE19753034 | 1997-11-18 | ||
PCT/DE1998/002932 WO1999026210A1 (en) | 1997-11-18 | 1998-09-25 | Method for predicting a parameter representing the state of a system, especially a traffic parameter representing the state of a traffic network, and a device for carrying out said method |
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Publication Number | Publication Date |
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EP1032927A1 EP1032927A1 (en) | 2000-09-06 |
EP1032927B1 true EP1032927B1 (en) | 2003-03-26 |
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EP98958804A Expired - Lifetime EP1032927B1 (en) | 1997-11-18 | 1998-09-25 | Method for predicting a parameter representing the state of a system, especially a traffic parameter representing the state of a traffic network |
Country Status (4)
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EP (1) | EP1032927B1 (en) |
AT (1) | ATE235729T1 (en) |
DE (2) | DE19753034A1 (en) |
WO (1) | WO1999026210A1 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102568205A (en) * | 2012-01-10 | 2012-07-11 | 吉林大学 | Traffic parameter short-time prediction method based on empirical mode decomposition and classification combination prediction in abnormal state |
Families Citing this family (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE19944075C2 (en) * | 1999-09-14 | 2002-01-31 | Daimler Chrysler Ag | Traffic condition monitoring method for a traffic network with effective bottlenecks |
DE10022812A1 (en) | 2000-05-10 | 2001-11-22 | Daimler Chrysler Ag | Method for determining the traffic situation on the basis of reporting vehicle data for a traffic network with traffic-regulated network nodes |
DE10036789A1 (en) * | 2000-07-28 | 2002-02-07 | Daimler Chrysler Ag | Method for determining the traffic condition in a traffic network with effective bottlenecks |
DE10163505A1 (en) * | 2001-12-21 | 2003-07-17 | Siemens Ag | Procedure for examining a measured variable |
DE10200492B4 (en) * | 2002-01-03 | 2004-02-19 | DDG GESELLSCHAFT FüR VERKEHRSDATEN MBH | Method for self-consistent estimation of predictive travel times when using mobile or stationary detectors to measure experienced travel times |
DE202004021667U1 (en) | 2004-03-16 | 2010-05-12 | Epoq Gmbh | Forecasting device for the evaluation and prediction of stochastic events |
DE102005055245A1 (en) * | 2005-11-19 | 2007-05-31 | Daimlerchrysler Ag | Method for preperation of traffic pattern data base, involves analyzing, evaluating and combining local traffic condition data in vehicle at different temporal and spacial basis modules of traffic pattern |
AT503846B1 (en) * | 2006-07-03 | 2008-07-15 | Hofkirchner Hubertus Mag | Optimized forecast determining method for controlling or regulation of operative system and/or process, involves eliminating contradictory individual forecasts and aggregating remaining individual forecasts into optimized overall forecast |
CN102542801B (en) * | 2011-12-23 | 2014-10-08 | 北京易华录信息技术股份有限公司 | Traffic condition prediction system fused with various traffic data and method |
CN109448361B (en) * | 2018-09-18 | 2021-10-19 | 云南大学 | Resident traffic travel flow prediction system and prediction method thereof |
CN110910659B (en) * | 2019-11-29 | 2021-08-17 | 腾讯云计算(北京)有限责任公司 | Traffic flow prediction method, device, equipment and storage medium |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5539645A (en) * | 1993-11-19 | 1996-07-23 | Philips Electronics North America Corporation | Traffic monitoring system with reduced communications requirements |
DE19604083B4 (en) * | 1995-03-23 | 2006-06-29 | T-Mobile Deutschland Gmbh | Method for the parameterization of vehicle routes in vehicle control and / or information systems |
ES2135134T3 (en) * | 1995-04-28 | 1999-10-16 | Inform Inst Operations Res & M | PROCEDURE FOR THE DETECTION OF DISTURBANCES IN ROLLED TRAFFIC. |
-
1997
- 1997-11-18 DE DE19753034A patent/DE19753034A1/en not_active Withdrawn
-
1998
- 1998-09-25 EP EP98958804A patent/EP1032927B1/en not_active Expired - Lifetime
- 1998-09-25 WO PCT/DE1998/002932 patent/WO1999026210A1/en active IP Right Grant
- 1998-09-25 AT AT98958804T patent/ATE235729T1/en active
- 1998-09-25 DE DE59807678T patent/DE59807678D1/en not_active Expired - Lifetime
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102568205A (en) * | 2012-01-10 | 2012-07-11 | 吉林大学 | Traffic parameter short-time prediction method based on empirical mode decomposition and classification combination prediction in abnormal state |
CN102568205B (en) * | 2012-01-10 | 2013-12-04 | 吉林大学 | Traffic parameter short-time prediction method based on empirical mode decomposition and classification combination prediction in abnormal state |
Also Published As
Publication number | Publication date |
---|---|
WO1999026210A8 (en) | 1999-07-15 |
DE59807678D1 (en) | 2003-04-30 |
ATE235729T1 (en) | 2003-04-15 |
WO1999026210A1 (en) | 1999-05-27 |
EP1032927A1 (en) | 2000-09-06 |
DE19753034A1 (en) | 1999-06-17 |
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