EP1174842B1 - Method to create forecasted traffic data for traffic information - Google Patents
Method to create forecasted traffic data for traffic information Download PDFInfo
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- EP1174842B1 EP1174842B1 EP01250111A EP01250111A EP1174842B1 EP 1174842 B1 EP1174842 B1 EP 1174842B1 EP 01250111 A EP01250111 A EP 01250111A EP 01250111 A EP01250111 A EP 01250111A EP 1174842 B1 EP1174842 B1 EP 1174842B1
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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
Definitions
- the invention relates to a method for generating predicted traffic data for traffic information.
- Traffic conditions can change very quickly due to different influences.
- Resulting traffic disturbances e.g. Traffic jams represent a danger for the following road users.
- Current route announcements or route recommendations based on distance-related travel time losses inform the road users of such disturbances, also for safety reasons to avoid accidents.
- traffic information systems and methods responding to current traffic readings e.g. Speeds and traffic flows at locations of the road network
- traffic information such as e.g. Traffic messages and travel time information generate, with varying speed on changes in the state of traffic, which are reflected in the traffic readings.
- delay times of the measuring systems and the delays in the transmission the measured values to the downstream systems to be processed.
- runtimes and delays occur.
- the timeliness of the generated traffic information can be increased by they are forecasted to some extent. Thus, the inherent system run times and response times can be reduced or even completely compensated.
- the traffic situation is determined from traffic measurements. Consequently, these measured values are to be forecast.
- the forecasting method must work adaptively and be appropriately influenced in order to be able to react accordingly to slowly or rapidly changing conditions. In particular, it must be possible select, evaluate or even correct the respective traffic data source to be used depending on the situation be able to determine the forecast horizon to be achieved, adaptively during the term of the forecasting system and for a follow-up quality control to determine the actually achieved forecast quality continuously updated.
- Hydrographs or historical load curves are averaged over comparable weekdays courses of
- Traffic simulation models eg flow simulation calculations of systems partially Differential equations [Drivers, Helbing, Numerical Simulation of Macroscopic Traffic Equations, CISE 5, 89 (1999)], are used to predict traffic readings regarding locations and times when there are no readings.
- the object of the invention is therefore to solve the above-mentioned problems and to provide a method that can be used on all types of roads, both inside and outside for forecasting current traffic data. It should also be suitable for the creation of travel time forecasts, traffic forecast forecasts, detour recommendations as well as for direct vehicle control and traffic control, as well as for numerous other services.
- the achievable forecasting quality depends on various factors.
- the forecasting method can therefore not work independently of the forecasting quality. It is therefore necessary to determine the quality of forecasting continuously and to make it available to the forecasting method used as a feedback control variable.
- the ongoing measurement of the forecasting quality is also used for continuous quality control in order to determine situations in which the prognosis is not or only insufficiently possible. If the forecast quality decreases suddenly, this is in some cases not only a sign of a sudden change of the inherently chaotic system but of an unforeseeable disturbance on the traffic system, e.g. in the form of an accident.
- the target / actual comparison of the forecast with the current traffic data is made via a similarity measure, which evaluates the current time with a local environment, the deviations of the data from each other. This can be done, for example, via a correlation or a distance metric.
- the local smoothing of the predicted traffic data and the current traffic data is done via a local interpolation.
- the weight function g (u, v) as integration kernel becomes smaller for larger magnitude deviations x - r or t - s and thus ensures the local evaluation of the deviations with respect to x and t. It can be eg a Gaussian function or simply set to 1, which corresponds to a rectangular window.
- the method is extended by a continuous generation of a measure of the prediction quality at the level of the products (traffic reports and travel times) (FIG. 2). These is used as an additional quality control and also as a manipulated variable. This results in a regulated variable forecasting horizon through a double-cascaded indirect or direct control loop. In principle, only the outer control loop can be operated for quality control or evaluation.
- the measurement of the prognosis quality can also be done retrospectively, ie "offline", on the basis of archived measurement data or products (archived traffic reports and travel times) for a quality evaluation in order to determine situations in which the prognosis was not or only insufficiently possible.
- Similarity measures are used for traffic reports.
Abstract
Description
Die Erfindung betrifft ein Verfahren zur Erstellung prognostizierter Verkehrsdaten für Verkehrsinformationen.The invention relates to a method for generating predicted traffic data for traffic information.
Im Straßenverkehr können sich Verkehrszustände aufgrund unterschiedlicher Einflüsse sehr schnell ändem. Resultierende Verkehrsstörungen, z.B. Staus, stellen dabei eine Gefahr für nachfolgende Verkehrsteilnehmer dar. Über aktuelle Verkehrsmeldungen oder auf streckenbezogenen Reisezeitverlusten basierende Routenempfehlungen werden den Verkehrsteilnehmern solche Störungen, auch aus Sicherheitsgründen zur Unfallvermeidung, zur Kenntnis gebracht.In traffic, traffic conditions can change very quickly due to different influences. Resulting traffic disturbances, e.g. Traffic jams represent a danger for the following road users. Current route announcements or route recommendations based on distance-related travel time losses inform the road users of such disturbances, also for safety reasons to avoid accidents.
Nun hat aber jedes System eine inhärente Reaktionszeit. So reagieren auch die Verkehrsinformationssysteme und Verfahren, welche aus aktuellen Verkehrsmeßwerten, z.B. Geschwindigkeiten und Verkehrsflüsse an Orten des Straßenverkehrsnetzes, Verkehrsinformationen wie z.B. Verkehrsmeldungen und Reisezeitinformationen erzeugen, unterschiedlich schnell auf Zustandsänderungen des Verkehrs, welche sich in den Verkehrsmeßwerten abzeichnen. Hinzu kommen noch die Verzögerungszeiten der Meßsysteme sowie die Verzögerungen bei der Übermittlung, der Meßwerte an die zu verarbeitenden nachgeschalteten Systeme. Auch bei der Weitergabe der erzeugten Verkehrsinformationen an die Verkehrsteilnehmer oder Service-Provider treten Laufzeiten und Verzögerungen auf.Now every system has an inherent reaction time. Thus, traffic information systems and methods responding to current traffic readings, e.g. Speeds and traffic flows at locations of the road network, traffic information such as e.g. Traffic messages and travel time information generate, with varying speed on changes in the state of traffic, which are reflected in the traffic readings. In addition there are the delay times of the measuring systems and the delays in the transmission, the measured values to the downstream systems to be processed. Also when passing the generated traffic information to the road users or service providers, runtimes and delays occur.
Folglich ist die Aktualität von Verkehrsinformationen immer eingeschränkt, selbst wenn sie auf Basis völlig aktueller Verkehrsmeßwerte berechnet worden sind. Es besteht daher ein dringender Bedarf an aktuelleren Verkehrsinformationen, als sie heutzutage angeboten werden können.Consequently, the timeliness of traffic information is always limited, even if it has been calculated on the basis of completely up-to-date traffic readings. There is therefore an urgent need for more up-to-date traffic information than can be offered today.
Die Aktualität der erzeugten Verkehrsinformationen kann dadurch gesteigert werden, indem sie bis zu einem gewissen Grad prognostiziert werden. Damit lassen sich die inhärenten Systemlaufzeiten und -reaktionszeiten reduzieren oder sogar völlig kompensieren.The timeliness of the generated traffic information can be increased by they are forecasted to some extent. Thus, the inherent system run times and response times can be reduced or even completely compensated.
Bei der Realisierung, dieser Zielsetzung bestehen folgende Probleme:In realizing this objective, there are the following problems:
Die Verkehrslage wird aus Verkehrsmeßwerten bestimmt. Folglich sind diese Meßwerte zu prognostizieren. Das Prognoseverfahren muß adaptiv arbeiten und geeignet beeinflußt werden, um auf langsam oder auch schnell veränderliche Bedingungen entsprechend reagieren zu können. Insbesondere muß es auch möglich sein,
die jeweils zu verwendenden Verkehrsdatenquelle situationsabhängig auswählen, bewerten oder gar korrigieren zu können,
den zu erzielenden Prognosehorizont geeignet festlegen zu können, und zwar adaptiv während der Laufzeit des Prognosesystems sowie
für eine mitlaufende Qualitätskontrolle die tatsächlich erzielte Prognosegüte fortlaufend aktuell zu bestimmen.The traffic situation is determined from traffic measurements. Consequently, these measured values are to be forecast. The forecasting method must work adaptively and be appropriately influenced in order to be able to react accordingly to slowly or rapidly changing conditions. In particular, it must be possible
select, evaluate or even correct the respective traffic data source to be used depending on the situation
be able to determine the forecast horizon to be achieved, adaptively during the term of the forecasting system and
for a follow-up quality control to determine the actually achieved forecast quality continuously updated.
Desweiteren
soll das Verfahren streckenunabhängig arbeiten, d.h. nicht auf Anschlußstellen usw. angewiesen sein, diese aber bei Bedarf geeignet berücksichtigen.
Sind mehrere physikalische Meßgrößen einzubeziehen, die miteinander verkoppelt sind, z.B. Geschwindigkeiten und Verkehrsflüsse,
sind Verkehrsmeßwerte unterschiedlicher Eigenschaften und Quellen, synchron getaktete und asynchron bzw. ereignisindiziert auftretende, an festen bzw. variablen Orten erfaßte, einzubeziehen und konsistent zu nutzen (Induktionsschleifen, Floatin, Car Data (FCD), stationäre Erfassungssysteme).Furthermore
If the procedure is to work independently of the distance, ie not dependent on connection points, etc., these must be taken into account as appropriate.
Are several physical quantities to be included that are coupled together, eg speeds and flows,
are traffic measurements of different characteristics and sources, synchronously clocked and asynchronously or event-triggered, detected at fixed or variable locations, to include and consistently use (induction loops, Floatin, Car Data (FCD), stationary detection systems).
Ganglinien oder historische Lastkurven, wie sie z.B. auch bei Energieversorgem Verwendung finden, sind über vergleichbare Wochentage gemittelte Verläufe von Verkehrsmeßwerten (v = Geschwindigkeit, d = Verkehrsdichte (Fahrzeuge/Wegstrecke), f = Verkehrsfluß (Fahrzeuge/Zeit). Solche mittleren Funktionen über die Zeit sind aber nur bei gleichen häufig sich wiederholenden Verkehrsereignissen geeignet, um Vorhersagen aufgrund der Historie treffen zu können.Hydrographs or historical load curves, e.g. are also used by energy suppliers, are averaged over comparable weekdays courses of Verkehrsmeßwerten (v = speed, d = traffic density (vehicles / distance), f = traffic flow (vehicles / time)., Such mean functions over time are common only at the same time repetitive traffic events to make predictions on the basis of history.
Verkehrssimulationsmodelle, z.B. Flußsimulationsrechnungen von Systemen partieller Differentialgleichungen [Treiber, Helbing,: Numerical Simulation of Macroscopic Traffic Equations, CISE 5, 89 (1999)], dienen der Prognose von Verkehrsmeßwerten bzgl. Orten und Zeiten, an denen keine Meßwerte vorliegen.Traffic simulation models, eg flow simulation calculations of systems partially Differential equations [Drivers, Helbing, Numerical Simulation of Macroscopic Traffic Equations, CISE 5, 89 (1999)], are used to predict traffic readings regarding locations and times when there are no readings.
Diese bekannten Verfahren alleine bieten keine Lösung für die dargestellten Probleme.These known methods alone do not provide a solution to the problems presented.
Aufgabe der Erfindung ist es daher die vorstehend genannten Probleme zu lösen und ein Verfahren zu schaffen, das auf allen Straßentypen, sowohl inner- als auch außerorts zur Prognose aktueller Verkehrsdaten einsetzbar ist. Es soll auch geeignet sein zur Erstellung von Reisezeitprognosen, Verkehrsmeldungsprognosen, Umleitungsempfehlungen sowie zur direkten Fahrzeugbeeinflussung und Verkehrslenkung, sowie für zahlreiche andere Dienste.The object of the invention is therefore to solve the above-mentioned problems and to provide a method that can be used on all types of roads, both inside and outside for forecasting current traffic data. It should also be suitable for the creation of travel time forecasts, traffic forecast forecasts, detour recommendations as well as for direct vehicle control and traffic control, as well as for numerous other services.
Gelöst wird diese Aufgabe erfindungsgemäß mit den Merkmalen des Patentanspruchs 1. Weitere Ausbildungen ergeben sich aus den Unteransprüchen.This object is achieved according to the invention with the features of claim 1. Further embodiments will become apparent from the dependent claims.
Nachfolgend soll die Erfindung in Zusammenhang mit den Zeichnungen erläutert werden. Dabei zeigt:
- Fig. 1:
- ein Schema eines Systems zur adaptiven Regelung einer Verkehrsprognose durch fortlaufende Bestimmung des Prognosefehlers über eine Rückkopplung mit Verzögerung und Soll-/Istvergleich. δ0 steht dabei für die Impulsfunktion
- Fig. 2
- ein erweitertes System zur adaptiven Regelung einer Verkehrsprognose durch einen zweifach kaskadierten Regelkreis mit zusätzlichem Soll-/Istabgleich auf Produktebene. δ0 steht dabei für die Impulsfunktion.
- Fig. 1:
- a schematic of a system for adaptive control of a traffic forecast by continuously determining the forecast error on a feedback with delay and target / actual comparison. δ 0 stands for the impulse function
- Fig. 2
- an extended system for the adaptive control of a traffic forecast by a double cascaded control loop with additional setpoint / actual adjustment on product level. δ 0 stands for the impulse function.
Je nach dem augenblicklichen Verkehrszustand an einem Ort des Verkehrsnetzes ist eine Prognose mehr oder weniger tragfähig, da das Verkehrsgeschehen sich in kritischen Bereichen des Gesamtsystems abspielen kann, in denen das "System" zunehmend chaotisch reagieren kann. Plötzlich eintretende Unfälle können auch nur schwer vorher gesagt werden. Das Prognosesystem wird in diesen Fällen fehlerhafte Werte liefern.Depending on the current traffic condition at a location of the transport network, a prognosis is more or less sustainable, since the traffic situation can take place in critical areas of the overall system in which the "system" can increasingly react chaotically. Suddenly occurring accidents are difficult to predict. The forecasting system will provide erroneous values in these cases.
Die erreichbare Prognosegüte hängt damit von verschiedenen Faktoren ab. Das Prognoseverfahren kann daher nicht losgelöst von der Prognosegüte arbeiten. Es ist also fortlaufend die Prognosegüte zu bestimmen und dem eingesetzten Prognoseverfahren als rückkoppelnde Stellgröße zur Verfügung zu stellen.The achievable forecasting quality depends on various factors. The forecasting method can therefore not work independently of the forecasting quality. It is therefore necessary to determine the quality of forecasting continuously and to make it available to the forecasting method used as a feedback control variable.
Die laufende Messung der Prognosegüte dient auch der ständigen Qualitätskontrolle, um Situationen festzustellen, in denen die Prognose nicht oder nur unzureichend möglich ist. Nimmt die Prognosegüte plötzlich ab, so ist dies in einigen Fällen nicht nur ein Zeichen für einen schlagartigen Wechsel des inhärent chaotischen Systems, sondern für einen unvorhersehbaren Störeinfluß auf das Verkehrssystem, z.B. in Form eines Unfalls.The ongoing measurement of the forecasting quality is also used for continuous quality control in order to determine situations in which the prognosis is not or only insufficiently possible. If the forecast quality decreases suddenly, this is in some cases not only a sign of a sudden change of the inherently chaotic system but of an unforeseeable disturbance on the traffic system, e.g. in the form of an accident.
Es ist daher notwendig,
die eingesetzten Prognoseverfahren adaptiv an den jeweiligen Systemzustand anpassen zu können,
den erreichbaren Prognosehorizont geeignet adaptiv festzulegen sowie
eine Prognosegüte zur Qualitätskontrolle fortlaufend zu ermitteln.It is therefore necessary
adapt the used forecasting methods adaptively to the respective system state,
determine the attainable forecast horizon suitably and adaptively
to continuously determine a forecasting quality control quality.
Das Verfahren ist in folgende Schritte gegliedert (Fig. 1):
- 1. Von verschiedenen Quellen eingehende Verkehrsdaten werden über ein Prognosesystem
für einen bestimmten Zeitraum gerechnet vom aktuellen Zeitpunkt t der Berechnung der Prognose sowie einen gewissen Ortsbereich um Orte mit real vorliegenden Verkehrsdaten, den zeitlich-örtlichen Prognosehorizont tp bzw. xp, vorhergesagt. Dabei bestehen die Verkehrsdaten im einfachsten Fall aus Verkehrsmeßwerten aus dem Straßenverkehrsnetz, z.B. Verkehrsflüsse, -dichten oder-geschwindigkeiten. Sie können aber auch daraus abgeleitete Werte enthalten, z.B. gefilterte Verkehrsmeßwerte. - 2. Es wird die aktuell erreichte Prognosegüte bzw. der Prognosefehler durch Soll-/Ist Vergleich der prognostizierten Verkehrsdaten mit den nach Verstreichen des Prognosezeitraumes aktuell ermittelten Verkehrsdaten während des Prognose- und Produktionsbetriebs laufend aktuell bestimmt.
- 3. Der gemessene Prognosefehler wird über eine Rückkopplung als Stellgröße zur adaptiven Anpassung des eingesetzten Prognoseverfahrens als auch des anvisierten Prognosehorizontes verwendet. Über eine Rückkopplung wird das Prognoseverfahren also laufend adaptiv gesteuert.
Bei größer werdendem Prognosefehler wird dann der Prognosehorizont für das eingesetzte Simulationsverfahren entsprechend verringert und umgekehrt. Ebenso kann die Auswahl der für die Prognose zu verwendenden Verkehrsdatenquellen über den Prognosefehler gesteuert werden. Zusätzlich wird der Prognosefehler als Qualitätsmaß den prognostizierten Verkehrsdaten hinzugefügt, um nachfolgenden verarbeitenden Systemen eine Bewertung bzw. Auswahl zu ermöglichen. - 4. der Prognosefehler wird fortlaufend ausgegeben zur Qualitätskontrolle der Prognose. Er dient auch als Indikator für einen plötzlich aufgetretenen Störfall. Ebenso werden gemittelte Verläufe von historischen Verkehrsmeßwerten (Ganglinien) in der Nähe eines erkannten Störfalls korrigiert, z.B. die Flußwerte an stromabwärtigen Detektorstandorten verringert.
- 5. die prognostizierten Verkehrsdaten werden sodann Verkehrsinformationssystemen zugeleitet, die dann die üblichen Verkehrsinformationen erzeugen, nur aktueller auf Basis der aktuelleren prognostizierten Datenbasis.
- 1. Traffic data received from various sources is passed through a forecasting system
Calculated for a certain period calculated from the current time t of the calculation of the forecast and a certain area around places with real traffic data available, the temporal-local forecast horizon t p or x p , predicted. In the simplest case, the traffic data consists of traffic measurements from the road traffic network, eg traffic flows, densities or speeds. You can also use derived values, such as filtered traffic readings. - 2. The currently achieved forecast quality or the forecast error is determined by actual / actual comparison of the forecast traffic data with the traffic data currently determined after the expiry of the forecast period during the forecasting and production operation.
- 3. The measured forecast error is used via a feedback as a manipulated variable for the adaptive adjustment of the used forecasting method as well as the envisaged forecast horizon. By means of feedback, the forecasting process is thus continuously adaptively controlled.
As the forecast error increases, the forecast horizon for the simulation method used is then correspondingly reduced and vice versa. Likewise, the Selection of the traffic data sources to be used for the forecast are controlled by the forecast error. In addition, the forecast error is added as a measure of quality to the predicted traffic data to allow subsequent processing systems to score. - 4. The forecast error is continuously output for quality control of the forecast. It also serves as an indicator of a sudden accident. Similarly, averaged histories of historical traffic readings (hydrographs) in the vicinity of a detected accident are corrected, eg, the flux values at downstream detector sites are reduced.
- 5. The predicted traffic data is then sent to traffic information systems, which then generate the usual traffic information, only more up-to-date on the basis of the more recent predicted database.
Der Soll-/Ist Vergleich der prognostizierten mit den aktuellen Verkehrsdaten wird über ein Ähnlichkeitsmaß vorgenommen, das zum aktuellen Zeipunkt mit einer örtlichen Umgebung, die Abweichungen der Daten voneinander bewertet. Dies kann z.B. über eine Korrelation oder eine Abstandsmetrik geschehen. Im Verfahren wird als Abstandsmetrik die mittlere quadratische Abweichung
zwischen prognostizierten und aktuellen Verkehrsdaten in einer lokalen Umgebung, dr und ds von z.B.
xp = 500m und tl = -4 min respektive verwendet. Die lokale Glättung der prognostizierten Verkehrsdaten und der gegenwärtigen Verkehrsdaten wird über eine Ortsinterpolation vorgenommen. Die Gewichtsfunktion g(u,v) als Integrationskern wird für größere betragsmäßige Abweichungen x - r bzw. t - s kleiner und sorgt damit für die lokale Bewertung der Abweichungen bzgl. x und t. Sie kann z.B. eine Gaußfunktion sein oder auch einfach zu 1 gesetzt werden, was einem Rechteckfenster entspricht.The target / actual comparison of the forecast with the current traffic data is made via a similarity measure, which evaluates the current time with a local environment, the deviations of the data from each other. This can be done, for example, via a correlation or a distance metric. In the method, the distance metric is the mean square deviation
between predicted and current traffic data in a local environment, dr and ds of eg
x p = 500m and t l = -4 min respectively used. The local smoothing of the predicted traffic data and the current traffic data is done via a local interpolation. The weight function g (u, v) as integration kernel becomes smaller for larger magnitude deviations x - r or t - s and thus ensures the local evaluation of the deviations with respect to x and t. It can be eg a Gaussian function or simply set to 1, which corresponds to a rectangular window.
Zusätzlich wird das Verfahren erweitert durch eine laufende Erzeugung eines Maßes für die Prognosegüte auf Ebene der Produkte (Verkehrsmeldungen und Reisezeiten) (Fig. 2). Diese wird als zusätzliche Qualitätskontrolle und auch als Stellgröße verwendet. Es ergibt sich damit ein geregelter variabler Prognosehorizont durch zweifach kaskadierten mittelbaren oder unmittelbaren Regelkreis. Prinzipiell kann auch nur der äußere Regelkreis für eine Qualitätskontrolle bzw. -auswertung betrieben werden.In addition, the method is extended by a continuous generation of a measure of the prediction quality at the level of the products (traffic reports and travel times) (FIG. 2). These is used as an additional quality control and also as a manipulated variable. This results in a regulated variable forecasting horizon through a double-cascaded indirect or direct control loop. In principle, only the outer control loop can be operated for quality control or evaluation.
Die Messung der Prognosegüte kann auch im Nachhinein, also "offline", auf Basis archivierter Meßdaten oder Produkte (archivierte Verkehrsmeldungen und Reisezeiten) für eine Qualitätsauswertung erfolgen, um Situationen festzustellen, in denen die Prognose nicht oder nur unzureichend möglich war.
Zur Bestimmung der Prognosegüte für die Produktdaten (äußerer Soll-/Ist-Vergleich in Figur 2) werden Ähnlichkeitsmaße für Verkehrsmeldungen eingesetzt.The measurement of the prognosis quality can also be done retrospectively, ie "offline", on the basis of archived measurement data or products (archived traffic reports and travel times) for a quality evaluation in order to determine situations in which the prognosis was not or only insufficiently possible.
To determine the forecast quality for the product data (external target / actual comparison in FIG. 2), similarity measures are used for traffic reports.
Claims (11)
- A method of drawing up predicted traffic data for traffic information,
characterised in that,
on the basis of traffic data originating from various sources, traffic data are forecast in a prediction process for a location and a time as prediction horizon,- in that the currently achieved prediction quality or prediction error is determined on an ongoing basis during prediction and production operation by predicted/actual comparison of the predicted traffic data with the traffic data currently determined once the prediction period has passed,- in that the measured prediction error is used via feedback as a correcting variable for adaptive adaptation of the prediction process used and also of the target prediction horizon,- wherein, if the prediction error grows larger, the prediction horizon for the simulation process used is reduced accordingly and, if the prediction error diminishes, said horizon is increased,- and in that traffic information may then be compiled and output from the traffic data predicted in this way. - A method according to claim 1,
characterised in that
the traffic data consist of measured traffic values, such as traffic flow, density and speed. - A method according to any one of the preceding claims,
characterised in that
the traffic data contain variables derived or calculated from measured traffic values, such as filtered measured traffic values. - A method according to any one of the preceding claims,
characterised in that
the predicted/actual comparison of the traffic data is performed via a similarity measure, and in particular via a correlation or a distance metric between locally smoothed versions of the predicted and current traffic data. - A method according to any one of the preceding claims,
characterised in that
local smoothing of the predicted traffic data is effected via interpolation. - A method according to any one of the preceding claims,
characterised in that
the prediction error is used as a fault indicator, to generate corresponding traffic reports or for taking corrected account of current or historic measured traffic data from detectors surrounding the particular location x under consideration. - A method according to any one of the preceding claims,
characterised in that
the traffic data sources to be used for the prediction are selected via the prediction error. - A method according to any one of the preceding claims,
characterised in that
the currently achieved prediction quality or prediction error is determined on an ongoing basis during prediction and production operation on the basis of traffic products, e.g. traffic reports, by predicted/actual comparison of the traffic products determined from predicted traffic data with the traffic products currently determined once the prediction period has passed (Fig. 2). - A method according to any one of the preceding claims,
characterised in that
prediction quality is determined as a function of location and time on the basis of archived traffic data or archived traffic products. - A method according to any one of the preceding claims,
characterised in that
the measured traffic values for the traffic conditions of the road traffic network are detected by stationary and/or mobile, synchronously and/or asynchronously transmitting detectors.
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DE10246185A1 (en) * | 2002-10-02 | 2004-04-15 | Bayerische Motoren Werke Ag | Procedures for the quality check of traffic incident reporting procedures |
DE10246184A1 (en) * | 2002-10-02 | 2004-09-30 | Bayerische Motoren Werke Ag | Process for improving the quality of traffic incident reporting processes |
DE102005032975A1 (en) * | 2005-07-14 | 2007-01-25 | Siemens Ag | Dynamic road traffic management system responds to a disturbance and uses modelling system to determine traffic control changes |
DE102007022169A1 (en) * | 2007-05-11 | 2008-11-13 | Siemens Ag | Method and system for controlling object movements |
RU2666333C1 (en) | 2017-04-04 | 2018-09-06 | Общество С Ограниченной Ответственностью "Яндекс" | Methods of determining error parameter in calculation of user traffic, which is associated with estimated traffic conditions |
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SE470367B (en) * | 1992-11-19 | 1994-01-31 | Kjell Olsson | Ways to predict traffic parameters |
ES2135134T3 (en) * | 1995-04-28 | 1999-10-16 | Inform Inst Operations Res & M | PROCEDURE FOR THE DETECTION OF DISTURBANCES IN ROLLED TRAFFIC. |
DK0902405T3 (en) * | 1997-09-11 | 2004-09-13 | Siemens Ag | Procedure for obtaining traffic information |
EP0903711A3 (en) * | 1997-09-18 | 2000-08-23 | Siemens Aktiengesellschaft | Method for transmitting traffic information |
DE19935769C2 (en) * | 1999-07-23 | 2002-02-07 | Ddg Ges Fuer Verkehrsdaten Mbh | Traffic condition forecast through feedback cascade |
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Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
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CN106295888A (en) * | 2016-08-12 | 2017-01-04 | 东南大学 | A kind of public building based on measured data is joined and is built parking position and share time window and determine method |
CN106295888B (en) * | 2016-08-12 | 2020-02-14 | 东南大学 | Method for determining shared time window of public building parking lot configuration based on measured data |
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ATE324646T1 (en) | 2006-05-15 |
EP1174842A1 (en) | 2002-01-23 |
DE10036364C2 (en) | 2003-08-28 |
ES2259647T3 (en) | 2006-10-16 |
DE50109601D1 (en) | 2006-06-01 |
DE10036364A1 (en) | 2002-02-07 |
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