EP1071057B1 - Method and device for traffic condition prognosis by cascading state feedback - Google Patents

Method and device for traffic condition prognosis by cascading state feedback Download PDF

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Publication number
EP1071057B1
EP1071057B1 EP00250248A EP00250248A EP1071057B1 EP 1071057 B1 EP1071057 B1 EP 1071057B1 EP 00250248 A EP00250248 A EP 00250248A EP 00250248 A EP00250248 A EP 00250248A EP 1071057 B1 EP1071057 B1 EP 1071057B1
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Prior art keywords
data
traffic
forecast
measurement data
time
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German (de)
French (fr)
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EP1071057A1 (en
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Markus Dipl.-Phys. Dr. Rer Nat. Becker
Ulrich Dipl.-Phys. Dr. Rer Nat. Fastenrath
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DDG Gesellschaft fuer Verkehrsdaten mbH
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DDG Gesellschaft fuer Verkehrsdaten mbH
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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 a traffic forecast.
  • Traffic information systems generate up-to-date traffic information, such as traffic reports or travel time estimates and navigation information, based on measurement data from stationary sensors arranged along roads of the traffic network and / or sensors (FCD) arranged in the traffic network and / or other measurement data sources.
  • traffic information such as traffic reports or travel time estimates and navigation information
  • FCD sensors
  • WO 98/27525 discloses a method for completing spatial gaps in the measurement data by multiple feedback of forecasts and other data generated from past times.
  • the object of the present invention is to provide a method and a device for predicting location state data concerning the state of a traffic network at a future prognosis time on the basis of measurement data relating to a plurality of locations and a plurality of points in time and relating to the state of a traffic network.
  • the object is achieved in each case by the independent claims.
  • the present invention enables a very reliable prognosis of future traffic conditions by analyzing the value history of measurement data measured at several past times to generate measurement data simulating replacement data for measurement data gaps in the past and in the future.
  • measurement data can be used, which are detected asynchronously in time.
  • spatially and / or temporally incomplete measurement data can be used to simulate (ie, virtually generate) a spatially and temporally complete traffic data source for the past and also for the future.
  • the result of this is related to road sections (also referred to as Häsmeßquerête RMQ) related time artificially synchronized traffic data.
  • road sections also referred to as Häsmeßqueritese RMQ
  • These suitably have a uniform format such that they are in equal cyclic intervals and / or equal units; the intervals may be for example one minute.
  • FIG. 1 illustrates the data flow on the basis of a block diagram of a device for carrying out the method according to the invention.
  • the measured data used comprise data 1 (FCD) collected by sensors arranged in vehicles in the traffic network, data 2 (SES) detected by stationary sensors in the road network, and data 3 coming from another traffic information center (VIZ) (for example based on country reporting messages, police radio Etc.).
  • FCD data 1
  • SES data 2
  • VIZ traffic information center
  • the data 4 output at the end represent spatially and temporally complete with location for further processing sufficient accuracy (from the data 1 to 3) location status data 4.
  • the location status data 4 (speeds, traffic density, traffic jams, etc.) are spatially such gapless that, for example, for a digital Map of the road network with spatial subsections for each spatial subsection is present a measurement date for a relevant time, allowing for easier and better processing. For example, they can be complete in terms of time in that, for a sufficient number of measurement data (location-state data) completed before the current time, that there are recently completed times.
  • the completion essentially takes place in a multi-data logic MDL 5, in which essentially the method according to the invention runs.
  • traffic analysis methods are carried out in which different traffic flow models (for example according to claims 2-4) are used and optimized based on the measurement data completed in the MDL 5.
  • the multi-model logic MML 9 combines the results of the modules M1 to M3 based on different analysis methods, in particular in the form of a reliability / credibility analysis and selection.
  • the simulation component SIM 10 calculates a forecast for the future on the basis of the data generated by the multimodal logic 9, the prognosis time affected by the prognosis being in the future compared to the forecast generation time.
  • the prognosis of the future based on measured data collected at a previous point in time, an optimized utilization of measured data by a more accurate process analysis of processes (congestion, etc.) in the road network is possible.
  • the component HPR 11 generates out of the current data generated by the MML 9 hydrographs (ie time histories of the measured data) and tries to learn the relationship between traffic conditions and certain selection characteristics.
  • the results of the simulation component 10 are fed back via a feedback unit RER in the multi-data logic for optimizing the (besides the data 1 to 3) flowing into the MDL measurement data basis.
  • the generated by the component HPR hydrographs and relationships between traffic conditions and selection features are coupled (via a not shown here module ZYR) also as an input to the multi-data logic 5.
  • the MML 9 and the HPR 11 14 data are created in a data fusion unit, which represent predicted traffic conditions of sections of the road network.
  • FIG. 2 clarifies the problem with incoming measurement data on the basis of a measurement data history.
  • the right-pointing axis shows the time and the upward-pointing axis the speed.
  • the solid line sequence shows vehicle average speeds (for example all vehicles in one minute) detected at different times with a stationary sensor (SES) at a position in the road network.
  • SES stationary sensor
  • the measurement data acquired by the sensor relate to a plurality of points in time, one behind the other, and those that have occurred a short time ago; These measurement data are integrated in such a way that their temporal course is subjected to an analysis and used to complete other measurement data.
  • this is explained, for example, by means of a vehicle which at one time passes a sensor at one location and after a certain time at a different location behind the sensor has a certain (same or other congestion in congestion etc.). From different speeds of vehicles at several times at the location of the sensor can thus be closed on suspected (not present as a measurement) speeds of the vehicles at locations behind the sensor as well as (with propagating traffic jams in front of the sensor).
  • FIG. 3 illustrates by way of example as a table that different conditional gaps in incoming different generated measurement data can be completed with different substitute data sources.
  • Measurement data gaps in measurement data (SES) generated by stationary detectors in the traffic network can be completed with substitute data sources from historical databases (HPR in FIG. 1) and traffic analysis system, wherein the measurement data quality is also possible through an error estimation (LOS estimation).
  • LOS estimation error estimation
  • Data loss in data from another traffic information center (which has access to state registration offices, police reports, etc.) and data from a sensor detection system can be completed in case of data loss, for example, from a historical database HPR.
  • unmonitored lanes can be completed by a lane estimator, which can close unmonitored lanes based on experience from monitored lanes.
  • Unmonitored nodes of a traffic network may cause unknown values for average speeds and / or vehicle numbers between different measuring points of a sensor detection system, whereby these unknown factors - if available - can also be relatively accurately completed by historical databases.
  • An LOS estimator (for example according to FIG. 3) can be used as a substitute data source. If the reporting behavior of stationary detectors (SES) in the road network provides that a detector always responds when a Change between defined speed ranges in the measurement data measured by him has taken place (local transmission criterion) and this is the LOS estimation known, each time a data telegram (forecast time) from a detector on the basis of the transmitted LOS concerning the road a forecast for the average speed will be hit. The prediction quality is guaranteed by half the width of the LOS, if the prognosis value is equated with the mean value of the LOS.
  • the LOS Level of Service
  • a possible classification is from LOS 1 (bad, 0 to 30 km / h), LOS 2 (medium, 30 to 60 km / h), LOT 3 (good, 60 to 90 km / h), LOT 4 (very good, > 90 km / h).
  • the forecasting quality of a forecast is guaranteed by half the width of the speed range of a LOS (for example 0 to 30 km / h); if the prognosis value is equated with the mean value (in the case, for example, 15 km / h) of the LOS, since with stronger deviations a renewed data telegram would be sent to the detector.
  • the LOS estimation method can also be used to shift a current line (representing the time course in the system for a directional measuring cross section (in the case of stationary detectors, for example) into the current LOS range, if a deviation of the latter current measured value of a currently valid for the Meßquerites hydrograph exists.
  • the difference to the mean value of the corresponding interval can be formed and the speed guideline value can be shifted by this difference.
  • FIG. 2 The temporal sequence in which the measurement and replacement data are provided is illustrated by FIG. 2.
  • the hydrograph management system HPR becomes the first hydrograph for the detector (of which the illustrated SES diagram comes). If this is not the case, the previous day's hydrograph can be used to complete the data if it is stored persistently in the HPR.
  • this detector transmits measurement data concerning a past time point (ie, a measurement data history) due to LOS change (average speed change on a road section as mentioned above), and the LOS estimator transmits a forecast for future times on the basis of these data.
  • a past time point ie, a measurement data history
  • LOS change average speed change on a road section as mentioned above
  • the detector transmits another set of measurement data (further measurement data history) due to a renewed LOS change of the road section observed by it, and the LOS estimator then generates a new prognosis based on this.
  • the hydrograph management system HPR updates the hydrograph delivered at the beginning of the day (t1).
  • the new hydrograph really describes the traffic situation better than the old hydrograph, because the subsystem HPR has more information to select the hydrograph.
  • gaps in the measured data can be eliminated by resorting to replacement data from the historical data source HPR.
  • the data source can be selected for which most of the measured data is present, or in the absence of measured data, the replacement data with the lowest calculated error probability.
  • the completed data can, for example, on time intervals of length 1 min. be transformed.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The method involves detecting spatially and/or temporally incomplete data relating to the state of the traffic network up until the creation point of the forecast. The gaps in the data are filled using a representative traffic model to generate simulated replacement data. Replacement data are generated from the traffic model for future time points. A forecast is made of the state of the traffic network at a future a time point based on the existing data and replacement data. An Independent claim is included for an apparatus for carrying out the method.

Description

Die Erfindung betrifft ein Verfahren zur Erstellung einer Verkehrsprognose.The invention relates to a method for generating a traffic forecast.

Verkehrsinformationssysteme erzeugen aktuelle Verkehrsinformationen, wie Verkehrsmeldungen oder Reisezeitschätzungen und Navigationsinformationen, basierend auf Meßdaten aus entlang Straßen des Verkehrsnetzes angeordneten stationären Sensoren und/oder in im Verkehrsnetz beweglichen Fahrzeugen angeordneten Sensoren (FCD) und/oder anderen Meßdatenquellen.Traffic information systems generate up-to-date traffic information, such as traffic reports or travel time estimates and navigation information, based on measurement data from stationary sensors arranged along roads of the traffic network and / or sensors (FCD) arranged in the traffic network and / or other measurement data sources.

Aus der WO 98/27525 ist ein Verfahren zur Vervollständigung von räumlichen Lücken in den Meßdaten durch mehrfache Rückkopplung von zu vergangenen Zeitpunkten erstellten Prognosen und anderen Daten bekannt.WO 98/27525 discloses a method for completing spatial gaps in the measurement data by multiple feedback of forecasts and other data generated from past times.

Aufgabe der vorliegenden Erfindung ist die Schaffung eines Verfahrens bzw. einer Vorrichtung zur Prognose von den Zustand eines Verkehrsnetzes zu einem künftigen Prognosezeitpunkt betreffenden Ortszustandsdaten aufgrund von sich auf mehrere Orte und mehrere Zeitpunkte beziehenden, den Zustand eines Verkehrsnetzes betreffenden Meßdaten. Die Aufgabe wird jeweils durch die unabhängigen Ansprüche gelöst.The object of the present invention is to provide a method and a device for predicting location state data concerning the state of a traffic network at a future prognosis time on the basis of measurement data relating to a plurality of locations and a plurality of points in time and relating to the state of a traffic network. The object is achieved in each case by the independent claims.

Die vorliegende Erfindung ermöglicht eine sehr zuverlässige Prognose von künftigen Verkehrszuständen durch Analyse des Werte-Verlaufes von zu mehreren vergangenen Zeitpunkten gemessenen Meßdaten zur Erstellung von Meßdaten simulierenden Ersatzdaten für Meßdatenlücken in der Vergangenheit und in der Zukunft. Dabei können auch Meßdaten verwendet werden, die zeitlich asynchron erfaßt werden.The present invention enables a very reliable prognosis of future traffic conditions by analyzing the value history of measurement data measured at several past times to generate measurement data simulating replacement data for measurement data gaps in the past and in the future. In this case, measurement data can be used, which are detected asynchronously in time.

Zur Erstellung von Vekehrsprognosen kann im Prinzip aus räumlich und/oder zeitlich lückenhaften Meßdaten eine räumlich und zeitlich lückenlose Verkehrsdaten-Quelle für die Vergangenheit und darauf auch für die Zukunft simuliert (also virtuell erzeugt) werden. Das Ergebnis hiervon sind auf Straßenabschnitte (auch als Richtungsmeßquerschnitte RMQ bezeichnet) bezogene zeitlich künstlich synchronisierte Verkehrsdaten. Diese weisen zweckmäßig ein einheitliches Format dergestalt auf, daß sie in gleichen zyklischen Intervallen und/oder gleichen Einheiten vorliegen; die Intervalle können beispielsweise eine Minute betragen. Bei der Erstellung der räumlich/zeitlich lückenlosen Verkehrsdatenbasis können durch Fehlerschätzung bei der Berechnung für die einzelnen Werte Qualitätsangaben mitgeneriert werden.In order to generate traffic forecasts, spatially and / or temporally incomplete measurement data can be used to simulate (ie, virtually generate) a spatially and temporally complete traffic data source for the past and also for the future. The result of this is related to road sections (also referred to as Richtungsmeßquerschnitte RMQ) related time artificially synchronized traffic data. These suitably have a uniform format such that they are in equal cyclic intervals and / or equal units; the intervals may be for example one minute. When creating the spatially / temporally gap-free traffic database, quality estimates can be generated for the individual values by estimating the errors in the calculation.

Weitere Merkmale und Vorteile ergeben sich aus den Unteransprüchen und der nachfolgenden Beschreibung eines Ausführungsbeispieles. Dabei zeigt:

Fig. 1
als Blockschaltbild Komponenten einer Vorrichtung zur Durchführung des erfindungsgemäßen Verfahrens,
Fig. 2
im Verlaufe der Zeit von einem Sensor gemessene Meßdaten, aus einer historischen Datenbank entnommene Meßdaten und eine Intervallschätzung (=LOS-Schätzung),
Fig. 3
als Tabelle grundsätzlich zur Vervollständigung von bestimmten Meßdatenlücken etc. geeignete Ersatzdatenquellen.
Further features and advantages emerge from the dependent claims and the following description of an embodiment. Showing:
Fig. 1
Block diagram of components of an apparatus for carrying out the method according to the invention,
Fig. 2
measuring data measured over time by a sensor, measured data taken from a historical database and an interval estimation (= LOS estimation),
Fig. 3
As table in principle for the completion of certain measuring data gaps etc. suitable substitute data sources.

Figur 1 verdeutlicht den Datenfluß anhand eines Blockschaltbildes einer Vorrichtung zur Durchführung des erfindungsgemäßen Verfahrens.FIG. 1 illustrates the data flow on the basis of a block diagram of a device for carrying out the method according to the invention.

Die verwendeten Meßdaten umfassen von in im Verkehrsnetz beweglichen Fahrzeugen angeordneten Sensoren erfaßte Daten 1 (FCD), von stationären Sensoren im Straßenverkehrsnetz erfaßte Daten 2 (SES) sowie von einer anderen Verkehrsinformationszentrale (VIZ) kommende Daten 3 (beispielsweise basierend auf Landesmeldestellen-Meldungen, Polizeifunk etc.).The measured data used comprise data 1 (FCD) collected by sensors arranged in vehicles in the traffic network, data 2 (SES) detected by stationary sensors in the road network, and data 3 coming from another traffic information center (VIZ) (for example based on country reporting messages, police radio Etc.).

Die am Ende ausgegebenen Daten 4 repräsentieren räumlich und zeitlich mit zur Weiterverarbeitung ausreichender Genauigkeit lückenlos (aus den Daten 1 bis 3) vervollständigte Ortszustandsdaten 4. Die Ortszustandsdaten 4 (Geschwindigkeiten, Verkehrsdichte, Staus etc.) sind räumlich dergestalt lückenlos, daß beispielsweise für eine digitale Karte des Straßenverkehrsnetzes mit räumlichen Unterabschnitten für jeden räumlichen Unterabschnitt ein Meßdatum für einen relevanten Zeitpunkt vorliegt, was eine einfachere und bessere Weiterverarbeitung ermöglicht. Zeitlich lückenlos können sie beispielsweise insofern sein, daß für eine ausreichende Zahl von vor dem aktuellen Zeitpunkt liegenden, kurz zurückliegenden Zeitpunkten vervollständigte Meßdaten (Ortszustandsdaten) vorliegen.The data 4 output at the end represent spatially and temporally complete with location for further processing sufficient accuracy (from the data 1 to 3) location status data 4. The location status data 4 (speeds, traffic density, traffic jams, etc.) are spatially such gapless that, for example, for a digital Map of the road network with spatial subsections for each spatial subsection is present a measurement date for a relevant time, allowing for easier and better processing. For example, they can be complete in terms of time in that, for a sufficient number of measurement data (location-state data) completed before the current time, that there are recently completed times.

Die Vervollständigung erfolgt im wesentlichen in einer Multidatenlogik MDL 5, in welcher im wesentlichen das erfindungsgemäße Verfahren abläuft. In den Teilmodulen M1 bis M3 6 bis 8 laufen verkehrstechnische Analyseverfahren ab, in welchen unterschiedliche Verkehrsflußmodelle (beispielsweise gemäß Ansprüchen 2 - 4) basierend auf den in der MDL 5 vervollständigten Meßdaten verwendet und optimiert werden. Die Multimodell-Logik MML 9 verknüpft die Ergebnisse der auf unterschiedlichen Analyseverfahren beruhenden Module M1 bis M3, insbesondere in Form einer Zuverlässigkeits-/Glaubwürdigkeits-Analyse und -Auswahl.The completion essentially takes place in a multi-data logic MDL 5, in which essentially the method according to the invention runs. In the submodules M1 to M3 6 to 8, traffic analysis methods are carried out in which different traffic flow models (for example according to claims 2-4) are used and optimized based on the measurement data completed in the MDL 5. The multi-model logic MML 9 combines the results of the modules M1 to M3 based on different analysis methods, in particular in the form of a reliability / credibility analysis and selection.

Die Simulationskomponente SIM 10 berechnet erfindungsgemäß aufgrund der von der Multimodell-Logik 9 erzeugten Daten eine Prognose für die Zukunft, wobei der durch die Prognose betroffene Prognosezeitpunkt gegenüber dem Prognose-Erstellung-Zeitpunkt in der Zukunft liegt. Bei der Zukunftsprognose, ausgehend von zu einem vergangenen Zeitpunkt erfaßten Meßdaten, ist eine optimierte Ausnutzung gemessener Meßdaten durch eine genauere Ablaufanalyse von Vorgängen (Staubildung etc.) im Straßenverkehrsnetz möglich. Die Komponente HPR 11 generiert aus den von der MML 9 erzeugten aktuellen Daten Ganglinien (also zeitliche Verläufe der Meßdaten) und versucht, den Zusammenhang zwischen Verkehrszuständen und bestimmten Selektionsmerkmalen zu lernen. Die Ergebnisse der Simulationskomponente 10 werden über eine Rückkopplungseinheit RER in die Multidatenlogik rückgekoppelt zur Optimierung der (neben den Daten 1 bis 3) in die MDL einfließenden Meßdaten-Basis.According to the invention, the simulation component SIM 10 calculates a forecast for the future on the basis of the data generated by the multimodal logic 9, the prognosis time affected by the prognosis being in the future compared to the forecast generation time. In the prognosis of the future, based on measured data collected at a previous point in time, an optimized utilization of measured data by a more accurate process analysis of processes (congestion, etc.) in the road network is possible. The component HPR 11 generates out of the current data generated by the MML 9 hydrographs (ie time histories of the measured data) and tries to learn the relationship between traffic conditions and certain selection characteristics. The results of the simulation component 10 are fed back via a feedback unit RER in the multi-data logic for optimizing the (besides the data 1 to 3) flowing into the MDL measurement data basis.

Die von der Komponente HPR generierten Ganglinien und Zusammenhänge zwischen Verkehrszuständen und Selektionsmerkmalen werden (über ein hier nicht dargestelltes Modul ZYR) ebenfalls als Eingang in die Multidatenlogik 5 eingekoppelt.The generated by the component HPR hydrographs and relationships between traffic conditions and selection features are coupled (via a not shown here module ZYR) also as an input to the multi-data logic 5.

Basierend auf den Augsangsdaten der SIM 10, der MML 9 und der HPR 11 werden in einer Datenfusionseinheit 14 Daten erstellt, welche prognostizierte Verkehrszustände von Abschnitten des Straßenverkehrsnetzes repräsentieren.Based on the output data of the SIM 10, the MML 9 and the HPR 11 14 data are created in a data fusion unit, which represent predicted traffic conditions of sections of the road network.

Eine Grundidee der MDL 5 besteht darin, aus räumlich und/oder zeitlich unvollständig eingehenden Meßdaten 1 bis 3 (von Sensoren etc.) durch Vervollständigung eine räumlich und zeitlich lückenlose und zeitlich synchrone Meßdatenquelle für die Vergangenheit und eine zwischen dem Prognose-Erstellungs-Zeitpunkt und dem Prognosezeitpunkt liegende Zukunft zu simulieren, um eine einfache hochwertige Weiterverarbeitung (für Verkehrsmeldungen, Prognosen, Navigationshinweise etc.) zu ermöglichen.
Figur 2 verdeutlicht die Problematik bei eingehenden Meßdaten aufgrund einer Meßdatenhistorie. In Figur 2 zeigt die nach rechts weisende Achse die Zeit und die nach oben weisende Achse die Geschwindigkeit. Die durchgezogene Linienfolge zeigt zu verschiedenen Zeitpunkten mit einem stationären Sensor (SES) an einer Position im Straßenverkehrsnetz erfaßte Fahrzeugdurchschnittsgeschwindigkeiten (beispielsweise alle Fahrzeuge in einer Minute). Die vom Sensor erfaßten Meßdaten betreffen mehrere bezüglich des jetzigen Zeitpunktes vergangene, hintereinanderliegende und kurz zurückliegende Zeitpunkte; diese Meßdaten werden derart eingebunden, daß ihr zeitlicher Verlauf einer Analyse unterworfen wird und zur Vervollständigung anderer Meßdaten verwendet wird.A basic idea of the MDL 5 consists of spatially and / or temporally incomplete incoming measurement data 1 to 3 (of sensors, etc.) by completing a spatially and temporally complete and temporally synchronous measurement data source for the past and between the forecast creation time and to simulate the future of the forecast in order to enable simple high-quality further processing (for traffic reports, forecasts, navigation instructions, etc.).
FIG. 2 clarifies the problem with incoming measurement data on the basis of a measurement data history. In Figure 2, the right-pointing axis shows the time and the upward-pointing axis the speed. The solid line sequence shows vehicle average speeds (for example all vehicles in one minute) detected at different times with a stationary sensor (SES) at a position in the road network. The measurement data acquired by the sensor relate to a plurality of points in time, one behind the other, and those that have occurred a short time ago; These measurement data are integrated in such a way that their temporal course is subjected to an analysis and used to complete other measurement data.

Anschaulich erklärt sich dies beispielsweise anhand eines Fahrzeuges, welches zu einem Zeitpunkt einen Sensor an einem Ort passiert und nach einer gewissen Zeit an einem anderen Ort hinter dem Sensor eine bestimmte (gleiche oder bei Staus etc. andere bestimmbare) Geschwindigkeit hat. Aus verschiedenen Geschwindigkeiten von Fahrzeugen zu mehreren Zeitpunkten am Ort des Sensors kann somit auf vermutete (als Meßwert nicht vorliegende) Geschwindigkeiten der Fahrzeuge an Orten hinter dem Sensor wie auch (bei sich ausbreitenden Staus vor dem Sensor) geschlossen werden.Illustratively, this is explained, for example, by means of a vehicle which at one time passes a sensor at one location and after a certain time at a different location behind the sensor has a certain (same or other congestion in congestion etc.). From different speeds of vehicles at several times at the location of the sensor can thus be closed on suspected (not present as a measurement) speeds of the vehicles at locations behind the sensor as well as (with propagating traffic jams in front of the sensor).

Neben Daten von stationären Sensoren kann dies auch mit von im Verkehr mitschwimmenden Fahrzeugen implementieren Meßsensoren generierten Meßdaten erfolgen; diese Meßdaten sind ebenfalls unvollständig, da sie nur unter bestimmten Bedingungen und/oder in bestimmten Zeitintervallen übermittelt werden; auch diese Meßdaten aus Fahrzeugen werden in der Regel als Paket übermittelt, wobei in einem Paket mehrere Durchschnittsgeschwindigkeiten (des Fahrzeuges) an verschiedenen Orten (entlang einer vom Fahrzeug befahrenen Straße) zu verschiedenen Zeitpunkten (den Meßzeitpunkten) auf dem Weg entlang der Straße enthalten sind.In addition to data from stationary sensors, this can also be done with measurement data generated by measuring sensors that are used in vehicles that are floating in traffic; these measurement data are also incomplete, since they are transmitted only under certain conditions and / or at certain time intervals; also these measurement data from vehicles are usually transmitted as a package, wherein in a package several average speeds (of the vehicle) at different locations (along a road traveled by the vehicle) at different times (the measurement times) are included along the road.

Figur 3 verdeutlicht beispielhaft als Tabelle, daß unterschiedlich bedingte Lücken in eingehenden unterschiedlichen generierten Meßdaten mit unterschiedlichen Ersatzdatenquellen vervollständigt werden können. Meßdatenlücken in von stationären Detektoren im Verkehrsnetz erzeugten Meßdaten (SES) können mit Ersatzdatenquellen aus historischen Datenbanken (HPR in Figur 1) und Verkehrsanalysesystem vervollständigt werden, wobei auch die Meßdatenqualität durch eine Fehlerschätzung (LOS-Schätzung) möglich ist.
Datenausfälle in von einer anderen Verkehrsinformationszentrale (welche auf Landesmeldestellen, Polizeimeldungen etc. Zugriff hat) kommenden Daten und Daten von einem Sensorerfassungssystem können bei Datenausfall beispielsweise auch aus einer historischen Datenbank HPR vervollständigt werden.FIG. 3 illustrates by way of example as a table that different conditional gaps in incoming different generated measurement data can be completed with different substitute data sources. Measurement data gaps in measurement data (SES) generated by stationary detectors in the traffic network can be completed with substitute data sources from historical databases (HPR in FIG. 1) and traffic analysis system, wherein the measurement data quality is also possible through an error estimation (LOS estimation).
Data loss in data from another traffic information center (which has access to state registration offices, police reports, etc.) and data from a sensor detection system can be completed in case of data loss, for example, from a historical database HPR.

Wenn bei einem Sensor Erfassungssysteme nur bestimmte Spuren (= Fahrbahnen) zu einer Straße überwacht werden, können nicht überwachte Spuren durch einen Spurschätzer, welcher aufgrund von Erfahrungswerten aus überwachten Spuren auf nicht überwachte Spuren schließen kann, vervollständigt werden.If only certain lanes (= lanes) to a road are monitored in a sensor detection system, unmonitored lanes can be completed by a lane estimator, which can close unmonitored lanes based on experience from monitored lanes.

Nicht überwachte Knoten eines Verkehrsnetzes, wie Ein- und Ausfahrten können zwischen verschiedenen Meßstellen eines Sensorerfassungssystems unbekannte Werte für Durchschnittsgeschwindigkeiten und/oder Fahrzeugzahlen bedingen, wobei diese unbekannten Faktoren - soweit verfügbar -ebenfalls durch historische Datenbanken relativ genau vervollständigbar sind.Unmonitored nodes of a traffic network, such as entrances and exits, may cause unknown values for average speeds and / or vehicle numbers between different measuring points of a sensor detection system, whereby these unknown factors - if available - can also be relatively accurately completed by historical databases.

Ein LOS-Schätzer (beispielsweise gemäß Figur 3), ist als Ersatzdatenquelle verwendbar. Wenn das Meldeverhalten von stationären Detektoren (SES) im Straßenverkehrsnetz vorsieht, daß sich ein Detektor stets dann meldet, wenn ein Wechsel zwischen definierten Geschwindigkeitsbereichen in den von ihm gemessenen Meßdaten sicher stattgefunden hat (lokales Übertragungskriterium) und dies dem LOS-Schätzverfahren bekannt ist, kann bei jeder Übermittlung eines Datentelegramms (Prognose-Zeitpunkt) von einem Detektor anhand des übermittelten LOS betreffend die Straße eine Prognose für die mittlere Geschwindigkeit getroffen werden. Die Prognosegüte ist durch die halbe Breite des LOS garantiert, wenn der Prognosewert mit dem Mittelwert des LOS gleichgesetzt wird. Als LOS (Level of Service) wird dabei die Qualität einer Straße in Form der auf ihr fahrbaren Geschwindigkeit bezeichnet. Eine mögliche Einteilung ist von LOS 1 (schlecht, 0 bis 30 km/h), LOS 2 (mittel, 30 bis 60 km/h), LOS 3 (gut, 60 bis 90 km/h), LOS 4 (sehr gut, > 90 km/h).An LOS estimator (for example according to FIG. 3) can be used as a substitute data source. If the reporting behavior of stationary detectors (SES) in the road network provides that a detector always responds when a Change between defined speed ranges in the measurement data measured by him has taken place (local transmission criterion) and this is the LOS estimation known, each time a data telegram (forecast time) from a detector on the basis of the transmitted LOS concerning the road a forecast for the average speed will be hit. The prediction quality is guaranteed by half the width of the LOS, if the prognosis value is equated with the mean value of the LOS. The LOS (Level of Service) refers to the quality of a road in terms of the speed that can be traveled on it. A possible classification is from LOS 1 (bad, 0 to 30 km / h), LOS 2 (medium, 30 to 60 km / h), LOT 3 (good, 60 to 90 km / h), LOT 4 (very good, > 90 km / h).

Die Prognosegüte einer Prognose ist durch die halbe Breite des Geschwindigkeitsbereichs eines LOS garantiert (beispielsweise 0 bis 30 km/h); wenn der Prognosewert mit dem Mittelwert (in dem Falle beispielsweise 15 km/h) des LOS gleichgesetzt wird, da bei stärkeren Abweichungen ein erneutes Datentelegramm des Detektors übersandt würde.
Das LOS-Schätzverfahren kann auch dazu benutzt werden, eine aktuell im System für einen Richtungsmeßquerschnitt (bei stationären Detektoren beispielsweise ein Meßort in Form einer Brücke) vorliegende (den Zeitverlauf repräsentierende) Ganglinie in den aktuellen LOS-Berech zu verschieben, falls eine Abweichung des letzten aktuellen Meßwertes von einer für den Meßquerschnitt aktuell gültigen Ganglinie existiert. Zum Unterschied des letzten aktuellen Meßwertes der Geschwindigkeit der SES-Daten kann die Differenz zu dem Ganglinienwert des entsprechenden Intervalls gebildet und der Ganglinienwert für die Geschwindigkeit um diese Differenz verschoben werden.The forecasting quality of a forecast is guaranteed by half the width of the speed range of a LOS (for example 0 to 30 km / h); if the prognosis value is equated with the mean value (in the case, for example, 15 km / h) of the LOS, since with stronger deviations a renewed data telegram would be sent to the detector.
The LOS estimation method can also be used to shift a current line (representing the time course in the system for a directional measuring cross section (in the case of stationary detectors, for example) into the current LOS range, if a deviation of the latter current measured value of a currently valid for the Meßquerschnitt hydrograph exists. In contrast to the last actual measurement of the speed of the SES data, the difference to the mean value of the corresponding interval can be formed and the speed guideline value can be shifted by this difference.

Falls sich die Geschwindigkeitsganglinie eines Straßenverkehrsabschnittes über der oberen Grenze eines LOS-Bereichs befindet, müssen die Geschwindigkeiten der Ganglinie abgesenkt werden, wenn sich die Geschwindigkeitsganglinie unter der unteren Grenze des LOS-Bereichs befindet, müssen sie angehoben werden.If the speed line of a traffic segment is above the upper limit of a LOS range, the speeds of the hydrograph must be lowered, if the speed delta line is below the lower limit of the LOS range, they must be raised.

Die zeitliche Abfolge, in der die Meß- und Ersatzdaten bereitgestellt werden, verdeutlicht sich anhand Figur 2.The temporal sequence in which the measurement and replacement data are provided is illustrated by FIG. 2.

Zum Zeitpunkt t1 (bei Tagesbeginn) wird von dem Ganglinien-Managementsystem HPR die erste Ganglinie für den Detektor (von welchem das dargestellte SES-Diagramm kommt) übermittelt. Wenn dies nicht der Fall ist, kann zur Datenvervollständigung die Ganglinie des Vortages verwendet werden, falls sie persistent im HPR gespeichert ist.At time t1 (at the beginning of the day), the hydrograph management system HPR becomes the first hydrograph for the detector (of which the illustrated SES diagram comes). If this is not the case, the previous day's hydrograph can be used to complete the data if it is stored persistently in the HPR.

Zum Zeitpunkt t2 übermittelt dieser Detektor mehrere vergangene Zeitpunkte betreffende Meßdaten (also eine Meßdatenhistorie) aufgrund eines LOS-Wechsels (Durchschnittsgeschwindigkeitsänderung auf einem Straßenabschnitt wie oben angegeben), und der LOS-Schätzer übermittelt auf der Basis dieser Daten eine Prognose für künftige Zeitpunkte.At time t2, this detector transmits measurement data concerning a past time point (ie, a measurement data history) due to LOS change (average speed change on a road section as mentioned above), and the LOS estimator transmits a forecast for future times on the basis of these data.

Zum Zeitpunkt t3 übermittelt der Detektor aufgrund eines erneuten LOS-Wechsels des von ihm beobachteten Straßenabschnittes einen weiteren Satz Meßdaten (weitere Meßdatenhistorie), und der LOS-Schätzer erstellt hierauf basierend eine neue Prognose.At time t3, the detector transmits another set of measurement data (further measurement data history) due to a renewed LOS change of the road section observed by it, and the LOS estimator then generates a new prognosis based on this.

Zum Zeitpunkt t4 aktualisiert das Ganglinien-Managementsystem HPR die zu Tagesbeginn (t1) gelieferte Ganglinie. Die neue Ganglinie beschreibt das Verkehrsgeschehen wirklich besser als die alte Ganglinie, da dem Teilsystem HPR zur Selektion der Ganglinie mehr Informationen vorliegen.At time t4, the hydrograph management system HPR updates the hydrograph delivered at the beginning of the day (t1). The new hydrograph really describes the traffic situation better than the old hydrograph, because the subsystem HPR has more information to select the hydrograph.

So können Lücken in den Meßdaten durch einen Rückgriff aus Ersatzdaten aus der historischen Datenquelle HPR beseitigt werden.Thus, gaps in the measured data can be eliminated by resorting to replacement data from the historical data source HPR.

Bei sich widersprechenden Daten aus unterschiedlichen Quellen (beispielsweise aktualisierten Ganglinien/alten Ganglinien, LOS-Schätzungen/aktuellen Ganglinien, Meßdatenhistorien/aktuellen Sensormeßdaten) ist ein Auswahlprozeß aufgrund der Meßdatenqualität ausführbar. Dabei kann die Datenquelle ausgewählt werden, für welche die meisten Meßdaten vorliegen, bzw. bei Fehlen von Meßdaten die Ersatzdaten mit der geringsten berechneten Fehlerwahrscheinlichkeit.With conflicting data from different sources (eg, updated hydrographs / old hydrographs, LOS estimates / current hydrographs, measured data histories / actual sensor measurement data), a selection process is feasible due to the measurement data quality. In this case, the data source can be selected for which most of the measured data is present, or in the absence of measured data, the replacement data with the lowest calculated error probability.

Die vervollständigten Daten können beispielsweise auf Zeitintervalle der Länge 1 min. transformiert werden.The completed data can, for example, on time intervals of length 1 min. be transformed.

Claims (18)

  1. A method of preparing a traffic forecast for a forecast time from measurement data, relating to the state of a traffic network, in a traffic centre,
    wherein, at a plurality of measurement locations in the traffic network, spatially and/or temporally incomplete measurement data relating to the state of the traffic network at the measurement locations are detected at a plurality of times within a time period extending temporally backwards from the forecast creation time,
    wherein, taking into consideration the path of temporal values for a plurality of measurement data detected at these times, replacement data which simulate measurement data and temporally and/or spatially fill the gaps in the measurement data are generated by traffic models representing temporal-spatial developments in the traffic network,
    and replacement data are also generated by the traffic models for times which are later than the forecast creation time, but are before the forecast time,
    whereafter a forecast of location-state data, representing the state of the traffic network at locations in the traffic network at a forecast time which is later than the forecast creation time, is made on the basis of the measurement data and replacement data existing for times before the forecast creation time and the replacement data existing for times after the forecast creation time.
  2. A method according to claim 1, characterised in that a traffic model is used which fills spatial gaps in the measurement data between two measurement locations for which measurement data exist by assigning replacement data to the locations between the two measurement locations, which replacement data are obtained by interpolation, especially linear interpolation of the measurement data existing for the two measurement locations.
  3. A method according to either one of the preceding claims, characterised in that a traffic model is used which determines traffic disturbances between two measurement points A and B on the basis of the temporal development of the difference in the traffic flows at the measurement locations A and B.
  4. A method according to any one of the preceding claims, characterised in that a traffic-flow model is used which determines replacement data, relating to the traffic state at locations and at times in the traffic network for which measurement data do not exist, from existing measurement data by solutions of differential equations relating to the traffic flow, the traffic density and the traffic speed.
  5. A method according to any one of the preceding claims, characterised in that the spatially and/or temporally incomplete measurement data are completed for at least also the forecast creation time by preparing forecasts, directed at the forecast creation time, from the path of temporal values for measurement data relating to a plurality of times in the past in order to generate, in this manner, a spatially and/or temporally sufficiently complete measurement-data and replacement-data basis representing the current state of the traffic network and for making a forecast for the future.
  6. A method according to any one of the preceding claims, characterised in that the state of a plurality of road sections is determined.
  7. A method according to any one of the preceding claims, characterised in that measurement data are detected by stationary sensors arranged on roads of the traffic network, especially in the form of measurement data which include average speeds of a plurality of vehicles at one point and/or the number of vehicles passing the point per unit time.
  8. A method according to any one of the preceding claims, characterised in that measurement data are detected by sensors (FCD) arranged on vehicles moving in traffic networks.
  9. A method according to any one of the preceding claims, characterised in that the measurement data include speeds of a respective vehicle.
  10. A method according to any one of the preceding claims, characterised in that the location-state data representing a future state of the traffic network are used to generate navigational information to be sent to road users.
  11. A method according to any one of the preceding claims, characterised in that the forecast location-state data representing a future state of the traffic network at a forecast time are used to generate information which is to be sent to road users and which represents average vehicle speeds and/or journey times within a respective road section of the traffic network.
  12. A method according to any one of the preceding claims, characterised in that information representing the degree of congestion and based on forecast location-state data representing a state of the traffic network at a future forecast time is sent to road users.
  13. A method according to any one of the preceding claims, characterised in that the state of locations at a future forecast time is also deduced, for which locations measurement data do not exist.
  14. A method according to any one of the preceding claims, characterised in that the forecast time lies in the near future, especially less than 30 minutes after the forecast creation time.
  15. A method according to any one of the preceding claims, characterised in that the forecast time lies after the forecast creation time by at most the time which the traffic needs in order to spread out from one network intersection, in the form of a crossroads, junction or the like, to the next network intersection.
  16. A method according to any one of the preceding claims,
    characterised in that information based on the location-state data relating to a future forecast time is sent to at least one road user, especially by mobile radio (SMS-MT or SMS-CB) or radio (especially RDS-TMC).
  17. A method according to any one of the preceding claims, characterised in that the accuracy and/or reliability of the result is estimated by analysis of the error propagation during the calculations according to the method or by an expert system, and this estimate is output with traffic information.
  18. A device for preparing traffic forecasts for a future forecast time from measurement data, relating to the state of a traffic network, in a traffic centre, characterised by:
    - a multidata logic (5) to which the data acquired from mobile and stationary sensors (1, 2) and from another traffic information centre (3) are sent,
    - one or more sub-modules (6, 7, 8) in which traffic-related analysis processes take place,
    - a multimodel logic (9) in which the results from the sub-modules (6, 7, 8) are combined,
    - a simulation component (10) in which a forecast for the future is calculated on the basis of the data generated in the multimodel logic (9),
    - a component HPR (11) which generates progress lines from the current data generated by the multimodel logic (9),
    - the feedback of the data from the simulation component (10) and/or the data from the component HPR (11) into the multidata logic (5),
    - a data fusion unit (14) which generates data, representing current and/or forecast traffic states of the road traffic network, from the output data from the simulation component (10), the multimodel logic (9) and the component HPR (11),
    wherein the device is formed
    - so that, taking into consideration the path of temporal values for a plurality of measurement data detected at these times, replacement data which simulate measurement data and temporally and/or spatially fill the gaps in the measurement data are generated by traffic models representing temporal-spatial developments in the traffic network,
    - so that replacement data are also generated by the traffic models for times which are later than the forecast creation time, but are before the forecast time, and
    - so that thereafter a forecast of location-state data, representing the state of the traffic network at locations in the traffic network at a forecast time which is later than the forecast creation time, is made on the basis of the measurement data and replacement data existing for times before the forecast creation time and the replacement data existing for times after the forecast creation time.
EP00250248A 1999-07-23 2000-07-20 Method and device for traffic condition prognosis by cascading state feedback Expired - Lifetime EP1071057B1 (en)

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