EP1071058A1 - Method and device for cascaded state feedback - Google Patents

Method and device for cascaded state feedback Download PDF

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Publication number
EP1071058A1
EP1071058A1 EP00250249A EP00250249A EP1071058A1 EP 1071058 A1 EP1071058 A1 EP 1071058A1 EP 00250249 A EP00250249 A EP 00250249A EP 00250249 A EP00250249 A EP 00250249A EP 1071058 A1 EP1071058 A1 EP 1071058A1
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Prior art keywords
measurement data
time
traffic network
state
data
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EP00250249A
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German (de)
French (fr)
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EP1071058B1 (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 completing and / or verifying data relating to the state of a traffic network in a traffic control center.
  • Traffic information systems generate current traffic information such as Traffic reports or travel time estimates and navigation information, based on time and / or space with respect to the transport network incomplete (i.e. incomplete) measurement data from stationary along streets of the Traffic network arranged stationary sensors and / or in the traffic network sensors (FCD) and / or others arranged in moving vehicles Measurement data sources.
  • transport network incomplete i.e. incomplete
  • FCD traffic network sensors
  • Spatial gaps in the measurement data are due to the fact that stationary sensors and / or sensors arranged in vehicles moving in the traffic network at all times are spatially spaced so that measurement data gaps occur between them.
  • the measurement data also contain time gaps, since sensors usually only send invavail at certain times, between which no current measurement data available.
  • measured values are transmitted, which in addition to the almost current point in time several past times and / or (with sensors arranged in vehicles) affect different places.
  • WO 98/27525 describes a method for completing spatial gaps in the measurement data by multiple feedback from past times predictions and other data.
  • the object of the present invention is to create a method or a method Device for completing and / or verifying time and space incomplete, referring to multiple locations and multiple times, the State of a traffic network related measurement data.
  • the task is completed by solved the independent claims.
  • the present invention enables spatial and temporal completion data relating to the state of a traffic network in a traffic center.
  • intelligent preprocessing before the creation of Traffic forecasts, traffic information, navigation information etc. are made, which in principle consists of spatially and / or temporally incomplete measurement data and simulated seamless traffic data source (i.e. generated virtually).
  • the result of this preprocessing is on road sections (also as Directional measurement cross sections RMQ) related artificially in time synchronized traffic data.
  • road sections also as Directional measurement cross sections RMQ
  • RMQ Directional measurement cross sections
  • These expediently have a uniform format such that they are at the same cyclic intervals and / or the same units present; the intervals can be, for example, one minute.
  • the Creation of the spatially / temporally seamless traffic database can be done by Error estimate in the calculation for the individual values of quality information can also be generated.
  • Figure 1 illustrates the data flow using a block diagram of a device to carry out the method according to the invention.
  • the measurement data used include those that are movable in the transport network Vehicles arranged sensors acquired data 1 (FCD), from stationary Data collected from road traffic sensors 2 (SES) and from another Traffic information center 3 (VIZ) incoming data (for example based on State registration office reports, police radio etc.).
  • FCD transport network Vehicles arranged sensors acquired data 1 (FCD), from stationary Data collected from road traffic sensors 2 (SES) and from another Traffic information center 3 (VIZ) incoming data (for example based on State registration office reports, police radio etc.).
  • the data 4 output at the end also represent in space and time Processing of sufficient accuracy without gaps (from data 1 to 3) completed location status data 4.
  • the location status data 4 (speeds, Traffic density, traffic jams, etc.) are spatially complete in such a way that, for example, for a digital map of the road network with spatial subsections for each spatial subsection has a measurement date for a relevant point in time, which enables easier and better processing. Gaps in time For example, they can be such that for a sufficient number of before current times, recent times completed Measurement data (location status data) are available.
  • the completion takes place essentially in a multi-data logic MDL 5, in which essentially runs the method according to the invention.
  • the sub-modules M1 to M3 6 to 8 run traffic analysis methods, in which different traffic flow models based on those in MDL 5 completed location status data are used and optimized.
  • the multi-model logic MML 9 links the results of the different analysis methods based modules M1 to M3, in particular in the form of a reliability / credibility analysis and selection.
  • the simulation component SIM 10 calculates on the basis of the multi-model logic 9 generated data a forecast for the future, this future one on the Data-related future can be; for previously recorded (and measurement data completed in MDL is the future compared to them Seeing the time of acquisition can also be the present or one between the time of recording and the present time (see However, it can also be a future point in time compared to the present affect).
  • a future forecast starting from a past one Measured data recorded for a point of time lying opposite its time of acquisition
  • the future which lies before the current present, is an optimized utilization measured data through a more precise process analysis of processes (Congestion etc.) possible in the road network.
  • the component generates HPR 11 from the current data generated by the MML 9 hydrographs (i.e.
  • the results of the Simulation components 10 are connected to the feedback unit RER Multi-data logic fed back to optimize the (in addition to 1 to 3) in the MDL incoming measurement data basis.
  • a data fusion unit 14 Based on the initial data of SIM 10, MML 9 and HPR 11, in a data fusion unit 14 creates data which current and / or predicted Represent traffic conditions of sections of the road network.
  • MDL 5 A basic idea of MDL 5 is that it is incomplete in space and / or time incoming measurement data 1 to 3 (from sensors etc.) by completing a to simulate seamlessly and spatially and temporally synchronous measurement data source, for simple high-quality processing (for traffic reports, To enable forecasts, navigation instructions etc.).
  • Figure 2 illustrates the problem with incoming measurement data due to Measurement data history.
  • the axis pointing to the right shows the time and the upward axis the speed.
  • the solid line shows at different times with a stationary sensor (SES) at one position Average vehicle speeds recorded in the road network (for example, all vehicles in one minute).
  • SES stationary sensor
  • Average vehicle speeds recorded in the road network for example, all vehicles in one minute.
  • the measurement data recorded by the sensor concern several past with respect to the current time, successive and recent times; this measurement data will integrated in such a way that their temporal course is subjected to an analysis and to Completion of other measurement data is used.
  • Figure 3 illustrates, by way of example as a table, that gaps in different conditions in incoming different generated measurement data with different Replacement data sources can be completed.
  • Measurement data gaps in from stationary Detectors generated in the traffic network (SES) can with Substitute data sources from historical databases (HPR in Figure 1) and Traffic analysis system (VAS in Figure 1) can be completed, including the Measured data quality is possible through an error estimate (LOS estimate).
  • LOS estimate error estimate
  • Data failures in from another traffic information center which on State registration offices, police reports etc. have access
  • coming data and data from a sensor detection system can, for example, also from a historical database HPR to be completed.
  • lanes lanes
  • Unsupervised nodes of a traffic network such as entrances and exits unknown between different measuring points of a sensor detection system Values for average speeds and / or number of vehicles require, where these unknown factors - if available - also by historical ones Databases can be completed relatively precisely.
  • a LOS estimator (for example according to FIG. 3) is used as a substitute data source usable. If the reporting behavior of stationary detectors (SES) in the Road traffic network provides that a detector always reports when a Switch between defined speed ranges in the ones he measured Measured data has taken place safely (local transmission criterion) and this the LOS estimation method is known can with each transmission of a data message (Forecast time) from a detector based on the transmitted LOS the road a forecast for the average speed will be made. The Forecast quality is guaranteed by half the width of the LOS if the forecast value is equated with the mean of the LOS.
  • 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), LOS 3 (good, 60 to 90 km / h), LOS 4 (very good,> 90 km / h).
  • the forecast 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 forecast value is equated with the average value (in the case, for example, 15 km / h) of the LOS, since a renewed data telegram from the detector would be sent if there were major deviations.
  • the LOS estimation method can also be used to shift a current curve (representing the course of time) present in the system for a directional measurement cross-section (for example, a measurement location in the form of a bridge in the case of stationary detectors) into the current LOS calculation, if a deviation of the last one current measured value of a currently valid curve for the cross section exists. In contrast to the last current measured value of the speed of the SES data, the difference to the curve value of the corresponding interval can be formed and the curve value for the speed can be shifted by this difference.
  • the speeds of the The gait line can be lowered if the speed gait line is below the lower limit of the LOS range, they must be raised.
  • the time sequence in which the measurement and replacement data are provided can be seen from FIG. 2.
  • the gangway management system HPR is the first curve for the detector (from which the SES diagram shown comes) transmitted. If not, you can go to Data completion the chart of the previous day can be used if it is stored persistently in the HPR.
  • this detector transmits several past times relevant measurement data (ie a measurement data history) due to a LOS change (Average speed change on a section of road as above specified), and the LOS estimator transmits a based on this data Forecast for future times.
  • relevant measurement data ie a measurement data history
  • LOS change Average speed change on a section of road as above specified
  • the detector transmits the signal due to another LOS change another section of measurement data (further Measurement data history), and the LOS estimator creates a new one based on this Forecast.
  • the curve management system HPR updates the to The beginning of the day (t1) delivered curve.
  • the new curve describes that Traffic is really better than the old gait line because the HPR subsystem is used Selection of the curve more information is available.
  • Gaps in the measurement data can be made by using replacement data from the historical data source HPR are eliminated.
  • conflicting data from different sources e.g. updated hydrographs / old hydrographs, LOS estimates / current hydrographs, Measurement data histories / current sensor measurement data
  • the data source can be selected for which most of the measurement data are available, or the absence of measurement data Substitute data with the lowest calculated error probability.
  • the completed data can, for example, on time intervals of length 1 min. be transformed.

Abstract

Data are provided at several measurement locations in the traffic network, relating to several time points within a time period extending backwards from the current time. The variations with time of several data detected in the past are used to determine local state data representing the current state of the traffic network. The state of several road sections may be determined e.g. using stationary sensors. An Independent claim is included for an apparatus for performing the method.

Description

Die Erfindung betrifft ein Verfahren zur Vervollständigung und/oder Verifizierung von den Zustand eines Verkehrsnetzes betreffenden Daten in einer Verkehrszentrale.The invention relates to a method for completing and / or verifying data relating to the state of a traffic network in a traffic control center.

Verkehrsinformationssysteme erzeugen aktuelle Verkehrsinformationen, wie Verkehrsmeldungen oder Reisezeitschätzungen und Navigationsinformationen, basierend auf zeitlich und/oder räumlich hinsichtlich des Verkehrsnetzes unvollständigen (also lückenhaften) Meßdaten aus stationär 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 current traffic information such as Traffic reports or travel time estimates and navigation information, based on time and / or space with respect to the transport network incomplete (i.e. incomplete) measurement data from stationary along streets of the Traffic network arranged stationary sensors and / or in the traffic network sensors (FCD) and / or others arranged in moving vehicles Measurement data sources.

Räumliche Lücken in den Meßdaten sind dadurch bedingt, daß stationäre Sensoren und/oder in im Verkehrsnetz beweglichen Fahrzeugen angeordnete Sensoren stets räumlich beabstandet sind, so daß zwischen ihnen Meßdaten-Lücken auftreten.Spatial gaps in the measurement data are due to the fact that stationary sensors and / or sensors arranged in vehicles moving in the traffic network at all times are spatially spaced so that measurement data gaps occur between them.

Überdies enthalten die Meßdaten auch zeitliche Lücken, da Sensoren in der Regel nur in bestimmten Zeitinvervailen senden, zwischen welchen keine aktuellen Meßdaten vorliegen. Allerdings werden in in Zeitintervallen etc. übermittelten Datenpaketen in der Regel Meßwerte übermittelt, welche neben dem nahezu aktuellen Zeitpunkt auch mehrere vergangene Zeitpunkte und/oder (bei in Fahrzeugen angeordneten Sensoren) verschiedene Orte betreffen.In addition, the measurement data also contain time gaps, since sensors usually only send invavail at certain times, between which no current measurement data available. However, in data packets transmitted in time intervals etc. in the Usually, measured values are transmitted, which in addition to the almost current point in time several past times and / or (with sensors arranged in vehicles) affect different places.

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 describes a method for completing spatial gaps in the measurement data by multiple feedback from past times predictions and other data.

Aufgabe der vorliegenden Erfindung ist die Schaffung eines Verfahrens bzw. einer Vorrichtung zur Vervollständigung und/oder Verifizierung von zeitlich und räumlich lückenhaften, 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 create a method or a method Device for completing and / or verifying time and space incomplete, referring to multiple locations and multiple times, the State of a traffic network related measurement data. The task is completed by solved the independent claims.

Die vorliegende Erfindung ermöglicht eine räumliche und zeitliche Vervollständigung von den Zustand eines Verkehrsnetzes betreffenden Daten in einer Verkehrszentrale. Dabei können Meßdaten verwendet werden, die zeitlich asynchron erfaßt werden. Es können zeitlich und räumlich im Rahmen der erforderlichen Genauigkeit lückenlose vervollständigte Meßdaten (= Ortszustandsdaten) generiert werden, wobei dies so erfolgen kann, daß sie in gleichen zeitlichen Intervallen (= synchron) vorliegen.The present invention enables spatial and temporal completion data relating to the state of a traffic network in a traffic center. Measurement data can be used which are recorded asynchronously in time. It can be seamless in terms of time and space within the required accuracy Completed measurement data (= location status data) are generated, with this can take place that they are present at the same time intervals (= synchronous).

Erfindungsgemäß kann eine intelligente Vorverarbeitung vor der Erstellung von Verkehrsprognosen, Verkehrsinformationen, Navigationsinformationen etc. erfolgen, welche im Prinzip aus räumlich und/oder zeitlich lückenhaften Meßdaten eine räumlich und zeitlich lückenlose Verkehrsdaten-Quelle simuliert (also virtuell erzeugt). Das Ergebnis dieser Vorverarbeitung 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.According to the invention, intelligent preprocessing before the creation of Traffic forecasts, traffic information, navigation information etc. are made, which in principle consists of spatially and / or temporally incomplete measurement data and simulated seamless traffic data source (i.e. generated virtually). The The result of this preprocessing is on road sections (also as Directional measurement cross sections RMQ) related artificially in time synchronized traffic data. These expediently have a uniform format such that they are at the same cyclic intervals and / or the same units present; the intervals can be, for example, one minute. In the Creation of the spatially / temporally seamless traffic database can be done by Error estimate in the calculation for the individual values of quality information can also be generated.

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 erfindungsgemaßen Verfahrens,
Fig. 2
im Verlaufe der Zeit von einem Sensor gemessene Meßdaten, aus einer historischen Datenbank entnommene Meßdaten und eine Fehlerabschätzung,
Fig. 3
als Tabelle grundsätzlich zur Vervollständigung von bestimmten Meßdatenlücken etc. geeignete Ersatzdatenquellen.
Further features and advantages result from the subclaims and the following description of an exemplary embodiment. It shows:
Fig. 1
components of a device for carrying out the method according to the invention as a block diagram,
Fig. 2
measurement data measured by a sensor over time, measurement data taken from a historical database and an error estimate,
Fig. 3
as a table, suitable substitute data sources to complete certain measurement data gaps, etc.

Figur 1 verdeutlicht den Datenfluß anhand eines Blockschaltbildes einer Vorrichtung zur Durchführung des erfindungsgemäßen Verfahrens.Figure 1 illustrates the data flow using a block diagram of a device to carry 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 3 (VIZ) kommende Daten (beispielsweise basierend auf Landesmeldestellen-Meldungen, Polizeifunk etc.).The measurement data used include those that are movable in the transport network Vehicles arranged sensors acquired data 1 (FCD), from stationary Data collected from road traffic sensors 2 (SES) and from another Traffic information center 3 (VIZ) incoming data (for example based on State registration office reports, 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 also represent in space and time Processing of sufficient accuracy without gaps (from data 1 to 3) completed location status data 4. The location status data 4 (speeds, Traffic density, traffic jams, etc.) are spatially complete in such a way that, for example, for a digital map of the road network with spatial subsections for each spatial subsection has a measurement date for a relevant point in time, which enables easier and better processing. Gaps in time For example, they can be such that for a sufficient number of before current times, recent times completed Measurement data (location status data) are available.

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 basierend auf den in der MDL 5 vervollständigten Ortszustandsdaten verwendet und optimiert weden. 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 takes place essentially in a multi-data logic MDL 5, in which essentially runs the method according to the invention. In the sub-modules M1 to M3 6 to 8 run traffic analysis methods, in which different traffic flow models based on those in MDL 5 completed location status data are used and optimized. The multi-model logic MML 9 links the results of the different analysis methods based modules M1 to M3, in particular in the form of a reliability / credibility analysis and selection.

Die Simulationskomponente SIM 10 berechnet aufgrund der von der Multimodell-Logik 9 erzeugten Daten eine Prognose für die Zukunft, wobei diese Zukunft eine auf die Daten bezogene Zukunft sein kann; für zu einem vergangenen Zeitpunkt erfaßte (und in MDL vervollständigte) Meßdaten ist die Zukunft gegenüber deren Erfassungszeitpunkt zu sehen, kann also auch die jetzige Gegenwart oder einen zwischen der Erfassung und der jetzigen Gegenwart liegenden Zeitpunkt betreffen (sie kann jedoch auch einen gegenüber der jetzigen Gegenwart künftigen Zeitpunkt betreffen). Bei einer Zukunftsprognose, ausgehend von zu einem vergangenen Zeitpunkt erfaßten Maßdaten für eine gegenüber deren Erfassungszeitpunkt liegende Zukunft, welche vor der aktuellen Gegenwart liegt, 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 1 bis 3) in die MDL einfließenden Meßdaten-Basis.The simulation component SIM 10 calculates on the basis of the multi-model logic 9 generated data a forecast for the future, this future one on the Data-related future can be; for previously recorded (and measurement data completed in MDL is the future compared to them Seeing the time of acquisition can also be the present or one between the time of recording and the present time (see However, it can also be a future point in time compared to the present affect). With a future forecast, starting from a past one Measured data recorded for a point of time lying opposite its time of acquisition The future, which lies before the current present, is an optimized utilization measured data through a more precise process analysis of processes (Congestion etc.) possible in the road network. The component generates HPR 11 from the current data generated by the MML 9 hydrographs (i.e. temporal courses the measured data) and tries to establish the relationship between traffic conditions and to learn certain selection characteristics. The results of the Simulation components 10 are connected to the feedback unit RER Multi-data logic fed back to optimize the (in addition to 1 to 3) in the MDL incoming 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 hydrographs and relationships between the HPR component Traffic conditions and selection features are (via a not shown here Module ZYR) also coupled as 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 aktuelle und/oder prognostizierte Verkehrszustände von Abschnitten des Straßenverkehrsnetzes repräsentieren.Based on the initial data of SIM 10, MML 9 and HPR 11, in a data fusion unit 14 creates data which current and / or predicted Represent 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 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 MDL 5 is that it is incomplete in space and / or time incoming measurement data 1 to 3 (from sensors etc.) by completing a to simulate seamlessly and spatially and temporally synchronous measurement data source, for simple high-quality processing (for traffic reports, To enable forecasts, navigation instructions etc.). Figure 2 illustrates the problem with incoming measurement data due to Measurement data history. In Figure 2, the axis pointing to the right shows the time and the upward axis the speed. The solid line shows at different times with a stationary sensor (SES) at one position Average vehicle speeds recorded in the road network (for example, all vehicles in one minute). The measurement data recorded by the sensor concern several past with respect to the current time, successive and recent times; this measurement data will integrated in such a way that their temporal course is subjected to an analysis and to Completion of other measurement data is used.

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.This is clearly explained, for example, with the help of a vehicle that is too passes a sensor in one place at a time and turns on after a certain time a different location behind the sensor (same or in the case of traffic jams etc. other determinable) speed. From different speeds of Vehicles at several points in time at the location of the sensor can therefore be suspected Vehicle speeds at locations behind (not available as measured value) the sensor as well as (in the case of spreading traffic jams in front of the sensor) closed become.

Neben Daten von stationären Sensoren kann dies auch mit von im Verkehr mitschwimmenden Fahrzeugen implementierten 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 used in traffic with floating vehicles implemented measuring sensors generated measuring data respectively; this measurement data is also incomplete, since it is only under certain Conditions and / or at certain time intervals; this too Measurement data from vehicles are usually transmitted as a package, in one Package several average speeds (of the vehicle) at different Locations (along a road driven by the vehicle) at different times (the measurement times) are included on the way 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 (VAS in Figur 1) 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 beispielsweise auch aus einer historischen Datenbank HPR vervollständigt werden. Figure 3 illustrates, by way of example as a table, that gaps in different conditions in incoming different generated measurement data with different Replacement data sources can be completed. Measurement data gaps in from stationary Detectors generated in the traffic network (SES) can with Substitute data sources from historical databases (HPR in Figure 1) and Traffic analysis system (VAS in Figure 1) can be completed, including the Measured data quality is possible through an error estimate (LOS estimate). Data failures in from another traffic information center (which on State registration offices, police reports etc. have access) coming data and data from a sensor detection system can, for example, also from a historical database HPR to be completed.

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 with a sensor detection system only certain lanes (= lanes) to lanes can be monitored by a road Track estimator, which is based on empirical values from monitored tracks tracks that are not monitored can be completed.

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.Unsupervised nodes of a traffic network, such as entrances and exits unknown between different measuring points of a sensor detection system Values for average speeds and / or number of vehicles require, where these unknown factors - if available - also by historical ones Databases can be completed relatively precisely.

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).A LOS estimator (for example according to FIG. 3) is used as a substitute data source usable. If the reporting behavior of stationary detectors (SES) in the Road traffic network provides that a detector always reports when a Switch between defined speed ranges in the ones he measured Measured data has taken place safely (local transmission criterion) and this the LOS estimation method is known can with each transmission of a data message (Forecast time) from a detector based on the transmitted LOS the road a forecast for the average speed will be made. The Forecast quality is guaranteed by half the width of the LOS if the forecast value is equated with the mean of the LOS. LOS (Level of Service) is included the quality of a road in the form of the speed that can be driven on it. A possible classification is from LOS 1 (bad, 0 to 30 km / h), LOS 2 (medium, 30 to 60 km / h), LOS 3 (good, 60 to 90 km / h), LOS 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 werden und der Ganglinienwert für die Geschwindigkeit um diese Differenz verschoben werden.The forecast 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 forecast value is equated with the average value (in the case, for example, 15 km / h) of the LOS, since a renewed data telegram from the detector would be sent if there were major deviations.
The LOS estimation method can also be used to shift a current curve (representing the course of time) present in the system for a directional measurement cross-section (for example, a measurement location in the form of a bridge in the case of stationary detectors) into the current LOS calculation, if a deviation of the last one current measured value of a currently valid curve for the cross section exists. In contrast to the last current measured value of the speed of the SES data, the difference to the curve value of the corresponding interval can be formed and the curve value for the speed 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 curve of a road traffic section is above the upper limit of a LOS range, the speeds of the The gait line can be lowered if the speed gait 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 time sequence in which the measurement and replacement data are provided can be seen from 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 gangway management system HPR is the first curve for the detector (from which the SES diagram shown comes) transmitted. If not, you can go to Data completion the chart of the previous day can be used 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 several past times relevant measurement data (ie a measurement data history) due to a LOS change (Average speed change on a section of road as above specified), and the LOS estimator transmits a based on this data Forecast for future times.

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 the signal due to another LOS change another section of measurement data (further Measurement data history), and the LOS estimator creates a new one based on this Forecast.

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 curve management system HPR updates the to The beginning of the day (t1) delivered curve. The new curve describes that Traffic is really better than the old gait line because the HPR subsystem is used Selection of the curve more information is available.

So können Lücken in den Meßdaten durch einen Rückgriff aus Ersatzdaten aus der historischen Datenquelle HPR beseitigt werden.Gaps in the measurement data can be made by using replacement data from the historical data source HPR are eliminated.

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.In the case of conflicting data from different sources (e.g. updated hydrographs / old hydrographs, LOS estimates / current hydrographs, Measurement data histories / current sensor measurement data) is a selection process based on the Measurable data quality executable. The data source can be selected for which most of the measurement data are available, or the absence of measurement data Substitute 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 (11)

Verfahren zur Vervollständigung und/oder Verifizierung von den Zustand eines Verkehrsnetzes betreffenden Daten in einer Verkehrszentrale,
wobei zu mehreren Meßorten im Verkehrsnetz mehrere Zeitpunkte innerhalb eines sich ab dem jetzigen Zeitpunkt zeitlich rückwärts erstreckenden Zeitraumes betreffende Meßdaten zum Zustand des Verkehrsnetzes an den Meßorten vorliegen,
wobei aus dem zeitlichen Werte-Verlauf mehrerer zu vergangenen Zeitpunkten erfaßter Meßdaten zu einem Meßort auf den aktuellen Zustand des Verkehrsnetzes repräsentierende Ortszustandsdaten zumindest an Orten geschlossen wird, für welche Orte keine Meßdaten vorliegen.Method for completing and / or verifying data relating to the state of a traffic network in a traffic center,
where at several measuring points in the traffic network there are several points in time of a measurement data relating to the state of the traffic network at the measuring points, which period extends backwards from the current point in time,
wherein from the temporal value profile of several measurement data recorded at past points in time for a measurement location, conclusions are drawn about location status data representing the current status of the traffic network, at least for locations for which no measurement data are available. Verfahren nach Anspruch 1,
dadurch gekennzeichnet,
daß der Zustand mehrerer Straßenabschnitte bestimmt wird.Method according to claim 1,
characterized,
that the condition of several road sections is determined. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß Meßdaten von an Straßen des Verkehrsnetzes stationär angeordneten Sensoren erfaßt werden.Method according to one of the preceding claims,
characterized,
that measurement data are recorded by sensors arranged stationary on roads of the traffic network. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß die Meßdaten Durchschnittsgeschwindigkeiten mehrerer Fahrzeuge an einer Stelle und/oder die Anzahl von die Stelle passierenden Fahrzeugen pro Zeiteinheit umfassen. Method according to one of the preceding claims,
characterized,
that the measurement data include average speeds of several vehicles at one location and / or the number of vehicles passing the location per unit of time. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß Meßdaten von in in Verkehrsnetzen beweglichen Fahrzeugen angeordneten Sensoren (FCD) erfaßte werden.Method according to one of the preceding claims,
characterized,
that measurement data are recorded by sensors (FCD) arranged in vehicles which are movable in traffic networks. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß die Meßdaten Geschwindigkeiten jeweils eines Fahrzeuges umfassen.Method according to one of the preceding claims,
characterized,
that the measurement data include speeds of one vehicle at a time. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß die den Zustand des Verkehrsnetzes an Orten ohne Meßdaten repräsentierenden Ortszustandsdaten so generiert werden, daß sie für jeden Ort und für gleiche Zeitintervalle, insbesondere für gleiche Zeitintervalle, wie die Erfassungsintervalle von Sensoren, vorliegen.Method according to one of the preceding claims,
characterized,
that the location status data representing the status of the traffic network at locations without measurement data are generated such that they are available for each location and for the same time intervals, in particular for the same time intervals as the detection intervals of sensors. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß die den Zustand des Verkehrsnetzes an Orten ohne Meßdaten repräsentierenden Ortszustandsdaten mittlere Fahrzeuggeschwindigkeiten und/oder Reisezeiten in jeweils einem Straßenabschnitt des Verkehrsnetzes repräsentieren.Method according to one of the preceding claims,
characterized,
that the state of the state data representing the state of the traffic network at locations without measurement data represent average vehicle speeds and / or travel times in each section of the traffic network. Verfahren nach einem der vorhergehenden Ansprüche,
dadurch gekennzeichnet,
daß die den Zustand des Verkehrsnetzes an Orten ohne Meßdaten repräsentierenden Ortszustandsdaten die Zahl der Fahrzeuge in jeweils einem Straßenabschnitt des Verkehrsnetzes repräsentieren. Method according to one of the preceding claims,
characterized,
that the state of the state data representing the state of the traffic network at locations without measurement data represents the number of vehicles in each section of the traffic network. Vorrichtung zur Durchführung des Verfahrens nach einem der vorhergehenden Ansprüche.Device for carrying out the method according to one of the preceding Expectations. Vorrichtung, insbesondere nach Anspruch 10, zur Vervollständigung oder Verifizierung von den Zustand eines Verkehrsnetzes betreffenden Meßdaten in einer Verkehrszentrale,
wobei zu mehreren Meßorten im Verkehrsnetz mehrere Zeitpunkte innerhalb eines sich ab dem jetzigen Zeitpunkt zeitlich rückwärts erstreckenden Zeitraumes betreffende Meßdaten betreffend den Zustand des Verkehrsnetzes an den Meßorten vorliegen,
wobei die Vorrichtung eine Meßdaten-Vervollständigungseinrichtung aufweist, die so ausgebildet ist, daß aus dem zeitlichen Werte-Verlauf zu mehreren vergangenen Zeitpunkten erfaßter Meßdaten zu einem Meßort auf den aktuellen Zustand des Verkehrsnetzes an Orten geschlossen wird, für welche Orte keine Meßdaten vorliegen.Device, in particular according to claim 10, for completing or verifying measurement data relating to the state of a traffic network in a traffic center,
where at several measuring points in the traffic network there are several points of time relating to the state of the traffic network at the measuring points within a period of time which extends backwards from the current point in time,
the device having a measurement data completion device which is designed in such a way that the current state of the traffic network at locations for which locations no measurement data are available is inferred from the temporal value profile of measurement data acquired at several past points in time at a measurement location.
EP00250249A 1999-07-23 2000-07-20 Method and device for cascaded state feedback Expired - Lifetime EP1071058B1 (en)

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EP0921509A2 (en) * 1997-10-16 1999-06-09 Navigation Technologies Corporation System and method for updating, enhancing or refining a geographic database using feedback

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EP0902405A2 (en) * 1997-09-11 1999-03-17 Siemens Aktiengesellschaft Method for sending traffic information
EP0921509A2 (en) * 1997-10-16 1999-06-09 Navigation Technologies Corporation System and method for updating, enhancing or refining a geographic database using feedback

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