EP2364494B1 - Method for approximating the time curve of traffic data - Google Patents

Method for approximating the time curve of traffic data Download PDF

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Publication number
EP2364494B1
EP2364494B1 EP09771264A EP09771264A EP2364494B1 EP 2364494 B1 EP2364494 B1 EP 2364494B1 EP 09771264 A EP09771264 A EP 09771264A EP 09771264 A EP09771264 A EP 09771264A EP 2364494 B1 EP2364494 B1 EP 2364494B1
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Prior art keywords
measurement
data
road
unit
data curve
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German (de)
French (fr)
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EP2364494A1 (en
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Bernhard Nowotny
Martin Reinthaler
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Osterreichisches Forschungs und Pruefzentrum Arsenal Gesselshaft Mbh
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Osterreichisches Forschungs und Pruefzentrum Arsenal Gesselshaft 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

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  • the invention relates to a method for approximating the time profile of traffic data according to the preamble of claim 1.
  • the invention relates to a device for approximating the time course of traffic data according to the preamble of claim 4.
  • the invention relates to a data carrier, a computer program and a computer program product.
  • the invention in particular the method according to the invention and the device according to the invention, are used in the field of automated traffic detection or traffic prediction.
  • Background of the invention is the determination of traffic data for a plurality of road sections, on which only isolated information is available.
  • Information can be determined for example by means of a stationary measuring device, but also by means of vehicles which are in flowing traffic.
  • the object of the invention is to solve the problems mentioned above and to provide a method and a device that provide continuous traffic information for these road sections even with sparse input data for certain predetermined road sections.
  • the invention solves this problem in a method of the type mentioned above with the features of the characterizing part of claim 1 and in a device having the features of the characterizing part of claim 4.
  • According to the temporal course of traffic data is approximated. From historical data, average group data profiles and average road data profiles are formed. Deviation time series are formed and using the time series analysis, optimal approximation values for these deviations are calculated. This method is applicable, both when complete data histories exist and when data of individual intervals are missing.
  • the group data progressions can be adapted successively to new traffic conditions.
  • Fig. 1 an inventive device for approximating the time course of traffic data is shown.
  • the device comprises a plurality of measuring devices, not shown, which determine or determine traffic data in the form of measured values at different measuring times.
  • This traffic data may concern either traffic flow or speed measurements.
  • a measuring device may, for example, be a permanently mounted sensor, for example located above a road. This sensor measures, for example, traffic flow or speed of passing vehicles and records the measurement data obtained in this way, whereby the measuring device assigns the time of recording or recording to the measurement data.
  • mobile measuring devices whose local position is variable. These data are then provided with a coding of the site, such as the GPS coordinates of the site, as well as with a time stamp. All measurement data records thus comprise the respective measured value, the measurement time of the recording and optionally an identifier and / or the coded position of the recording or recording instrument.
  • the measuring devices it is particularly necessary for the measuring devices to be located in an area immediately above or laterally above the road sections in a vehicle. Stationary measuring devices are fixedly arranged in the area of the road sections and assigned to them.
  • This association and grouping unit 1 comprises a grouping table in which a predetermined mutual association between road sections, groups of road sections and measuring devices or measuring positions is stored.
  • the number of predetermined Road sections divided into groups based on given criteria.
  • each group there are a plurality of road sections, each of the grouped road sections having similar characteristics, such as similar or identical top speeds, similar geometry, same type of road (highway, freeway, highway, local area), and so forth stationary measuring devices, so the assignment to a road section is unique, since the meter itself is assigned to the road section.
  • measurement data sets from mobile measuring instruments are available, it is also necessary to know the position of the measuring instrument in order to be assigned to a specific road segment.
  • the measuring positions in particular in the form of GPS coordinates, can be assigned to the road sections by means of a unit which is likewise not shown and which is included in the grouping and allocation unit 1.
  • the allocation and grouping unit allocates the measurement data sets and the respective road segments as well as the respective groups to each other in accordance with the allocation table.
  • Each measurement data set is provided in the course of this further processing with an identifier of this group or this road section.
  • the measurement data records are forwarded to the output of the assignment and grouping unit 1 and are present there in the form of, in particular digital, data.
  • the device comprises a unit 2 for forming the group data history, the input of which is connected to the output of the allocation and grouping unit.
  • This group data history forming unit 2 obtains a group time series for each group of road sections by time series forming the measurement data sets assigned to each group.
  • a periodically recurring measurement interval is predetermined, which is set in particular to a day or a week. All measuring times are recorded relative to the beginning of the respective measuring interval and assigned to the respective measured value or measured data record.
  • a mean group data course of the measured values over time, in particular over the measuring interval, is formed by time series formation by means of the measurement data sets assigned to the individual groups.
  • the group data history determined by time series generation is present for each group.
  • Fig. 1 an average road data approximation unit 3 is provided. At its input is the output of the assignment and grouping unit 1 and the output of the unit 2 for forming the group data history connected.
  • the procedure for determining the average road data course 92 is in Fig. 2 shown.
  • a typical, average road data course 92 is formed, in which the deviation between the measured value and the value of the group data profile present at the respective measuring time is first determined for each measured value or measurement data record assigned to the respective road section.
  • Fig. 2 shows a continuous group data course 91 and a multiplicity of deviations 95 between a measured value and the value of the group data profile 91 present at the respective measuring time.
  • time-of-flight series is formed.
  • the time-of-flight series is analyzed by a prior art time-series analysis method, an appropriate time-series model is identified, and the time series based on the time-series model is smoothed or a compensation function is formed.
  • the deviation time series determined in this way is added to the group data course 91 of the group to which the respective road section is assigned, the typical road data course 92, 92 'corresponding to the summation time profile thus formed. Even in those areas in which only fragmentary information about the traffic data of interest are available, a meaningful result can be formed over the typical road data course 92, 92 'by means of time series formation.
  • the typical road data paths 92, 92 'of the individual road sections are applied to the output of the road data flow approximation unit 3, 92'.
  • the average road data approximation unit 3 is followed by a unit 4 for approximating the interval data waveform.
  • the output of the assignment and grouping unit 1 is connected to the input for approximation of the interval data course 4.
  • the approximated interval data course 93, 93 'for the selected road section corresponds to the summation time curve formed and is present at the output of the unit 4 for approximating the interval data course 93, 93'.
  • a particular embodiment of the invention provides that for the determination of the group data courses 91, measurement data from those three to twelve months, which immediately precede the measurement time, are used. This is achieved in a device according to the invention in that in the unit 2 for forming the group data history 91, a group data control unit 21 is provided which deletes those records whose recording date is older than a predetermined period of time.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Description

Die Erfindung betrifft ein Verfahren zur Approximation des zeitlichen Verlaufs von Verkehrsdaten gemäß dem Oberbegriff des Anspruchs 1.The invention relates to a method for approximating the time profile of traffic data according to the preamble of claim 1.

Ferner betrifft die Erfindung eine Vorrichtung zur Approximation des zeitlichen Verlaufs von Verkehrsdaten gemäß dem Oberbegriff des Anspruchs 4.Furthermore, the invention relates to a device for approximating the time course of traffic data according to the preamble of claim 4.

Weiters betrifft die Erfindung einen Datenträger, ein Computerprogramm sowie ein Computerprogrammprodukt.Furthermore, the invention relates to a data carrier, a computer program and a computer program product.

Die Erfindung, insbesondere das erfindungsgemäße Verfahren sowie die erfindungsgemäße Vorrichtung, werden im Bereich der automatisierten Verkehrserfassung bzw. Verkehrsvorhersage eingesetzt.The invention, in particular the method according to the invention and the device according to the invention, are used in the field of automated traffic detection or traffic prediction.

Hintergrund der Erfindung ist die Ermittlung von Verkehrsdaten für eine Vielzahl von Straßenabschnitten, auf welchen nur vereinzelt Informationen vorliegen. Informationen können beispielsweise mittels eines stationären Messgeräts, aber auch mittels Fahrzeugen ermittelt werden, welche sich im fließenden Verkehr befinden.Background of the invention is the determination of traffic data for a plurality of road sections, on which only isolated information is available. Information can be determined for example by means of a stationary measuring device, but also by means of vehicles which are in flowing traffic.

Insbesondere bei weniger häufig befahrenen Straßen besteht bei der Erfassung der Daten wie auch bei der Vorhersage der Daten das Problem, dass aufgrund der fehlenden Messdaten für einzelne Zeitintervalle unzureichende Informationen über den aktuellen Verkehrsstand zur Verfügung stehen. Beispielsweise die US 2008/071465A1 beschreibt eine derartige Erfassung von Verkehrsdaten.Particularly in the case of less frequented roads, when capturing the data as well as during the prediction of the data, there is the problem that due to the missing measurement data for individual time intervals, insufficient information about the current traffic status is available. For example, the US 2008 / 071465A1 describes such detection of traffic data.

Aufgabe der Erfindung ist es, die eingangs genannten Probleme zu lösen und ein Verfahren sowie eine Vorrichtung zur Verfügung zu stellen, welche auch bei spärlichen Eingangsdaten für bestimmte vorgegebene Straßenabschnitte kontinuierliche Verkehrsinformation für diese Straßenabschnitte liefern.The object of the invention is to solve the problems mentioned above and to provide a method and a device that provide continuous traffic information for these road sections even with sparse input data for certain predetermined road sections.

Die Erfindung löst diese Aufgabe bei einem Verfahren der eingangs genannten Art mit den Merkmalen des Kennzeichens des Anspruchs 1 sowie bei einer Vorrichtung mit den Merkmalen des Kennzeichens des Anspruchs 4. Erfindungsgemäß wird der zeitliche Verlauf von Verkehrsdaten approximiert. Es werden aus historischen Daten mittlere Gruppen-Datenverläufe und mittlere Straßen-Datenverläufe gebildet. Es werden Abweichungszeitreihen gebildet und mit Hilfe der Zeitreihenanalyse optimale Approximationswerte für diese Abweichungen berechnet. Dieses Verfahren ist anwendbar, sowohl wenn komplette Datenverläufe existieren als auch wenn Daten einzelner Intervalle fehlen.The invention solves this problem in a method of the type mentioned above with the features of the characterizing part of claim 1 and in a device having the features of the characterizing part of claim 4. According to the temporal course of traffic data is approximated. From historical data, average group data profiles and average road data profiles are formed. Deviation time series are formed and using the time series analysis, optimal approximation values for these deviations are calculated. This method is applicable, both when complete data histories exist and when data of individual intervals are missing.

Erfindungsgemäß besteht der Vorteil, dass selbst bei Vorliegen spärlicher Verkehrsinformationen ein kontinuierlicher Verlauf der interessierenden Verkehrsgröße ermittelt werden kann. Somit ist es möglich, selbst auf wenig befahrenen Straßen oder auf Straßen, die nur selten mit mobilen Messfahrzeugen befahren werden, hinreichend genaue Information über die in Betracht stehenden Verkehrsdaten zu erhalten.According to the invention, there is the advantage that even in the presence of sparse traffic information, a continuous course of the traffic variable of interest can be determined. Thus, it is possible even on low-traffic roads or on Roads that are rarely used with mobile measuring vehicles to obtain sufficiently accurate information about the traffic data in question.

Mit den Kennzeichen der Ansprüche 2 und 5 können die Gruppen-Datenverläufe sukzessive an neue Verkehrsgegebenheiten angepasst werden.With the characteristics of claims 2 and 5, the group data progressions can be adapted successively to new traffic conditions.

Mit den Kennzeichen der Ansprüche 3 und 6 können die Straßen-Datenverläufe sukzessive an neue Verkehrsgegebenheiten angepasst werden.

  • Fig. 1 zeigt schematisch den Aufbau einer erfindungsgemäßen Vorrichtung.
  • Fig. 2 zeigt schematisch die Bildung eines Straßen-Datenverlaufs basierend auf einer Anzahl von Messwerten und dem zugehörigen Gruppen-Datenverlauf.
  • Fig. 3 zeigt die Bildung eines approximierten Straßen-Datenverlaufs bei vorgegebenem mittleren Straßen-Datenverlauf sowie einer Vielzahl von Messwerten.
With the characteristics of claims 3 and 6, the road data courses can be successively adapted to new traffic conditions.
  • Fig. 1 schematically shows the structure of a device according to the invention.
  • Fig. 2 schematically shows the formation of a road data history based on a number of measurements and the associated group data history.
  • Fig. 3 shows the formation of an approximated road data course for a given average road data course as well as a multiplicity of measured values.

In Fig. 1 ist eine erfindungsgemäße Vorrichtung zur Approximation des zeitlichen Verlaufs von Verkehrsdaten dargestellt. Die Vorrichtung umfasst eine Vielzahl von nicht dargestellten Messgeräten, welche zu unterschiedlichen Messzeitpunkten Verkehrsdaten in Form von Messwerten bestimmen oder ermitteln. Diese Verkehrsdaten können entweder Messungen des Verkehrsflusses oder der Geschwindigkeit betreffen. Ein Messgerät kann beispielsweise ein fest montierter, beispielsweise oberhalb einer Straße befindlicher Sensor sein. Dieser Sensor misst beispielsweise Verkehrsfluss oder Geschwindigkeit der vorbeifahrenden Fahrzeuge und zeichnet die so gewonnenen Messdaten auf, wobei das Messgerät den Messdaten den Zeitpunkt der Aufzeichnung oder Aufnahme zuordnet.In Fig. 1 an inventive device for approximating the time course of traffic data is shown. The device comprises a plurality of measuring devices, not shown, which determine or determine traffic data in the form of measured values at different measuring times. This traffic data may concern either traffic flow or speed measurements. A measuring device may, for example, be a permanently mounted sensor, for example located above a road. This sensor measures, for example, traffic flow or speed of passing vehicles and records the measurement data obtained in this way, whereby the measuring device assigns the time of recording or recording to the measurement data.

Alternativ können auch mobile Messgeräte vorgesehen werden, deren örtliche Position variabel ist. Diese Daten werden anschließend mit einer Codierung des Messorts, beispielsweise den GPS-Koordinaten des Messorts, sowie mit einem Zeitstempel versehen. Alle Messdatensätze umfassen somit den jeweiligen Messwert, den Messzeitpunkt der Aufnahme sowie gegebenenfalls eine Kennung und/oder die codierte Position des aufzeichnenden oder aufnehmenden Messgeräts. Bei mobilen Messgeräten ist es insbesondere erforderlich, dass sich die Messgeräte in einem Bereich unmittelbar oberhalb bzw. seitlich oberhalb der Straßenabschnitte in einem Fahrzeug befinden. Stationäre Messgeräte sind im Bereich der Straßenabschnitte fest angeordnet und diesen zugeordnet.Alternatively, it is also possible to provide mobile measuring devices whose local position is variable. These data are then provided with a coding of the site, such as the GPS coordinates of the site, as well as with a time stamp. All measurement data records thus comprise the respective measured value, the measurement time of the recording and optionally an identifier and / or the coded position of the recording or recording instrument. In the case of mobile measuring devices, it is particularly necessary for the measuring devices to be located in an area immediately above or laterally above the road sections in a vehicle. Stationary measuring devices are fixedly arranged in the area of the road sections and assigned to them.

Die so ermittelten Messdatensätze gelangen zu einer Zuordnungs- und Gruppierungseinheit. Diese Zuordnungs- und Gruppierungseinheit 1 umfasst eine Gruppierungstabelle, in der eine vorgegebene gegenseitige Zuordnung zwischen Straßenabschnitten, Gruppen von Straßenabschnitten und Messgeräten bzw. Messpositionen abgespeichert ist. Hierbei ist die Anzahl der vorgegebenen Straßenabschnitte auf Grund vorgegebener Kriterien in Gruppen unterteilt. In jeder Gruppe befindet sich eine Vielzahl von Straßenabschnitten, wobei jeder der in einer Gruppe befindlichen Straßenabschnitte ähnliche Eigenschaften aufweist, beispielsweise ähnliche oder idente Höchstgeschwindigkeiten, ähnliche Geometrie, gleiche Art von Straße (Autobahn, Schnellstraße, Bundesstraße, Ortsgebiet), usw. Liegen Messdatensätze von stationären Messgeräten vor, so ist die Zuordnung zu einem Straßenabschnitt eindeutig, da das Messgerät selbst dem Straßenabschnitt zugeordnet ist. Liegen hingegen Messdatensätze von mobilen Messgeräten vor, ist für die Zuordnung zu einem bestimmten Straßenabschnitt weiters die Kenntnis der Position des Messgeräts erforderlich. Mittels einer nicht dargestellten ebenfalls von der Gruppierungs- und Zuordnungseinheit 1 umfassten Einheit können die Messpositionen, insbesondere in Form von GPS-Koordinaten, den Straßenabschnitten zugeordnet werden. Die Zuordnungs- und Gruppierungseinheit ordnet in Übereinstimmung mit der Zuordnungstabelle die Messdatensätze und die jeweiligen Straßenabschnitte sowie die jeweiligen Gruppen einander zu. Jeder Messdatensatz wird im Zuge dieser Weiterverarbeitung mit einer Kennung dieser Gruppe bzw. dieses Straßenabschnittes versehen. Die Messdatensätze werden an den Ausgang der Zuordnungs- und Gruppierungseinheit 1 weitergeleitet und liegen dort in Form von, insbesondere digitalen, Daten vor.The measured data records thus obtained arrive at an allocation and grouping unit. This association and grouping unit 1 comprises a grouping table in which a predetermined mutual association between road sections, groups of road sections and measuring devices or measuring positions is stored. Here, the number of predetermined Road sections divided into groups based on given criteria. In each group there are a plurality of road sections, each of the grouped road sections having similar characteristics, such as similar or identical top speeds, similar geometry, same type of road (highway, freeway, highway, local area), and so forth stationary measuring devices, so the assignment to a road section is unique, since the meter itself is assigned to the road section. If, on the other hand, measurement data sets from mobile measuring instruments are available, it is also necessary to know the position of the measuring instrument in order to be assigned to a specific road segment. The measuring positions, in particular in the form of GPS coordinates, can be assigned to the road sections by means of a unit which is likewise not shown and which is included in the grouping and allocation unit 1. The allocation and grouping unit allocates the measurement data sets and the respective road segments as well as the respective groups to each other in accordance with the allocation table. Each measurement data set is provided in the course of this further processing with an identifier of this group or this road section. The measurement data records are forwarded to the output of the assignment and grouping unit 1 and are present there in the form of, in particular digital, data.

Ferner umfasst die erfindungsgemäße Vorrichtung eine Einheit 2 zur Bildung des Gruppen-Datenverlaufs, deren Eingang an den Ausgang der Zuordnungs- und Gruppierungseinheit angeschlossen ist. Diese Einheit 2 zur Bildung des Gruppen-Datenverlaufs ermittelt für jede Gruppe von Straßenabschnitten mittels der, der jeweiligen Gruppe zugeordneten, Messdatensätze durch Zeitreihenbildung eine Gruppenzeitreihe.Furthermore, the device according to the invention comprises a unit 2 for forming the group data history, the input of which is connected to the output of the allocation and grouping unit. This group data history forming unit 2 obtains a group time series for each group of road sections by time series forming the measurement data sets assigned to each group.

Zur einfachen Vergleichbarkeit der einzelnen Messdatensätze untereinander wird vorgesehen, dass ein periodisch wiederkehrendes Messintervall vorgegeben wird, welches insbesondere auf einen Tag oder eine Woche festgesetzt wird. Alle Messzeitpunkte werden bezogen auf den Anfang des jeweiligen Messintervalls aufgezeichnet und dem jeweiligen Messwert bzw. Messdatensatz zugeordnet.For easy comparability of the individual measurement data sets with each other, it is provided that a periodically recurring measurement interval is predetermined, which is set in particular to a day or a week. All measuring times are recorded relative to the beginning of the respective measuring interval and assigned to the respective measured value or measured data record.

Für jede Gruppe wird mittels der den einzelnen Gruppen zugeordneten Messdatensätze durch Zeitreihenbildung ein mittlerer Gruppen-Datenverlauf der Messwerte über die Zeit, insbesondere über das Messintervall, gebildet. Am Ausgang der Einheit 2 zur Bildung des Gruppen-Datenverlaufs liegt für jede Gruppe der durch Zeitreihenbildung ermittelte Gruppen-Datenverlauf an.For each group, a mean group data course of the measured values over time, in particular over the measuring interval, is formed by time series formation by means of the measurement data sets assigned to the individual groups. At the output of the group data formation unit 2, the group data history determined by time series generation is present for each group.

Ferner ist in Fig. 1 eine Einheit 3 zur Approximation des mittleren Straßen-Datenverlaufs vorgesehen. An deren Eingang ist der Ausgang der Zuordnungs- und Gruppierungseinheit 1 sowie der Ausgang der Einheit 2 zur Bildung des Gruppen-Datenverlaufs angeschlossen. Das Vorgehen zur Bestimmung des mittleren Straßen-Datenverlaufs 92 ist in Fig. 2 dargestellt. Für jeden Straßenabschnitt wird ein typischer, mittlerer Straßen-Datenverlauf 92 gebildet, in dem zunächst für jeden, dem jeweiligen Straßenabschnitt zugeordneten Messwert oder Messdatensatz die Abweichung zwischen dem Messwert und dem zum jeweiligen Messzeitpunkt vorliegenden Wert des Gruppen-Datenverlaufs ermittelt wird. Fig. 2 zeigt einen durchgehenden Gruppen-Datenverlauf 91 sowie eine Vielzahl von Abweichungen 95 zwischen einem Messwert und dem zum jeweiligen Messzeitpunkt vorliegenden Wert des Gruppen-Datenverlaufs 91. Aus diesen Abweichungen sowie den Zeitpunkten ihrer Aufnahme wird eine Abweichungszeitreihe gebildet. Die Abweichungszeitreihe wird mit einem Verfahren zur Zeitreihen-Analyse nach dem Stand der Technik analysiert, ein passendes Zeitreihenmodell identifiziert und die Zeitreihe auf Basis des Zeitreihenmodelles geglättet bzw. eine Ausgleichsfunktion gebildet.Furthermore, in Fig. 1 an average road data approximation unit 3 is provided. At its input is the output of the assignment and grouping unit 1 and the output of the unit 2 for forming the group data history connected. The procedure for determining the average road data course 92 is in Fig. 2 shown. For each road section, a typical, average road data course 92 is formed, in which the deviation between the measured value and the value of the group data profile present at the respective measuring time is first determined for each measured value or measurement data record assigned to the respective road section. Fig. 2 shows a continuous group data course 91 and a multiplicity of deviations 95 between a measured value and the value of the group data profile 91 present at the respective measuring time. From these deviations as well as the time points of their recording, a time-of-flight series is formed. The time-of-flight series is analyzed by a prior art time-series analysis method, an appropriate time-series model is identified, and the time series based on the time-series model is smoothed or a compensation function is formed.

Die so ermittelte Abweichungszeitreihe wird zum Gruppen-Datenverlauf 91 derjenigen Gruppe, welcher der jeweilige Straßenabschnitt zugeordnet ist, addiert, wobei der typische Straßen-Datenverlauf 92, 92' dem so gebildeten Summenzeitverlauf entspricht. Auch in denjenigen Bereichen, in welchen nur bruchstückhaft Informationen über die interessierenden Verkehrsdaten vorhanden sind, kann mittels Zeitreihenbildung ein aussagekräftiges Resultat über den typischen Straßen-Datenverlauf 92, 92' gebildet werden. Die typischen Straßen-Datenverläufe 92, 92' der einzelnen Straßenabschnitte liegen am Ausgang der Einheit 3 zur Approximation des Straßen-Datenverlaufs 92, 92' an. Der Einheit 3 zur Approximation des mittleren Straßen-Datenverlaufs ist eine Einheit 4 zur Approximation des Intervall-Datenverlaufs nachgeschaltet. Der Ausgang der Zuordnungs- und Gruppierungseinheit 1 ist an den Eingang zur Approximation des Intervall-Datenverlaufs 4 angeschlossen.The deviation time series determined in this way is added to the group data course 91 of the group to which the respective road section is assigned, the typical road data course 92, 92 'corresponding to the summation time profile thus formed. Even in those areas in which only fragmentary information about the traffic data of interest are available, a meaningful result can be formed over the typical road data course 92, 92 'by means of time series formation. The typical road data paths 92, 92 'of the individual road sections are applied to the output of the road data flow approximation unit 3, 92'. The average road data approximation unit 3 is followed by a unit 4 for approximating the interval data waveform. The output of the assignment and grouping unit 1 is connected to the input for approximation of the interval data course 4.

Für einen ausgewählten Straßenabschnitt wird mittels der in einem einzigen vorgegebenen Messintervall aufgezeichneten Datensätze ein approximierter Intervall-Datenverlauf 93 für dieses vorgegebene Messintervall gebildet, indem die folgenden Schritte durchgeführt werden:

  • Für jeden dem ausgewählten Straßenabschnitt sowie dem vorgegebenen Messintervall zugeordneten Messwert oder Messdatensatz wird die Abweichung zwischen dem Messwert und dem zum jeweiligen Messzeitpunkt vorliegenden Wert des mittleren Straßen-Datenverlaufs 92 ermittelt bzw. gebildet. Üblicherweise erfolgt die Erfassung der Messdaten in sequentieller Reihenfolge, sodass auch die Berechnung der Abweichung in sequentieller Reihenfolge durchgeführt wird, jeweils einschließlich der neuesten Messdaten. Anschließend wird eine Abweichungszeitreihe mittels der ermittelten Abweichungen 96 sowie der diesen zugeordneten Messpunkte 94 gebildet. Die Abweichungszeitreihe wird mit einem Verfahren zur Zeitreihen-Analyse nach dem Stand der Technik analysiert, ein passendes Zeitreihenmodell identifiziert und die Zeitreihe auf Basis des Zeitreihenmodelles gefiltert bzw. ermittelt. Die so ermittelte Abweichungszeitreihe wird zum approximierten Intervall-Datenverlauf des ausgewählten Straßenabschnitts addiert. Der approximierte Intervall-Datenverlauf entspricht somit näherungsweise dem zeitlichen Verlauf der Verkehrsdaten im vorgegebenen Messintervall sowie auf dem vorgegebenen Straßenabschnitt.
For a selected road segment, an approximate interval data history 93 for this predetermined measurement interval is formed by means of the data records recorded in a single predetermined measurement interval, by performing the following steps:
  • For each measured value or measured data record associated with the selected road section and the predetermined measuring interval, the deviation between the measured value and the value of the average road data course 92 present at the respective measuring time is determined or formed. Typically, the acquisition of the measurement data is performed in sequential order, so that the calculation of the deviation is performed in sequential order, each including the latest measurement data. Subsequently, a deviation time series is formed by means of the determined deviations 96 and the measuring points 94 assigned to them. The time-of-deviation series is determined by a state-of-the-art time-series analysis method analyzes the technology, identifies a suitable time series model and filters the time series based on the time series model. The thus determined deviation time series is added to the approximated interval data history of the selected road segment. The approximated interval data course thus corresponds approximately to the time profile of the traffic data in the predetermined measuring interval and on the given road section.

Diese Verfahrensschritte werden bei einer erfindungsgemäßen Vorrichtung von der Einheit 4 zur Approximation des Intervall-Datenverlaufs durchgeführt. Der approximierte Intervall-Datenverlauf 93, 93' für den ausgewählten Straßenabschnitt entspricht dem gebildeten Summenzeitverlauf und liegt am Ausgang der Einheit 4 zur Approximation des Intervall-Datenverlaufs 93, 93' an.These method steps are performed in a device according to the invention of the unit 4 for approximating the interval data history. The approximated interval data course 93, 93 'for the selected road section corresponds to the summation time curve formed and is present at the output of the unit 4 for approximating the interval data course 93, 93'.

Eine besondere Ausführungsform der Erfindung sieht vor, dass für die Ermittlung der Gruppen-Datenverläufe 91 Messdaten aus denjenigen drei bis zwölf Monaten, welche dem Messzeitpunkt unmittelbar vorangehen, herangezogen werden. Dies wird bei einer erfindungsgemäßen Vorrichtung dadurch erreicht, dass in der Einheit 2 zur Bildung des Gruppen-Datenverlaufs 91 eine Gruppendatenstandskontrolleinheit 21 vorgesehen ist, welche diejenigen Datensätze löscht, deren Aufzeichnungsdatum länger als eine vorgegebene Zeitspanne zurückliegt.A particular embodiment of the invention provides that for the determination of the group data courses 91, measurement data from those three to twelve months, which immediately precede the measurement time, are used. This is achieved in a device according to the invention in that in the unit 2 for forming the group data history 91, a group data control unit 21 is provided which deletes those records whose recording date is older than a predetermined period of time.

In Analogie dazu kann vorgesehen werden, dass für die Ermittlung der mittleren Straßen-Datenverläufe 92 Messdaten aus denjenigen drei bis zwölf Monaten herangezogen werden, welche dem Messzeitpunkt unmittelbar vorangehen. Dies wird in analoger Weise dadurch erreicht, dass in der Einheit 3 zur Approximation des mittleren Straßen-Datenverlaufs 92 eine Straßendatenstandskontrolleinheit 31 vorgesehen ist, welche Datensätze löscht, deren Aufzeichnungsdatum länger als eine vorgegebene Zeitspanne zurückliegt.By analogy with this, it can be provided that for the determination of the mean road data courses 92, measurement data from those three to twelve months are used, which immediately precede the measurement instant. This is achieved in an analogous manner by providing in the middle road data approximation unit 3 a road data status control unit 31 which deletes records whose recording date is longer than a predetermined period of time.

Claims (11)

  1. A process for the approximation of time curves of traffic data from a road section selected from a number of predetermined road sections,
    - traffic data being determined in the form of measurement data at a plurality of different measurement locations at different measurement times at the given road sections,
    - a periodic measuring interval, particularly of one day or one week, being set and all measurement times being recorded in relation to the end of the respective measuring interval and allocated to the respective measurement value,
    - measurement data sets, comprising the respective measurement value, the measurement time of its recording and, optionally, an identification or the position of the measurement location, being generated and allocated to the road section in which the measurement was taken, based on a predetermined allocation between the measurement location and the position respectively to the road sections, characterized in
    a) that the number of predetermined road sections is subdivided into groups based on given criteria,
    - an average group data curve (91) of the measurement values being determined over the time for each group by establishing time series based on the measurement data sets allocated to the individual groups,
    b) that an average road data curve (92) is established for each individual road section by
    - determining, for each measured value or measurement data allocated to the respective individual road section, the deviation between said measured value and the value of the group data curve (91) at the respective measurement time,
    - establishing a deviation time series based on the determined deviations and the allocated measurement times, and
    - adding the thus established deviation time series to the group data curve (91) of the group allocated to the respective individual road section, the average road data curve (92) corresponding to the thus obtained total time series, and
    c) that an approximated interval data curve (93) is established for the predetermined measurement interval for the selected road section based on the data sets recorded in a single predetermined measurement interval, by
    - the deviation between the measured value and the value of the average road data curve (92) at the respective measurement time is determined for each measurement value or measurement data set allocated to the predetermined road section and the respective predetermined measurement interval,
    - a deviation time series is established based on the determined deviations and their allocated measurement times, and
    - adding the thus established deviation time series to the approximated road data curve (92) of the selected road section,
    so that an approximated interval data curve (93) approximately corresponds to the curve of the traffic data and the thus established total time series.
  2. The process according to claim 1, characterized in that
    measurement data from three to twelve months immediately preceding the measurement time are used for the establishment of the average group data curves (91).
  3. The process according to claim 1 or claim 2, characterized in that
    measurement data from three to twelve months immediately preceding the measurement time are used for the establishment of the average road data curves.
  4. A device for the approximation of time curves of traffic data from a road section selected from a number of predetermined road sections, using
    - a plurality of measurement devices which determine traffic data in the form of measurement data at different measurement times,
    - said measurement devices being located in the areas of predetermined road sections and/or being allocated to them, and
    - said measurement devices allocating a measurement time to each measurement value and recording the measurement times in relation to the end of a periodic measurement interval, particularly of a day or a week, and allocating them to the measurement values, and
    - the measurement devices generating measurement data sets, comprising the respective measurement value, the measurement time of its recording, and, optionally, an identification and/or an encoded position of the recording or storing measurement device,
    characterized in
    a) an allocation and grouping unit (1) to which the measurement data sets generated by the measurement devices are supplied,
    - said allocation and grouping unit (1) comprising a grouping table in which predetermined mutual allocations of predetermined road sections, groups of road sections and measurement positions are stored, and
    - said allocation and grouping unit (1) allocating the measurement data sets to the predetermined road sections and groups according to the allocation table and providing them with an identification corresponding to the groups and the road section respectively, the thus processed measurement data sets being output at the output of said allocation and grouping unit (1),
    b) a unit (2) for establishing an average group data curve, which is connected to the allocation and grouping unit (1),
    said unit (2) for establishing the average group data curve (91) for each group of road sections determining an average group data curve (91) based on the measurement data sets allocated to the respective group by forming a time series and providing the average group data curves (91) allocated to the groups,
    c) a unit (3) for the approximation of the average road data curve (92) of a road section, whose input is connected to the output of the allocation and grouping unit (1) and to the output of the unit (2) for establishing the group data curve (91),
    - said unit (3) for the approximation of the average road data curve (92) establishing an average road data curve (92) for each individual road section, by
    - determining the deviation between the measured value and the value of the group data curve (91) at the respective measurement time for each measured value and/or measured data set allocated to the respective road section through unit (3) for the approximation of the road data curve (92),
    - establishing through unit (3) a deviation time series based on the determined deviations and the allocated measurement times for the approximation of the average road data curve (92), and
    - adding through unit (3) the thus established deviation time series to the group data curve (91) of the group to which the respective individual road section is allocated, the average road data curve (92) corresponding to the thus obtained total time series and the road data curves (92) of the individual road sections being output at the output of the unit (3) for the approximation of the road data curve (92),
    d) a unit (4) for the approximation of the interval data curve (93) for the selected road section, to which the output of the unit (3) for the approximation of the average road data curve (92) and the output of the allocation and grouping unit (1) are connected,
    - said unit (4) for the approximation of the interval data curve (93) establishing an approximated road data curve (92) for the predetermined measurement interval, based on the data sets recorded during a single predetermined measurement interval for the selected road section, by
    - determining the deviation between the measured value and the value of the road data curve (92) at the respective measurement time for each measured value and/or measured data set allocated to the respective road section and the respective predetermined measurement interval through unit (4) for the approximation of the interval data curve (93),
    - said unit (4) for the approximation of the interval data curve (93) establishing a deviation time series based on the determined deviations and the allocated measurement times,
    - said unit (4) for the approximation of the interval data curve (93) adding the thus established deviation time series to the road data curve (92) of the selected road section, and
    - the approximated interval data curve (93) for the selected road section corresponding to the thus established total deviation time series and being output at the output of said unit (4) for the approximation of the interval data curve (93).
  5. The device according to claim 4, characterized in that a group data stock control unit (21) is provided as part of the unit (2) for establishing the group data curve (91) for erasing the data sets which were recorded more than a predetermined time ago.
  6. The device according to claim 4 or claim 5, characterized in that a road data stock control unit (31) is provided as part of the unit (3) for the approximation of the average road data curve (92) for erasing the data sets which were recorded more than a predetermined time ago.
  7. A data carrier on which a program for executing a process according to any one of the claims 1 to 3 is stored.
  8. A computer program with programming code means for executing a process according to any one of the claims 1 to 3, when said program is run on a computer.
  9. The computer program according to claim 8 which is stored on a data carrier.
  10. A data carrier with electronically readable control signals which can co-operate with a programmable computer system in order to execute a process according to any one of the claims 1 to 3.
  11. A computer program product with a programming code for executing a process according to any one of the claims 1 to 3, if the program is run on a computer.
EP09771264A 2008-12-05 2009-12-04 Method for approximating the time curve of traffic data Not-in-force EP2364494B1 (en)

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AT19052008A AT507619B1 (en) 2008-12-05 2008-12-05 PROCESS FOR APPROXIMATING THE TIMELY OF TRAFFIC DATA
PCT/AT2009/000473 WO2010063054A1 (en) 2008-12-05 2009-12-04 Method for approximating the time curve of traffic data

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