EP2116981B1 - Procédé et dispositif de détermination de longueurs de retenue à hauteur de signaux lumineux - Google Patents

Procédé et dispositif de détermination de longueurs de retenue à hauteur de signaux lumineux Download PDF

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Publication number
EP2116981B1
EP2116981B1 EP20090090002 EP09090002A EP2116981B1 EP 2116981 B1 EP2116981 B1 EP 2116981B1 EP 20090090002 EP20090090002 EP 20090090002 EP 09090002 A EP09090002 A EP 09090002A EP 2116981 B1 EP2116981 B1 EP 2116981B1
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Prior art keywords
traffic
data
model
density
traffic model
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German (de)
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EP2116981A3 (fr
EP2116981A2 (fr
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Thorsten Neumann
Peter Dr. Wagner
Rüdiger Dr. Ebendt
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Deutsches Zentrum fuer Luft und Raumfahrt eV
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Deutsches Zentrum fuer Luft und Raumfahrt eV
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/07Controlling traffic signals
    • G08G1/08Controlling traffic signals according to detected number or speed of vehicles

Definitions

  • the invention relates to a method and a device for determining tailback lengths of traffic signal systems.
  • the essential problem of traffic situation detection in traffic today is generally based on a comparatively thin data basis to make meaningful or correct statements about the current traffic condition in each considered transport network.
  • the reason for the thin database is primarily to mention that the majority of currently used measuring equipment (induction loops, 7) provides only local information, so that no direct traffic data can be measured for the road sections between two such detectors.
  • the simplest method of traffic monitoring is manual traffic counting, where on-site traffic observations are carried out by appropriate persons.
  • This form of traffic situation detection can, however, usefully take place only within a limited time frame and sometimes provides only very rough data. Consequently, such data can hardly be used to obtain comprehensive and timely data Traffic information is used. Rather, they serve as purely historical information that is used primarily for offline planning purposes and as empirical values.
  • the smarter traffic detection methods either use appropriate traffic models or skillfully link the readings of multiple detectors to obtain more information.
  • a method with a simple balancing approach is known, in which the backpressure length is estimated by comparing the number of inflowing and outflowing vehicles, the inflow being controlled in each case by means of a detector.
  • a simple traffic model is used by assuming a uniform approach of the jammed vehicles with a constant, temporal distance for the departing vehicles.
  • the outflow is also controlled by means of a loop detector.
  • FCD Fluorescence Downlink Deformation
  • the positions are like that Floating Cars are sent at certain times to a central office, where they can be evaluated together with the data of other floating cars.
  • FCD systems are even stronger than the traffic observation with video cameras and unlike the classic traffic detectors already conceptually to a comprehensive traffic situation detection in a position.
  • FCD fleet sizes used in the current FCD systems are very small in relation to the size of the considered transport network, so that all pure FCD methods are currently used with an extremely high FCD thin data basis.
  • floating-car data is purely supportive in many applications and merely serves to control traffic data obtained by other means.
  • the traffic data obtained in this way is very noisy, especially in urban traffic networks, which often complicates the further evaluation. This is because the travel times vary greatly from the particular traffic signal phase that the particular FCD vehicle encounters (e.g., green wave or, conversely, many red phases).
  • travel times which in the situation described are generally not necessarily related to the individual network edges of the underlying digital map, are difficult to interpret in the context of traffic management, ie especially with regard to the control of traffic lights.
  • From the DE 100 18 562 C1 is a method for obtaining traffic data for a traffic network with traffic-controlled network nodes and these connecting route edges by moving mitbeezeden signaling vehicles known, data acquisition operations are not triggered at least for successively traveling network nodes each before leaving a branching into the respective network node route edge and in the respective data acquisition process time stamp information is obtained as traffic data indicating a reporting time related to the respective network node not earlier than the time of leaving the relevant route edge and not later than the time at which the reporting vehicle considered a portion of a subsequently traveled route edge before a next one Network node reached.
  • the required form of the reporting times increased demands on the equipment of floating cars used compared to the currently commonly used method that floating cars simply at regular intervals (typically 30 seconds to 5 minutes) transmit their current position data ,
  • a method for determining traffic information in a road network with at least one intersection on which the traffic flow is controlled by a traffic signal having a control unit for switching on light-emitting signaling devices, which correlates with the traffic flow traffic data and from this traffic information, in particular a Traffic demand for at least a portion of the road network are determined, being detected by the road intersection approaching vehicles a sampling fleet vehicle-specific traffic data by locally limited to the intersection, wireless transmission from the vehicle to the control unit of the traffic signal.
  • a method for determining a current traffic situation for traffic constructions and / or traffic forecasts in a traffic network wherein the current traffic situation is determined for a given area based on a location of mobile phones, wherein the location of the mobile phones performed at different successive times and from the determined Locating the mobile phones, a spatial distribution of the mobile phones at the different successive times is determined and stored.
  • a method for determining the current traffic situation for traffic constructions and / or traffic forecasts in a traffic network wherein the current traffic situation is determined for a given area based on a location of the mobile phone, each mobile phone is uniquely identifiable via an associated identification number, the location the identified mobile phones are performed at different consecutive times and determined and stored from the determined locations of the identified mobile phones at the different successive times.
  • a tracking over time is possible by the evaluation of the movement pattern of each mobile phone can be decided whether a detected mobile phone is moved about in a car belongs to a cyclist or to a pedestrian. Assuming, for example, that in the long term, presumably almost every car driver will carry a mobile phone with him, virtually every vehicle in the road can be compared to his Position are recorded. Subsequently, for example, all the car positions determined in this way can be represented graphically as points in a digital map by means of map matching. Due to a higher traffic density, congestion can easily be identified by the accumulation of such points.
  • the graphics are evaluated either manually or by means of automatic image processing by comparison with previously stored traffic patterns for which the desired traffic situation parameters such as tailback periods have already been determined.
  • the method essentially depends on the fact that the largest possible proportion of road users can be detected, so that, regardless of the immense volume of data that would be generated by a widespread use of the method, a possibly desirable transfer of the described graphical method to a classic FCD system, where the tracking accuracy is typically significantly higher, currently seems not possible.
  • the invention is based on the technical problem of providing a method and a device for determining tailback lengths of traffic signal systems, which can determine backstop lengths over a wide range with little metrological outlay.
  • the method and the device for determining tailback lengths at traffic light installations by means of a data processing device are characterized in that a traffic model for road segments with traffic signal systems is implemented in the data processing device, the traffic model providing at least density profiles as a function of a parameter, wherein position data of Reporting vehicles are supplied in each road segment and an estimation method for the determination the density profile can be carried out, which has the greatest agreement with the determined position data, wherein a reset length is determined at the traffic signals by means of the traffic model taking into account the parameter of the selected density profile.
  • the position data can be highly accurate GPS data, but also, for example, position data that has been determined by means of a mobile telephone. In this case, an identification of the reporting vehicles and / or mobile phones is not mandatory. Furthermore, the method requires only a few position data to deliver useful results.
  • the position data are preferably provided with a time stamp before they are transmitted to the device. It should be noted that the term "traffic model” generally also means a suitable, parameter-dependent, mathematical function.
  • Possible applications of the backlog length data thus determined are e.g. Quality assurance in traffic management, for example, in the context of controlling the effects of changes in the traffic light circuit diagrams or traffic-dependent navigation in urban road networks.
  • Quality assurance in traffic management for example, in the context of controlling the effects of changes in the traffic light circuit diagrams or traffic-dependent navigation in urban road networks.
  • an online traffic management in the sense of an up-to-the-minute, traffic-dependent traffic influencing (traffic signal control, dynamic routing, etc.) is also conceivable.
  • the method according to the invention is naturally also suitable for all other forms of traffic or transport networks with similar framework conditions (edges with periodically controlled outflow) in order to detect congestion of the respective traffic objects at the edge end with respect to their length.
  • the method implicitly incorporates some mechanism for self-correction, so that even the few required parameters compared to other approaches of the model-based traffic situation detection often need only be roughly estimated.
  • the method according to the invention therefore places comparatively small demands on the required model parameters, which on the one hand minimizes the calibration effort and on the other hand additionally makes the method more robust in a certain way.
  • the parameters segment length (road length) and control parameters of the traffic signal system are supplied to the traffic model, so for example the duration of the red and green phases.
  • the traffic model is additionally supplied with a maximum speed of the motor vehicles and / or a correction term as a parameter.
  • the maximum speed may be the legal maximum speed or the empirically determined actually driven maximum speed, the latter being preferred.
  • the density profiles depend on the parameter inflow or inflow probability / traffic demand.
  • the traffic model is designed as a Nagel-Schreckberg model.
  • the advantage of this model is that it is not too complicated and yet provides sufficiently accurate density profiles.
  • the model not only provides mean or maximum values for the respective desired traffic parameters, but even complete, approximate probability distributions for the corresponding parameters, in particular the tailback lengths at LSAs.
  • the estimation method is a maximum likelihood estimation.
  • the method of the maximum likelihood estimation has the advantage that the assignment of the corresponding reference is less dependent on a subjective impression of the observer and can be carried out with the aid of a standard numerical optimization algorithm, which sometimes additionally provides a quality measure for the reliability or unambiguity of the Can specify assignment.
  • Another advantage is that very simple additional data (keyword data fusion) in addition to the position data in the determination of the most appropriate density profile can be considered, which in particular with only a small database of position data still a very good estimate can be achieved (restriction of the parameter space by the additional Dates).
  • individual parameters can also be weighted on the basis of a priori information.
  • the traffic model determines further traffic parameters of the road segment and / or generates parameters for adaptive control of the traffic signal system.
  • the method described above for the determination of tailback lengths at traffic light systems is generally usable for determining tailback periods at temporarily blocked outputs of edges of a transport network.
  • a model is used to obtain density profiles as a function of a parameter, whereby the model can also consist of simple mathematical functions.
  • the objects moving on the transport network then output position data, in which case the density profile which best matches the position data is again determined by means of an estimation method, and the associated parameter or parameters are fed back into the model and from this a backlog length is calculated.
  • a possible application in logistics is the tracking of packages, containers or similar objects, which are partly designed with means for transmitting position data.
  • the transport network can be designed, for example, as a conveyor belt or conveyor belt.
  • the original input variable of the method according to the invention form the position data x i of a sample of relevant traffic objects (floating cars or detector vehicles), which are present as a result of map matching network edge related as distances to any, but fixed reference point of the respective road section.
  • a reference point for example, the segment start or the stop line of the traffic light system can be selected at the end of the segment.
  • the database can also be supplemented to any extent with historical data from a database.
  • position data of the same day of the week and / or the same time of day stored after network edges can be differentiated to the input variables of the method if the FCD coverage is too low.
  • FCD coverage it is completely irrelevant in which way the required position data are / were recorded.
  • suitable pre-processing ie differentiation according to types (pedestrians, cyclists, vehicles, etc.)
  • the position determination of the relevant traffic objects by means of mobile telephones is possible.
  • a traffic model which represents one of the essential components of the method level of the method according to the invention
  • corresponding profiles of local traffic densities can then be derived analytically or by simulation.
  • the determination of the required density profiles K ( q ) takes place within the framework of a mathematical analysis.
  • the density profiles are determined as a function of a certain inflow probability q , which essentially corresponds to the traffic demand.
  • a selection of such profiles for different q shows Fig. 2 , where very well the significantly higher local traffic density in the area of the traffic signal system (right edge of the graph) and the traffic density increasing overall with increasing traffic demand / inflow probability q are recognizable.
  • the required parameters of the Nagel-Schreckberg model which can vary depending on the network edge, are the respective length L of the relevant road section (typically segment start to stop line), the (maximum permissible) speed v max and the traffic light phase g for the (effective ) Green and r for the (effective) red phase. Yellow phases are neglected in the concrete embodiment of the method according to the invention, but can be taken into account as needed.
  • the available floating cars now transmit their respective current position independent of the surrounding traffic situation and without grid reference in the reporting strategy, ie, for example, at regular time intervals or initially store it internally within the framework of a suitable system architecture and later send as a package, but then not only the Vehicles themselves, but also the reported FCD positions distributed according to the current density profile of the associated road section.
  • FCD messages occur on the respective road segment in the area of the traffic light backlog (quantitatively the associated FCD positions Density profile correspondingly) is more likely to occur than on the part with free traffic.
  • Another important advantage of the statistical method used in the method according to the invention is also a special form of self-correction is that regardless of the actual quantitative correctness of the reference density profiles K ( q ) that is always selected for describing the traffic situation, which in certain Best fits the observed FCD positions. In particular, this is the case if the model parameters (especially v max , g and r ) do not correspond exactly to the real conditions. Because of the basically qualitative similarity of Density profiles K ( q ) for different constellations of the model parameters can, however, to a certain extent be determined even if the model is incorrectly calibrated, an approximately correct traffic situation (self-correction), ie in particular the correct tailback length at traffic light installations.
  • the calculation of the desired traffic situation parameters is in fact in complete consistency with the rest of the inventive method by a return of the estimated q * in the traffic model used, from the original, analytical determination of the multiple reference density profiles K ( q ) directly concrete formulas such as the expected, average tailback length L congestion, average ( q *), the expected, maximum stowage length L congestion, max ( q *) and their standard deviations ⁇ ( L congestion, average ( q *)) and ⁇ ( L congestion, max ( q *)) can be derived.
  • the estimated value q * and the associated density profile K ( q *) can be output.
  • a final peculiarity which additionally distinguishes the method according to the invention against other approaches to traffic position detection, is the fact that in addition to all these concrete values, even a complete probability distribution ⁇ ( q *) derived for the model analysis for the number of vehicles in the respective road section at the end of a red phase can be specified, which represents in a special way at the same time, among other things, a distribution for the average and maximum backwater length. Consequently, in addition to the aforementioned traffic parameters, all values (eg variance, higher moments, quantiles) which are contained as distribution properties in ⁇ ( q *) can be determined.
  • the available FCD speed data could additionally be made available via the data fusion interface described above, in that the parameter space ⁇ of the maximum likelihood estimation in the context of the method according to the invention is based on small or large values of the traffic demand / Inflow probability q was limited, with a complete supersaturation of the considered road section was excluded in principle.
  • the significantly better and again completely plausible, minimally smoothed results shows Fig. 9 , while emphasizing that the same database as in Fig. 8 underlying. Overall, it was thus possible to demonstrate in an advantageous manner that good results can still be achieved with the method according to the invention even with a comparatively thin or apparently insufficient database.

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

Claims (16)

  1. Procédé servant à déterminer des longueurs de retenue à hauteur d'installations de signal lumineux au moyen d'un système de traitement de données, comportant les étapes de procédé suivantes consistant à :
    a) établir un modèle de trafic pour des tronçons de route avec des installations de signal lumineux, sachant que le modèle de trafic fournit au moins des profils de densité en fonction d'un paramètre, sachant que les profils de densité représentent des densités de trafic locales sur le tronçon de route concerné ;
    b) déterminer des données de position de véhicules de rapport sur le tronçon de route concerné ;
    c) mettre en oeuvre un procédé d'estimation servant à déterminer le profil de densité, qui présente une très grande concordance avec les données de position déterminées et;
    d) déterminer une longueur de retenue à hauteur des installations de signal lumineux au moyen du modèle de trafic en tenant compte du paramètre de profil de densité sélectionné.
  2. Procédé selon la revendication 1, caractérisé en ce que les paramètres que sont la longueur de tronçon et le paramètre de commande de l'installation de signal lumineux sont amenés au modèle de trafic lors de l'établissement dudit modèle de trafic.
  3. Procédé selon la revendication 2, caractérisé en ce qu'en outre une vitesse maximale et/ou un terme de correction sont amenés en tant que paramètres au modèle de trafic.
  4. Procédé selon l'une quelconque des revendications précédentes, caractérisé en ce que les profils de densité dépendent du paramètre de l'affluence.
  5. Procédé selon l'une quelconque des revendications précédentes, caractérisé en ce que le modèle de trafic est réalisé comme un modèle proposé par Nagel et Schreckenberg.
  6. Procédé selon l'une quelconque des revendications précédentes, caractérisé en ce que le procédé d'estimation est une estimation du maximum de vraisemblance.
  7. Procédé selon l'une quelconque des revendications précédentes, caractérisé en ce qu'outre les données de position, la vitesse des véhicules de rapport et/ou des données de capteurs externes et/ou des évolutions historiques des profils de densité en fonction du jour de la semaine et de l'horaire sont prises en compte en tant qu'informations a priori dans l'estimation servant à déterminer le profil de densité.
  8. Procédé selon l'une quelconque des revendications précédentes, caractérisé en ce que le modèle de trafic permet de déterminer une longueur de retenue maximale attendue et/ou une répartition vraisemblable en résultant pour le nombre de véhicules sur le tronçon de route respectif à la fin de la période rouge et/ou de générer des paramètres pour une commande à ajustage automatique de l'installation de signal lumineux.
  9. Dispositif servant à déterminer des longueurs de retenue à hauteur d'installations de signal lumineux, comportant un système de traitement de données, sachant que dans ce dernier, un modèle de trafic est implémenté pour des tronçons de route avec des installations de signal lumineux, caractérisé en ce que le modèle de trafic fournit au moins des profils de densité en fonction d'un paramètre, sachant que les profils de densité représentent des densités de trafic locales sur le tronçon de route concerné, sachant que des données de position de véhicules de rapport sont amenées par l'intermédiaire d'une interface du système de traitement de données dans le tronçon de route concerné et qu'un procédé d'estimation est réalisé pour déterminer le profil de densité qui présente la plus grande concordance avec les données de position déterminées, sachant qu'une longueur de retenue est déterminée à hauteur des installations de signal lumineux au moyen du modèle de trafic en tenant compte du paramètre du profil de densité sélectionné.
  10. Dispositif selon la revendication 9, caractérisé en ce que les paramètres que sont la longueur de tronçon et le paramètre de commande de l'installation de signal lumineux sont amenés au modèle de trafic.
  11. Dispositif selon la revendication 10, caractérisé en ce qu'en outre une vitesse maximale et/ou un terme de correction sont amenés en tant que paramètres au modèle de trafic.
  12. Dispositif selon l'une quelconque des revendications 9 à 11, caractérisé en ce que les profils de densité dépendent du paramètre de l'affluence.
  13. Dispositif selon l'une quelconque des revendications 9 à 12, caractérisé en ce que le modèle de trafic est réalisé comme un modèle proposé par Nagel et Schreckenberg.
  14. Dispositif selon l'une quelconque des revendications 9 à 13, caractérisé en ce que le procédé d'estimation est une estimation du maximum de vraisemblance.
  15. Dispositif selon l'une quelconque des revendications 9 à 14, caractérisé en ce qu'outre les données de position, la vitesse des véhicules de rapport et/ou des données de capteurs externes et/ou des évolutions historiques des profils de densité en fonction du jour de la semaine et de l'horaire sont prises en compte comme informations a priori dans l'estimation servant à déterminer le profil de densité.
  16. Dispositif selon l'une quelconque des revendications 9 à 15, caractérisé en ce que le modèle de trafic détermine une longueur de retenue maximale attendue et/ou une répartition de vraisemblance en résultant pour le nombre de véhicules sur le tronçon de route concerné à la fin de la période rouge, et/ou en ce que le système de traitement de données génère des paramètres pour une commande à ajustage automatique de l'installation de signal lumineux.
EP20090090002 2008-05-02 2009-02-20 Procédé et dispositif de détermination de longueurs de retenue à hauteur de signaux lumineux Active EP2116981B1 (fr)

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DE102008022349A DE102008022349A1 (de) 2008-05-02 2008-05-02 Verfahren und Vorrichtung zur Ermittlung von Rückstaulängen an Lichtsignalanlagen

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EP2116981A3 (fr) 2009-12-09
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