EP0884708A2 - Procédé et dispositif de pronostic de l'état du trafic - Google Patents

Procédé et dispositif de pronostic de l'état du trafic Download PDF

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Publication number
EP0884708A2
EP0884708A2 EP98250194A EP98250194A EP0884708A2 EP 0884708 A2 EP0884708 A2 EP 0884708A2 EP 98250194 A EP98250194 A EP 98250194A EP 98250194 A EP98250194 A EP 98250194A EP 0884708 A2 EP0884708 A2 EP 0884708A2
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EP
European Patent Office
Prior art keywords
traffic
phase
edge
speed
node
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP98250194A
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German (de)
English (en)
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EP0884708B1 (fr
EP0884708A3 (fr
Inventor
Ulrich Dr. Dipl.-Phys. Fastenrath
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ATX Europe GmbH
Original Assignee
Mannesmann AG
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Filing date
Publication date
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Publication of EP0884708A3 publication Critical patent/EP0884708A3/fr
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Publication of EP0884708B1 publication Critical patent/EP0884708B1/fr
Anticipated expiration legal-status Critical
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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 and a device for forecasting the Traffic condition based on the current detected by detectors Detection data relating to the traffic condition, which are determined by the detectors a set or adjustable reporting behavior transmitted to a control center will
  • the task is accomplished through a procedure according to the independent Process claim 1 and a center according to the independent Device claim 16 solved.
  • the method according to the invention is basically based on the allocation of phases to edges or edge sections of the transport network, with a forecast of the Traffic condition based on the calculation of the movement of these phases in the Traffic network is created.
  • the edges are between two Sections of a road located at intersections or fatigue as Edge sections parts of edges and as nodes intersections, descents and Confluence. The method according to the invention works very efficiently.
  • FCD floating car data
  • FCD floating car data
  • the traffic state forecasting method used by the invention Phases describe the traffic condition in an edge or with discrete values an edge section at a certain time.
  • a phase can be simplest case with the coarsest quantization binary (ie “free”, “jammed”) to be discribed.
  • a phase description with higher resolution, in particular in five stages e.g. "free”, “lively”, “dense”, “tough”, “jammed” is also possible. It is appropriately assumed that each phase has its state (e.g. "free") retained unchanged in time.
  • the forecast of the future traffic condition is the speed of movement of the phase boundaries is calculated. This corresponds to a front phase boundary z.
  • B. a traffic jam start and a rear phase boundary a traffic jam end.
  • phase boundary and the rear phase boundary of a phase i.e. traffic jam start and traffic jam end or vice versa
  • moving speed changes the size of a phase. Phases thus become larger or smaller, in extreme cases up to zero length. So can according to the invention also the change in the sizes of phases for forecasting the future traffic conditions can be used.
  • the current traffic situation is appropriately disjoint, the traffic network completely overlapping phases described to a traffic condition forecast of the entire network. It is preferably in edges or Edge sections, in which, for example, because there are no vehicles in this Edge, there are no current detection data, the phase as "free” or according to the latest detection data from this section assumed to to receive a complete, current description of the traffic condition and one to allow complete traffic state forecasting, since this assumption is the same as that of the is most likely true.
  • phase boundary speeds to be used for the forecast can vary depending on the type of data available can be calculated using the following methods:
  • the calculation of the phase boundary speeds the phases based on the current detection data and the previous detection data e.g. through linear regression. It is possible to have two To determine the location of a phase boundary and the location and time To determine the phase boundary speed.
  • the flows (unit: vehicles per time) before and after the phase boundary can be taken directly from the detection data supplied by a stationary detection device.
  • each Phase boundary based on its current location and its phase boundary speed calculated their location at the time of the forecast and the edge sections between Phase start and phase end at the time of the forecast of the phase state of this Phase assigned.
  • phase boundary velocities resulting from this phase resulting partial phases in the other edges adjacent to this node due to the phase velocity of the phase spreading over the node in front of the node and in each case the river in an edge adjacent to the node in the event of traffic jams and calculated without traffic jam.
  • This enables realistic Traffic jam spread forecast across a node by including rivers in the node with traffic jam and without traffic jam.
  • Phase boundary speed for example the speed of the Locomotion of a traffic jam
  • the position of a The phase boundary at the time of the forecast results from the product of Phase boundary speed and the time between now and that Time of forecast.
  • the edge sections which are spatially between the two predicted phase limits of a phase, the respective phase ("free” or “jammed” etc.) which is a simple and efficient forecast of Phases of edge sections in phase propagation across nodes enables.
  • edge k 1 shows a road 1, on which vehicles A, B, C, D, E, F, G move in the direction of the arrow labeled u vehicle .
  • intersections and junctions of roads are called nodes and parts of roads between two nodes are called edges.
  • the section of a road 1 located between two nodes is designated as edge k 1 in FIG. 1.
  • an edge k 1 can be divided into edge sections a 1 , a 2 , a 3 .
  • the traffic situation shown should forecast the future in Fig. 1 below for the traffic situation shown as an example.
  • the current edge 1 is shown as an example in FIG. 1 above Traffic condition based on detection data detected by detectors in one Central in the form of traffic congestion covering the traffic jam in traffic phases representing an edge and for a future one Time a description of the traffic status of the transport network Calculation of movements and thus of future positions as well as moreover changes in the size of the traffic phases are forecast.
  • Detection data about the current traffic condition can be measured by mobile detectors and / or stationary detectors. In any case, the type of data available depends on location and time. All or some vehicles can be used as mobile detectors, vehicles A, B, C and D, for example in FIG. 1 above, each being equipped with an antenna 2, from which traffic data, in particular the vehicle speed u vehicle that can be detected via the speedometer of a vehicle and also transmit a position, for example via GPS (global processioning system), to a control center (not shown here), for example at regular time intervals, at certain times, on request by the control center and / or in the case of certain events, such as a reduction in the vehicle speed or a change in the speed variance will.
  • traffic data in particular the vehicle speed u vehicle that can be detected via the speedometer of a vehicle and also transmit a position, for example via GPS (global processioning system), to a control center (not shown here), for example at regular time intervals, at certain times, on request by the control center and / or in
  • FCD floating car data
  • the type of data obtained depends on the type of detection selected.
  • mobile detectors in the control data FCD
  • FCD control data
  • Stationary detectors 3 deliver the current flow réelle in a point-related manner, that is to say for a specific point on an edge, and, if they are equipped with speed measuring devices, can also measure the local vehicle speed.
  • sections for edges or Edge sections defined an assigned phase.
  • a phase can be with a binary quantification have the value “free” or “jammed”.
  • Subdivisions can include the values "free”, “lively”, “dense”, “tough”, “jammed” can be assigned. Any other quantification is also possible.
  • the phase can be obtained in one from detection data obtained from mobile detectors 2 Edge or an edge section in which these detectors are located, for example due to the average speed of the detecting vehicles be determined.
  • the speed variance can possibly be used in particular exact phase division can be used. So an edge section, in which the vehicles move at high speed (and possibly lower temporal speed fluctuation) move, assigned the phase "free” and an edge section in which the vehicles with lower Speed (and possibly a relatively high temporal fluctuation in speed) move, the phase "jammed” assigned.
  • state limits which of the phase limits discussed here are different.
  • Fig. 1 results, for example, that in the left edge section a 1 , the vehicle A is moving at a relatively high speed (and possibly low speed fluctuation), so that due to the data sent from the vehicle A via the antenna 2 to a control center, the control center Edge section a 1 the phase "free" can be assigned.
  • the edge section a 2 it can be assumed that the vehicles B, C, D, E and F move at lower speeds (and higher speed fluctuations). Accordingly, it can be expected that the vehicles B, C, D equipped with an antenna 2 report a relatively low speed (and possibly high speed fluctuations) to a control center. The center would accordingly assign the phase section " 2 " to the edge section.
  • the vehicle G Only the vehicle G is located in the edge section 3, but not with it an antenna 2, so no detection data to one here shown control center sent. So until later detection by a stationary detection device 3 is not detected. That is why the head office takes e.g. the Case, namely that there are only a few vehicles or none in the edge section a3 The vehicle is located and assigns the phase state "free" to this edge section a3. to. Alternatively, it is possible to phase an edge section or an edge assign based on the last measured detection data.
  • the age of the last measurement in one can certain section of the route can be a criterion, there the status "free" to assume.
  • data is only available from a small part of the vehicles to disposal.
  • the forecast is the expected movement of the phase boundaries of each phase used within the transport network to determine the future location of the phase boundaries and thus the position and here also the size of the phases to a future one To determine the point in time.
  • a phase has two phase boundaries, namely the top left of the vehicle in FIG. 1 B located front phase boundary 4 and the top right of the vehicle F in FIG. 1 rear phase boundary 5.
  • vehicle G drives onto car column B, C, E, F on, so that phase 2 extends a little to the rear, even though it is in Direction of travel of the vehicles.
  • the speed v is the phase boundaries of the speeds u Distinguish vehicles.
  • FIG. 2 shows a further example of the current traffic situation in FIG. 2 above and in FIG. 2 below the future traffic situation that results from this.
  • the front phase boundary 4 has moved to the left at the speed v front phase boundary and the rear phase boundary, that is to say the end of the traffic jam, has moved to the right at the speed V rear phase boundary .
  • the forecast phase is defined by calculating the two forecast phase limits.
  • v front phase boundary and v rear phase boundary can be calculated, for example by linear regression, from the positions of the phase boundaries at different times.
  • the phase boundaries are as above executed, a prognosis of the phase movement along an edge is possible.
  • FIG. 3 shows an example of a hatched jam in the upper edge k 1 , which has the front phase boundary 4 and the rear phase boundary 5 (indicated by thick arrows in the edges).
  • the vehicles move in the traffic jam in the direction of the thick arrow in the shaded area at the speed u vehicle .
  • the rear stowage limit 5 moves because, for example, more vehicles can drive onto the stowage limit from behind ( ⁇ ⁇ towards in FIG. 4) than from the stowage limit 5 towards the front ( ⁇ ⁇ down in FIG. 4), counter to the direction of the thick one Arrow, ie in the direction of the narrow arrow indicated next to the upper edge 1 at the speed v rear phase boundary towards the intersection.
  • ⁇ k i.e. the flow in an edge k (k 3 or k 4 ) before reaching the jam
  • ⁇ k the flow in an edge k (k 3 or k 4 ) before reaching the jam
  • FCD the flow in an edge k (k 3 or k 4 ) before reaching the jam
  • the residual flow ⁇ s 1 transported in the traffic jam can be determined using the following relationship obtained from a formula above by shaping.
  • ⁇ 1 s ⁇ 1 * u 1 s / u 1 * (u 1 - v 1 ) / (u s 1 - v 1 ).
  • 2 can be smaller than ⁇ 2 without a jam and in particular on ⁇
  • 2nd ⁇ 2nd * ⁇ 1 s / ⁇ 1 sink when the congestion front passes the knot. It can also be greater than this value due to congestion avoidance.
  • the local network topology and possible diversions can be taken into account.
  • u k s min (u 1 s ; ⁇ u k * ( ⁇ ⁇ from ) / ( ⁇ ⁇ to )), where ⁇ (0 ⁇ ⁇ 1) can be determined phenomenologically.
  • the speeds and, for example, positions of the vehicles that can be measured with GPS are transmitted from the vehicles to the control center.
  • the fluxes ⁇ are measured by the stationary detectors at the detector and transmitted to a control center.
  • the current common traffic condition is determined in the next common step 9 by the phases covering the network, a phase in an edge or an edge section being calculated in each case on the basis of the average vehicle speeds in this edge.
  • the variance of the vehicle speeds u may also be included. High speed (and possibly additionally low variance) in speed can be interpreted as a "free" phase, for example.
  • the calculation of the propagation speed v s of a front or rear phase boundary within an edge depends on the type of detection data available.
  • the propagation velocity v s of a phase boundary within an edge can be derived from the rivers in this edge in front of and behind the phase boundary, for example in front of and behind a traffic jam end and the vehicle speed in front of and behind the phase boundary can also be determined, for example, in front of and behind a traffic jam end.
  • the new phase limits (position phase limit ) can be determined from the phase limit speed (v phase limit in this edge) and the time difference between the forecast time and the current time. With the forecast of the phase boundaries, the forecast of the phases is possible within the edges.
  • phase boundary forecast location beyond a node i.e. one Phase spread across an account gives:
  • step 15 is to to return to start 6. This is a continuously updated traffic forecast possible.
  • the method described above is a possible embodiment. Further This forecasting process can be optimized.
  • the Measurement method not only FCD or FCD / stationary, but also a complex one location / time-dependent mixture of these. It may also be advantageous to knot to be classified according to turn rates.
  • a phenomenological model can also be used be included.
  • the dynamics of phase boundaries can be macroscopic Model or be determined phenomenologically.
  • the forecast can ongoing or on service request, i.e. user request as part of a Traffic telematics service.
  • Fig. 5 shows a block diagram of a "floating car” (floating in traffic Vehicle) with the reference symbol A, which is via a wireless communication interface 20 is connected to a central Z.
  • the wireless Communication interface 20 can be, for example, mobile radio.
  • the speed of the vehicle is measured with a sensor system 21 and the position of the vehicle (e.g. using GPS or in another way) certainly. It is possible that this data can be pre-processed using e.g. by Remote configuration created preset and virtual environment by comparison and evaluation.
  • the raw or edited data u (and possibly the position) are via a communication interface 22, i.e. a Sender, sent to the receiver 23 of the control center.
  • a computer 24 identifies traffic phases on the basis of this data (25) and the Movement of the phase boundaries predicted (26).
  • phase forecasts obtained can be used for a traffic jam prognosis and / or for a forecast of Travel times (27) can be used.

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  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Devices For Checking Fares Or Tickets At Control Points (AREA)
  • Mobile Radio Communication Systems (AREA)
EP98250194A 1997-06-12 1998-06-04 Procédé et dispositif de pronostic de l'état du trafic Expired - Lifetime EP0884708B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19725556 1997-06-12
DE19725556A DE19725556A1 (de) 1997-06-12 1997-06-12 Verfahren und Vorrichtung zur Verkehrszustandsprognose

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EP0884708A2 true EP0884708A2 (fr) 1998-12-16
EP0884708A3 EP0884708A3 (fr) 2000-03-15
EP0884708B1 EP0884708B1 (fr) 2004-08-25

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1026649A2 (fr) * 1999-02-06 2000-08-09 DaimlerChrysler AG Procédé et dispositif de préparation d'information de circulation
NL1011501C2 (nl) * 1999-03-09 2000-09-12 Wiebren De Jonge Het Traffic Information & Pricing (TIP) systeem.
EP1061491A1 (fr) * 1999-06-11 2000-12-20 DDG Gesellschaft für Verkehrsdaten mbH Procédé de filtrage de données du trafic pour déterminer la vitesse sur les routes
WO2001020574A1 (fr) * 1999-09-14 2001-03-22 Daimlerchrysler Ag Procede de surveillance de l'etat du trafic sur un reseau routier comportant des modifications effectives de trafic
WO2001055993A1 (fr) * 2000-01-27 2001-08-02 Trafficmaster Developments Limited Appareil et procede de controle de la circulation routiere
EP1154389A1 (fr) * 2000-05-10 2001-11-14 DaimlerChrysler AG Procédé de détermination de l'état du trafic sur un réseau routier
EP1176569A2 (fr) * 2000-07-28 2002-01-30 DaimlerChrysler AG Procédé de surveillance de l'état du trafic sur un réseau routier comportant des modifications effectives du trafic
EP1235195A2 (fr) * 2001-02-23 2002-08-28 Hitachi, Ltd. Procédé de pronostic des conditions de circulation au moyen de véhicules flottants
EP1326222A1 (fr) * 2002-01-03 2003-07-09 DDG Gesellschaft für Verkehrsdaten mbH Procédé pour la estimation consistante en soi même de la durée du trajet prédictive à des fins avec détecteurs mobiles ou stationnaires pour mesurer des durées du trajet allées
WO2004059592A1 (fr) * 2002-12-20 2004-07-15 Daimlerchrysler Ag Procede et systeme pour reconnaitre des perturbations prevues dans le temps, comprenant une centrale, par detection de flancs de brouillage au moyen de prevision de temps de trajet decoupee en sections
CN100463009C (zh) * 2006-12-25 2009-02-18 北京世纪高通科技有限公司 一种交通信息融合处理方法和系统
DE19917154B4 (de) * 1999-04-16 2013-09-05 Deutsche Telekom Ag Verfahren zum Erkennen von Stausituationen auf Straßen und Fahrzeuggeräte mit einer Einheit zur Durchführung des Verfahrens

Families Citing this family (8)

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DE19835979B4 (de) * 1998-08-08 2005-01-05 Daimlerchrysler Ag Verfahren zur Verkehrszustandsüberwachung und Fahrzeugzuflußsteuerung in einem Straßenverkehrsnetz
DE19944077C2 (de) * 1999-09-14 2002-02-07 Daimler Chrysler Ag Verfahren und Vorrichtung zur Verkehrszustandsüberwachung
DE19954971B4 (de) * 1999-11-16 2004-08-12 Daimlerchrysler Ag System zur Beeinflussung des Verkehrsflusses von Fahrzeugen
DE10018562C1 (de) * 2000-04-14 2002-02-07 Daimler Chrysler Ag Verfahren zur Gewinnung von Verkehrsdaten für ein Verkehrsnetz mit verkehrsgeregelten Netzknoten durch Meldefahrzeuge
DE10062856B4 (de) * 2000-12-16 2008-01-10 Daimlerchrysler Ag Verfahren zur fahrzeugindividuellen Verkehrsprognose
JP4982143B2 (ja) 2006-09-27 2012-07-25 クラリオン株式会社 交通状況予測装置
WO2009030524A1 (fr) 2007-08-29 2009-03-12 Continental Teves Ag & Co. Ohg Actualisation personnalisée de cartes de navigation numériques
DE102018210020A1 (de) 2018-06-20 2019-12-24 Robert Bosch Gmbh Verfahren zur Steuerung eines Verkehrsflusses

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EP0715285A1 (fr) * 1994-11-28 1996-06-05 MANNESMANN Aktiengesellschaft Méthode pour réduire la quantité de données qui doit être transmise par véhicules d'une flotte de véhicules
EP0740280A2 (fr) * 1995-04-28 1996-10-30 INFORM Institut für Operations Research und Management GmbH Méthode de détection des pertubations pour trafic routièr
DE19647127A1 (de) * 1996-11-14 1998-05-28 Daimler Benz Ag Verfahren zur automatischen Verkehrsüberwachung mit Staudynamikanalyse

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EP0715285A1 (fr) * 1994-11-28 1996-06-05 MANNESMANN Aktiengesellschaft Méthode pour réduire la quantité de données qui doit être transmise par véhicules d'une flotte de véhicules
EP0740280A2 (fr) * 1995-04-28 1996-10-30 INFORM Institut für Operations Research und Management GmbH Méthode de détection des pertubations pour trafic routièr
DE19647127A1 (de) * 1996-11-14 1998-05-28 Daimler Benz Ag Verfahren zur automatischen Verkehrsüberwachung mit Staudynamikanalyse

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SADAO TAKABA ET AL: "ESTIMATION AND MEASUREMENT OF TRAVEL TIME BY VEHICLE DETECTORS AND LICENSE PLATE READERS" PROCEEDINGS OF THE VEHICLE NAVIGATION AND INFORMATION SYSTEMS CONFERENCE,US,NEW YORK, IEEE, Bd. -, Seite 257-267 XP000348211 ISBN: 0-7803-0488-8 *

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1026649A3 (fr) * 1999-02-06 2000-09-27 DaimlerChrysler AG Procédé et dispositif de préparation d'information de circulation
US6240364B1 (en) 1999-02-06 2001-05-29 Daimlerchrysler Ag Method and device for providing traffic information
EP1026649A2 (fr) * 1999-02-06 2000-08-09 DaimlerChrysler AG Procédé et dispositif de préparation d'information de circulation
AU763951B2 (en) * 1999-03-09 2003-08-07 Wiebren De Jonge The traffic information and pricing (TIP) system
NL1011501C2 (nl) * 1999-03-09 2000-09-12 Wiebren De Jonge Het Traffic Information & Pricing (TIP) systeem.
WO2000054240A1 (fr) * 1999-03-09 2000-09-14 Wiebren De Jonge Systeme de peage et de renseignements relatifs a la circulation
DE19917154B4 (de) * 1999-04-16 2013-09-05 Deutsche Telekom Ag Verfahren zum Erkennen von Stausituationen auf Straßen und Fahrzeuggeräte mit einer Einheit zur Durchführung des Verfahrens
EP1061491A1 (fr) * 1999-06-11 2000-12-20 DDG Gesellschaft für Verkehrsdaten mbH Procédé de filtrage de données du trafic pour déterminer la vitesse sur les routes
WO2001020574A1 (fr) * 1999-09-14 2001-03-22 Daimlerchrysler Ag Procede de surveillance de l'etat du trafic sur un reseau routier comportant des modifications effectives de trafic
US6813555B1 (en) 1999-09-14 2004-11-02 Daimlerchrysler Ag Method for monitoring the condition of traffic for a traffic network comprising effective narrow points
WO2001055993A1 (fr) * 2000-01-27 2001-08-02 Trafficmaster Developments Limited Appareil et procede de controle de la circulation routiere
EP1154389A1 (fr) * 2000-05-10 2001-11-14 DaimlerChrysler AG Procédé de détermination de l'état du trafic sur un réseau routier
EP1176569A3 (fr) * 2000-07-28 2003-05-14 DaimlerChrysler AG Procédé de surveillance de l'état du trafic sur un réseau routier comportant des modifications effectives du trafic
EP1176569A2 (fr) * 2000-07-28 2002-01-30 DaimlerChrysler AG Procédé de surveillance de l'état du trafic sur un réseau routier comportant des modifications effectives du trafic
EP1235195A2 (fr) * 2001-02-23 2002-08-28 Hitachi, Ltd. Procédé de pronostic des conditions de circulation au moyen de véhicules flottants
EP1235195A3 (fr) * 2001-02-23 2005-02-09 Hitachi, Ltd. Procédé de pronostic des conditions de circulation au moyen de véhicules flottants
EP1326222A1 (fr) * 2002-01-03 2003-07-09 DDG Gesellschaft für Verkehrsdaten mbH Procédé pour la estimation consistante en soi même de la durée du trajet prédictive à des fins avec détecteurs mobiles ou stationnaires pour mesurer des durées du trajet allées
WO2004059592A1 (fr) * 2002-12-20 2004-07-15 Daimlerchrysler Ag Procede et systeme pour reconnaitre des perturbations prevues dans le temps, comprenant une centrale, par detection de flancs de brouillage au moyen de prevision de temps de trajet decoupee en sections
CN100463009C (zh) * 2006-12-25 2009-02-18 北京世纪高通科技有限公司 一种交通信息融合处理方法和系统

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DE59811857D1 (de) 2004-09-30
DE19725556A1 (de) 1998-12-24
ATE274736T1 (de) 2004-09-15
EP0884708B1 (fr) 2004-08-25
EP0884708A3 (fr) 2000-03-15

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