EP0771447A1 - Detection et prevision des perturbations de la circulation routiere - Google Patents

Detection et prevision des perturbations de la circulation routiere

Info

Publication number
EP0771447A1
EP0771447A1 EP96914510A EP96914510A EP0771447A1 EP 0771447 A1 EP0771447 A1 EP 0771447A1 EP 96914510 A EP96914510 A EP 96914510A EP 96914510 A EP96914510 A EP 96914510A EP 0771447 A1 EP0771447 A1 EP 0771447A1
Authority
EP
European Patent Office
Prior art keywords
traffic
queue
flow
predicted
values
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
EP96914510A
Other languages
German (de)
English (en)
Other versions
EP0771447B1 (fr
Inventor
Kjell Olsson
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.)
Dinbis AB
Original Assignee
Dinbis AB
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Dinbis AB filed Critical Dinbis AB
Publication of EP0771447A1 publication Critical patent/EP0771447A1/fr
Application granted granted Critical
Publication of EP0771447B1 publication Critical patent/EP0771447B1/fr
Anticipated expiration legal-status Critical
Expired - Lifetime legal-status Critical Current

Links

Classifications

    • 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

Definitions

  • the present invention relates to a method for detection and prediction of disturbances in the road traffic, e g the forming of traffic queues depending on overloading the road-net or incidents.
  • traffic management systems an important task is to avoid overloading, where traffic breakdown is introducing queues with reduced passability, increased rise for accidents and increased environmental problems. Incidents should be detected early to be able to reduce the damages.
  • the object is to get wounded people to hospitals, to reduce the secondary related accidents and to manage the traffic in such a way that no unnecessary blockings arise, but that the road-net will be efficiently utilized.
  • a background for basic technologies is given in the Swedish patent 9203474-3.
  • the present invention presupposes the existence of knowledge of that technology.
  • the algorithms have been formed by "trial and error", i e one has tested and changed untill one has no longer got a lot of false alarms, at the same time as one has not missed detection of many real incidents.
  • the traffic at sensor B might vary much. If e g during one period, there is not a single car passing, although there were many cars passing during the period before, that might indicate that an incident has occurred, which prevents traffic to pass. But it can also be a natural gap in the traffic. If one by measuring traffic upstream, finds that there is a gap in the traffic, which will be measured later on at B, that can be predicted for B, - and then the measurement of 0 cars passing at B will not be a sign of an incident between A and B, but a confirmation that the traffic is as expected. This method increases the freedom to measure during short time periods, and since one predicts, one is not losing time.
  • the understanding of the traffic processes are utilized, and that in a direct way.
  • An important part is the understanding of large traffic variations. Those can be regarded as results of stochastic processes, and if one measures e g flow at a sensor, then one experiences those as noise. Using short measurement periods, one obtaines relatively large variations around a given average. By utilizing knowledge about "noise”, one can understand how to make use of the information in those "noise-variations", and not only regard those as something that destroy the possibilities to perform simple detections of incidents. By measurements one can get means and standard deviations and from theory and measurements one can create approximative distributive functions, i e one knows statistically rather much about the traffic variations. E g assuming a normal distribution, a measured standard deviation can give information about the probability for a variation being larger than a given value. One also obtains an understanding about what one does not know and a measure of the uncertainty.
  • the knowledge is utilized about probability for deviations of a certain order to set thresholds, which by that give the desired false alarm rate. It might also be that a deviation that originates from an incident is not large enough to exceed the threshold. Then one can wait untill the next measured deviation is received and examine if those two values together are that large that the probability requirement now is fulfilled, i e that one is now exceeding the corresponding threshold. This can be repeated successively, and if the natural variations are very large, the threshold will be large, and there might be required more incident- caused deviations for them to exceed their threshold. However time is running, the more measurement periods there are needed, and the incident detection should be fast to prevent serious secondary effects. In the invention e g the threshold can be automatically set in that way that a minimum of extra measurement periods need to be used.
  • a third drawback with the traditional methodology is, that it is difficult to transfer from one situation, where it finally with trial and error, has been adapted to operation, to another situation. It might mean geographically, positions, eg transfer to another road section, where access-roads, intersections or number of lanes offer other traffic situations. It might mean changes of measuring time periods or other parameters. This effort can be very time-wasting and resource-consuming. One needs to observe the traffic in parallall with other accurate measuring means to get a key answer to compare with, giving possibility to change parameters in the algorithm towards better agreement with the reality. Also changes in the traffic situation might result in working through the process again, to get new better adapted parameters to put into the algorithm.
  • the starting values can be well chosen from the origin.
  • the topical deviations are measured, and the corresponding statistical measures are obtained, e g the standard deviation of the traffic deviations. Based on those measures the respective threshold values can be set automatically, and the method starts to generate incident detections, which the operator can observe are true or false. Since the method continuously measures the deviations, the statistical parameters can be successively updated and adapted to changes in the traffic situations.
  • a key-function is prediction of traffic breakdown and queue-forming.
  • prediction a time- margin is obtained before the predicted problem really is happening. That time-margin can be used to implement actions, which prevent that the problem arise in the real world.
  • time-margin can be used to implement actions, which prevent that the problem arise in the real world.
  • the detection process of queue-forming it is interesting to utilize prediction.
  • E g if free-flow is predicted and a queue anyhow forms, then the sensors offer values, showing the real traffic situation (queue). The deviations between the predicted free-flow values and the measured values can therefore be used as an indication on the forming of a queue. In this desciption of the invention, sometimes other words are used than "prediction", e g the word "expected”.
  • the notation of "corresponding value” often implies an association of a time direction of changed knowledge of the parameter, also if the value just have been obtained from historical values.
  • the notation "predict” is used including also estimations, that is not direct predictions, but is fulfilling a corresponding object.
  • the comparison value might be a mean- value or a mean-value plus a value based on a standard deviation, historically estimated value etc.
  • this value constitutes a type of expected comparison value, by which the measured value can reach criteria for detection of a queue.
  • the expected value has got a forward- associated function towards the measured value, and might be estimated in an equivalent process of a prediction, also when the expected value is estimated afterwards, i e after that the the measured value has been obtained.
  • a queue-detection according to the invention can also be performed when queues are formed on links between sensors. This is also valid for the use of video-sensors, IR-sensors , radar and similar sensors, which e g with an image can cover a longer road distance than those few meters that traditional loop-sensors cover. However, in practice the video-sensor range is much shorter than the distance one "can see". The limitations in height-positions of the cameras implies e g that a bus can hide a long row of cars. Video-sensors, positioned at 0,5 to 1 km interval, therefore might only have a guaranteed coverage of their respective close area, and the larger part of the distance in between, has to be treated in the corresponding way as with loop-sensors.
  • Detections can be performed at downstream as well as upstream sensor.
  • the queue is detected by the fact that the queue is within the direct measuring area of the sensor.
  • Characteristics of a queue is that traffic is dense and the speed is lower than at the free-flow mode. It is known, when the flow is approaching the capacity limit of the road, that the velocity is decreasing, e g at an access-road, where the speed limit at the motorway is 70 km/h, the motorway speed might drop to 55 km/h, because of the increased traffic density. At further increase of traffic density, the traffic breaks down to a queue, which might got still lower speeds. According to the invention, the later traffic state might be surveyed by measurements for at least two measuring periods.
  • Queues and queue-forming also get different process courses on ordinary roads with one lane, compared to two lanes and compared to motorways. Those queues that are most interesting for this patent, are such that are appearing on motorways and similar arterial roads for larger cities.
  • the essential queues are those creating large problems. Therefore small groups of cars driving close, are considerred as dense traffic. Also longer packets of cars are here considerred as dense traffic, when driving in somewhat reduced velocities compared with the free-flow velocity ( often the given speed-limit on signs ). Usually those car-packets are characterized in that the front of the packet is moving forward along the road ( "moving queue"). At velocities above the break-point, the traffic in such a packet is characterized by high flow and reasonable high velocity, why a calm (homogeneous) driving in such a packet might not constitute a direct traffic problem.
  • the traffic is instead successively predicted, and when the probability of collapse is above a certain given value, then the corresponding speed-limits are reduced on the signs.
  • time-margins for avoiding the traffic collapse, and the action influence on the traffic might be kept at a lower level.
  • the method is the same as that used for queue- and incident detection.
  • the present invention can also be used for control of on-flow traffic, e g for control of "ramp- metering".
  • on-flow traffic e g for control of "ramp- metering".
  • On-flow traffic e g for control of "ramp- metering".
  • On-flow traffic e g for control of "ramp- metering".
  • On-flow traffic e g for control of "ramp- metering
  • the prediction of traffic collapse at an on-ramp can be based on measurements at upstream sensors e g a sensor at the main road and a sensor at the access-road. Measurements of traffic by respective sensor can be used to predict the traffic a certain time-interval later on, equal to the travel time to the weaving area at the connection. By matching or synchronizing of measurements can e g occasions be predicted, when coinciding traffic peaks reach the access connection. The predicted flows are compared with the threshold values to obtain the prediction of overloading.
  • One way to estimate the threshold value for the main road is illustrated as follows.
  • the weaving capacity Cv Co - a * I-, where Co is a constant and k is the flow on the access road.
  • the factor a shows that the capacity on the main road is not determined by a simple sum of the two flows. Both Co and a should be calibrated for the present access road.
  • Those present algorithms have been shown good agreement down to small on-flow values. When traffic has broken down, other conditions are valid.
  • the queue-growth is determined by the difference between the flows behind and in front of the queue.
  • the flow in front of the queue might be estimated when needed, from a model for queue off-flow at the front of the queue.
  • the off-flow at the queue-front and the flow downstream the queue can be determined, and with information on the flow and the related velocity downstream the queue, also the growth and decay of the queue can be determined.
  • the queue off-flow algorithm is valid for many usual situations, and the gap g can be obtained typically from relations between gap, flow and velocity at queue-states.
  • the most interesting is not always to judge, if it would be the most probable outcome that the event occurs, i e if that probability is above 50%. If the rise for queue-forming is 30 % or the rise for an accident is 10 %, then that might be enough for actions to be taken to prevent the event from occurring, i e in spite of the largest probability being neither a queue nor an accident. Below, examples are given for the way to work with the probability determination according to the invention.
  • a typical distribution function within statistics is the Normal or Gaussian distribution. Assuming that one as approximately valid for the traffic on a certain part of the road-net, then the function can be calibrated from measurements and estimations of the variance of traffic around the average value. The probability for obtaining a certain value can be calculated or usually fetched from tables. Depending on the detection process, there might be a need for modifications of the distributions, or adaptions with the use of other distribution functions.
  • the Rayleigh-distribution e g is interesting at envelope detection and filtered noise deviations.
  • the number of measurement periods thus needs to be above 9,2/4, i e larger than 3. If the distribution instead had been simply linear, i e exp(-x/ ⁇ ), then there had been needed more than 20 periods.
  • Route guidance might e g be performed by the use of "VMS", variable message signs.
  • the message might e g contain information about different grades of problems on the given route. The larger the problem the larger the number of drivers that will consider choosing an alternative route.
  • That measure is also used for updating the value of strongness of the presently shown message, whereby the system successively stores an updated measure of the strongness for the respective messages.
  • the system beforehand can choose a message matching that share of the drivers, which is desireable for choosing a new route.
  • Calibration and updating is performed by successively measuring the consequences of the actions, and then matching the stored value of strongness for a message to the actions. In this process a slower rate of updating is preferrably chosen, in a way that deviations are only partially changing the former value.
  • the innovation is also suitable for management of "park and ride", e g parking the car and taking the train or bus, - where the control information partly is based on predicted problems at the road net-work.
  • Another area of use is the control of departure, e g information about traffic problems might influence some drivers to choose another transportation means or to delay the travel.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)

Abstract

Procédé permettant de détecter et de prévoir des incidents et des bouchons créés par une circulation trop dense. La détection se fait en temps réel à l'aide de détecteurs situés sur un réseau routier. Les prévisions sont également utilisées pour obtenir une détection plus rapide et plus fiable. Des mesures par détecteurs sont également utilisées dans ledit procédé, des comparaisons avec des valeurs attendues étant utilisées pour mettre à jour successivement des valeurs de paramètres stockées pour les algorithmes concernés. De cette manière, le système peut s'adapter avec succès aux changements de situations. Les fortes variations de la circulation qui ont lieu naturellement à des intervalles courts sont traitées à l'aide de procédés basés sur le bruit. On peut obtenir ainsi des mesures associées à la répartition comme par exemple l'écart standard qui peut être estimé à partir de mesures, et une base d'estimation de probabilités d'écarts d'un certain ordre, par exemple liés à l'écart standard. La détection automatique d'incidents (AID) est basée sur la détermination du taux désiré de fausses alarmes et sur le niveau de seuil associé. Le présent procédé comporte des mesures accumulées. Le procédé de prévision de la présente invention permet ainsi une détection plus rapide et plus fiable des incidents.
EP96914510A 1995-05-19 1996-05-13 Detection et prevision des perturbations de la circulation routiere Expired - Lifetime EP0771447B1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
SE9501919 1995-05-19
SE9501919A SE9501919L (sv) 1995-05-19 1995-05-19 Detektering och prediktion av trafikstörningar
PCT/SE1996/000620 WO1996036929A1 (fr) 1995-05-19 1996-05-13 Detection et prevision des perturbations de la circulation routiere

Publications (2)

Publication Number Publication Date
EP0771447A1 true EP0771447A1 (fr) 1997-05-07
EP0771447B1 EP0771447B1 (fr) 2004-02-25

Family

ID=20398415

Family Applications (1)

Application Number Title Priority Date Filing Date
EP96914510A Expired - Lifetime EP0771447B1 (fr) 1995-05-19 1996-05-13 Detection et prevision des perturbations de la circulation routiere

Country Status (4)

Country Link
EP (1) EP0771447B1 (fr)
DE (1) DE69631629T2 (fr)
SE (1) SE9501919L (fr)
WO (1) WO1996036929A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2562170A1 (fr) 2006-02-21 2013-02-27 Toyama Chemical Co., Ltd. Procédé de production de 3-[5-[4-(cyclopentyloxy)-2-hydroxybenzoyl]-2-[(3-oxo-2-substitué-2,3-dihydro-1,2-benzisoxazol-6-yl)méthoxy]phényl]propionate ester et intermédiaire pour ce procédé

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
SE509762C2 (sv) * 1996-08-09 1999-03-08 Dinbis Ab Metod och anordning för motorvägskontroll
SE510430C2 (sv) * 1998-01-30 1999-05-25 Dinbis Ab Metod och anordning för nätverksstyrning av trafik
DE19805869A1 (de) * 1998-02-13 1999-08-26 Daimler Chrysler Ag Verfahren und Vorrichtung zur Bestimmung der Verkehrslage auf einem Verkehrswegennetz
US7221287B2 (en) 2002-03-05 2007-05-22 Triangle Software Llc Three-dimensional traffic report
WO2005013063A2 (fr) 2003-07-25 2005-02-10 Landsonar, Inc. Systeme et procede pour determiner une heure de depart recommandee
DE102006033532A1 (de) * 2006-07-20 2008-01-24 Deutsche Telekom Ag Verfahren und Vorrichtung zur Generierung von Frühwarnungen vor Verkehrszusammenbrüchen an Engstellen
US8982116B2 (en) 2009-03-04 2015-03-17 Pelmorex Canada Inc. Touch screen based interaction with traffic data
US8619072B2 (en) 2009-03-04 2013-12-31 Triangle Software Llc Controlling a three-dimensional virtual broadcast presentation
US9046924B2 (en) 2009-03-04 2015-06-02 Pelmorex Canada Inc. Gesture based interaction with traffic data
WO2012159083A2 (fr) 2011-05-18 2012-11-22 Triangle Software Llc Système permettant d'utiliser des données de trafic et des données d'efficacité de conduite
US8781718B2 (en) 2012-01-27 2014-07-15 Pelmorex Canada Inc. Estimating time travel distributions on signalized arterials
US10223909B2 (en) 2012-10-18 2019-03-05 Uber Technologies, Inc. Estimating time travel distributions on signalized arterials
US9240123B2 (en) 2013-12-13 2016-01-19 Here Global B.V. Systems and methods for detecting road congestion and incidents in real time
US9336448B2 (en) 2014-08-11 2016-05-10 Here Global B.V. Variable speed sign value prediction and confidence modeling
US10109184B2 (en) 2014-10-08 2018-10-23 Here Global B.V. Probe based variable speed sign value
US11378403B2 (en) 2019-07-26 2022-07-05 Honeywell International Inc. Apparatus and method for terrain aided navigation using inertial position

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CH665497A5 (en) * 1981-11-12 1988-05-13 Alex Frauchiger Resolving and preventing traffic queues - by indicating modified speeds to vehicles based on waiting times and distances
SE470367B (sv) * 1992-11-19 1994-01-31 Kjell Olsson Sätt att prediktera trafikparametrar

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See references of WO9636929A1 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2562170A1 (fr) 2006-02-21 2013-02-27 Toyama Chemical Co., Ltd. Procédé de production de 3-[5-[4-(cyclopentyloxy)-2-hydroxybenzoyl]-2-[(3-oxo-2-substitué-2,3-dihydro-1,2-benzisoxazol-6-yl)méthoxy]phényl]propionate ester et intermédiaire pour ce procédé

Also Published As

Publication number Publication date
DE69631629T2 (de) 2004-12-23
SE503515C2 (sv) 1996-07-01
DE69631629D1 (de) 2004-04-01
WO1996036929A1 (fr) 1996-11-21
EP0771447B1 (fr) 2004-02-25
SE9501919D0 (sv) 1995-05-19
SE9501919L (sv) 1996-07-01

Similar Documents

Publication Publication Date Title
EP0771447B1 (fr) Detection et prevision des perturbations de la circulation routiere
EP0670066B1 (fr) Procede de prevision de parametres de circulation
Wu et al. Identification of oversaturated intersections using high-resolution traffic signal data
US5696502A (en) Method of sensing traffic and detecting traffic situations on roads, preferably freeways
Liu et al. A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials
CN102289937B (zh) 基于停车线检测器的城市地面道路交通状态自动判别方法
EP0825578B1 (fr) Système pour surveiller des changements dans l'écoulement du trafic
Versavel Road safety through video detection
Hawas et al. Optimized multistage fuzzy-based model for incident detection and management on urban streets
JP3007019B2 (ja) 交通流計測装置
JP2003296870A (ja) 交通流予測と事例推論を利用した交通渋滞の防止・解消方法
Wang et al. Prediction of red-light running on basis of inductive-loop detectors for dynamic all-red extension
JP3876653B2 (ja) 交通流の異常検知装置及び方法
Ahmed et al. Discrete dynamic models for freeway incident detection systems
Skabardonis et al. Operational analyses of freeway off-ramp bottlenecks
Corby et al. Analysis of freeway accident detection
JP2893544B2 (ja) 異常交通流の検出装置
Bagheri et al. Real-time estimation of saturation flow rates for dynamic traffic signal control using connected-vehicle data
JP4030354B2 (ja) 突発事象検出装置
Luk et al. Characterization of incidents on an urban arterial road
Dudek et al. Detecting freeway incidents under low-volume conditions
JPH08106594A (ja) 交通渋滞判定装置
JP2001101570A (ja) 所要時間算出方法及び所要時間算出装置
Wei et al. Improving freeway operation with ramp metering control using connected vehicles as “floating sensors”
KR20230063080A (ko) 교통량 증대를 위한 신호교차로 노면조건 및 교통패턴 분석 결과에 따른 신호등 운영 시스템

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): DE DK FR GB IT NL

17P Request for examination filed

Effective date: 19970314

17Q First examination report despatched

Effective date: 19980529

GRAH Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOS IGRA

RIC1 Information provided on ipc code assigned before grant

Free format text: 7G 06F 19/00 A, 7G 08G 1/01 B, 7G 08G 1/065 B

RIC1 Information provided on ipc code assigned before grant

Free format text: 7G 06F 19/00 A, 7G 08G 1/01 B, 7G 08G 1/065 B

GRAH Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOS IGRA

GRAA (expected) grant

Free format text: ORIGINAL CODE: 0009210

AK Designated contracting states

Kind code of ref document: B1

Designated state(s): DE DK FR GB IT NL

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: NL

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20040225

Ref country code: IT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT;WARNING: LAPSES OF ITALIAN PATENTS WITH EFFECTIVE DATE BEFORE 2007 MAY HAVE OCCURRED AT ANY TIME BEFORE 2007. THE CORRECT EFFECTIVE DATE MAY BE DIFFERENT FROM THE ONE RECORDED.

Effective date: 20040225

REG Reference to a national code

Ref country code: GB

Ref legal event code: FG4D

RIN1 Information on inventor provided before grant (corrected)

Inventor name: OLSSON, KJELL

REF Corresponds to:

Ref document number: 69631629

Country of ref document: DE

Date of ref document: 20040401

Kind code of ref document: P

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: DK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20040525

NLV1 Nl: lapsed or annulled due to failure to fulfill the requirements of art. 29p and 29m of the patents act
ET Fr: translation filed
PLBE No opposition filed within time limit

Free format text: ORIGINAL CODE: 0009261

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: NO OPPOSITION FILED WITHIN TIME LIMIT

26N No opposition filed

Effective date: 20041126

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: DE

Payment date: 20080523

Year of fee payment: 13

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: GB

Payment date: 20080522

Year of fee payment: 13

GBPC Gb: european patent ceased through non-payment of renewal fee

Effective date: 20090513

REG Reference to a national code

Ref country code: FR

Ref legal event code: ST

Effective date: 20100129

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: FR

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20090602

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: FR

Payment date: 20080526

Year of fee payment: 13

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: GB

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20090513

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: DE

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20091201