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WO1996036929A1 - Detection et prevision des perturbations de la circulation routiere - Google Patents

Detection et prevision des perturbations de la circulation routiere Download PDF

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Publication number
WO1996036929A1
WO1996036929A1 PCT/SE1996/000620 SE9600620W WO9636929A1 WO 1996036929 A1 WO1996036929 A1 WO 1996036929A1 SE 9600620 W SE9600620 W SE 9600620W WO 9636929 A1 WO9636929 A1 WO 9636929A1
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WO
WIPO (PCT)
Prior art keywords
traffic
queue
flow
predicted
values
Prior art date
Application number
PCT/SE1996/000620
Other languages
English (en)
Inventor
Kjell Olsson
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
Priority to EP96914510A priority Critical patent/EP0771447B1/fr
Priority to DE69631629T priority patent/DE69631629T2/de
Publication of WO1996036929A1 publication Critical patent/WO1996036929A1/fr

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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.

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  • 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.
PCT/SE1996/000620 1995-05-19 1996-05-13 Detection et prevision des perturbations de la circulation routiere WO1996036929A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP96914510A EP0771447B1 (fr) 1995-05-19 1996-05-13 Detection et prevision des perturbations de la circulation routiere
DE69631629T DE69631629T2 (de) 1995-05-19 1996-05-13 Erfassung und Vorhersage von Verkehrsbehinderungen

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SE9501919A SE503515C2 (sv) 1995-05-19 1995-05-19 Detektering och prediktion av trafikstörningar
SE9501919-6 1995-05-19

Publications (1)

Publication Number Publication Date
WO1996036929A1 true WO1996036929A1 (fr) 1996-11-21

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Country Status (4)

Country Link
EP (1) EP0771447B1 (fr)
DE (1) DE69631629T2 (fr)
SE (1) SE503515C2 (fr)
WO (1) WO1996036929A1 (fr)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999041726A1 (fr) * 1998-01-30 1999-08-19 Dinbis Ab Procede et dispositif destines a la commande d'un reseau routier
EP0936590A3 (fr) * 1998-02-13 2000-09-27 DaimlerChrysler AG Procédé et appareil de détermination des conditions de circulation d'un réseau routier
DE102006033532A1 (de) * 2006-07-20 2008-01-24 Deutsche Telekom Ag Verfahren und Vorrichtung zur Generierung von Frühwarnungen vor Verkehrszusammenbrüchen an Engstellen
WO2013113029A1 (fr) * 2012-01-27 2013-08-01 Triangle Software, Llc Estimation des distributions temporelles de parcours sur des artères signalisées
US8958988B2 (en) 2002-03-05 2015-02-17 Pelmorex Canada Inc. Method for choosing a traffic route
US8982116B2 (en) 2009-03-04 2015-03-17 Pelmorex Canada Inc. Touch screen based interaction with traffic data
US9046924B2 (en) 2009-03-04 2015-06-02 Pelmorex Canada Inc. Gesture based interaction with traffic data
US9127959B2 (en) 2003-07-25 2015-09-08 Pelmorex Canada Inc. System and method for delivering departure notifications
US9336448B2 (en) 2014-08-11 2016-05-10 Here Global B.V. Variable speed sign value prediction and confidence modeling
US9390620B2 (en) 2011-05-18 2016-07-12 Pelmorex Canada Inc. System for providing traffic data and driving efficiency data
US9448690B2 (en) 2009-03-04 2016-09-20 Pelmorex Canada Inc. Controlling a three-dimensional virtual broadcast presentation
US10109184B2 (en) 2014-10-08 2018-10-23 Here Global B.V. Probe based variable speed sign value
US10223909B2 (en) 2012-10-18 2019-03-05 Uber Technologies, Inc. Estimating time travel distributions on signalized arterials

Families Citing this family (4)

* 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
MX2008010787A (es) 2006-02-21 2008-10-27 Toyama Chemical Co Ltd Proceso para la produccion de ester de 3-[5-[4-(ciclopentiloxi)-2- hidroxibenzoil]-2-[(2,3-dihidro-1,2-benzisoxazol-6-il-3-oxo-2-sus tituido)metoxi] fenil] propionato e intermedio para el proceso.
US9240123B2 (en) 2013-12-13 2016-01-19 Here Global B.V. Systems and methods for detecting road congestion and incidents in real time
US11378403B2 (en) 2019-07-26 2022-07-05 Honeywell International Inc. Apparatus and method for terrain aided navigation using inertial position

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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

Patent Citations (2)

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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 (2)

* Cited by examiner, † Cited by third party
Title
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, Volume AC-24, No. 1, February 1979, JOHN S. BARAS et al., "Discrete Time Point Processes in Urban Traffic Queue Estimation". *
METHODS OF OPERATIONS RESEARCH, Volume 50, 1985, B. BETRO et al., "Recursive Predictors for Markov Chains in Urban Traffic". *

Cited By (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1999041726A1 (fr) * 1998-01-30 1999-08-19 Dinbis Ab Procede et dispositif destines a la commande d'un reseau routier
US6496773B1 (en) 1998-01-30 2002-12-17 Kjell Olsson Method and means for network control of traffic
EP0936590A3 (fr) * 1998-02-13 2000-09-27 DaimlerChrysler AG Procédé et appareil de détermination des conditions de circulation d'un réseau routier
US9082303B2 (en) 2002-03-05 2015-07-14 Pelmorex Canada Inc. Generating visual information associated with traffic
US8958988B2 (en) 2002-03-05 2015-02-17 Pelmorex Canada Inc. Method for choosing a traffic route
US9640073B2 (en) 2002-03-05 2017-05-02 Pelmorex Canada Inc. Generating visual information associated with traffic
US9070291B2 (en) 2002-03-05 2015-06-30 Pelmorex Canada Inc. Method for predicting a travel time for a traffic route
US9401088B2 (en) 2002-03-05 2016-07-26 Pelmorex Canada Inc. Method for predicting a travel time for a traffic route
US9602977B2 (en) 2002-03-05 2017-03-21 Pelmorex Canada Inc. GPS generated traffic information
US9489842B2 (en) 2002-03-05 2016-11-08 Pelmorex Canada Inc. Method for choosing a traffic route
US9368029B2 (en) 2002-03-05 2016-06-14 Pelmorex Canada Inc. GPS generated traffic information
US9644982B2 (en) 2003-07-25 2017-05-09 Pelmorex Canada Inc. System and method for delivering departure notifications
US9127959B2 (en) 2003-07-25 2015-09-08 Pelmorex Canada Inc. System and method for delivering departure notifications
DE102006033532A1 (de) * 2006-07-20 2008-01-24 Deutsche Telekom Ag Verfahren und Vorrichtung zur Generierung von Frühwarnungen vor Verkehrszusammenbrüchen an Engstellen
US9046924B2 (en) 2009-03-04 2015-06-02 Pelmorex Canada Inc. Gesture based interaction with traffic data
US8982116B2 (en) 2009-03-04 2015-03-17 Pelmorex Canada Inc. Touch screen based interaction with traffic data
US9448690B2 (en) 2009-03-04 2016-09-20 Pelmorex Canada Inc. Controlling a three-dimensional virtual broadcast presentation
US10289264B2 (en) 2009-03-04 2019-05-14 Uber Technologies, Inc. Controlling a three-dimensional virtual broadcast presentation
US9547984B2 (en) 2011-05-18 2017-01-17 Pelmorex Canada Inc. System for providing traffic data and driving efficiency data
US9390620B2 (en) 2011-05-18 2016-07-12 Pelmorex Canada Inc. System for providing traffic data and driving efficiency data
US9293039B2 (en) 2012-01-27 2016-03-22 Pelmorex Canada Inc. Estimating time travel distributions on signalized arterials
WO2013113029A1 (fr) * 2012-01-27 2013-08-01 Triangle Software, Llc Estimation des distributions temporelles de parcours sur des artères signalisées
US10223909B2 (en) 2012-10-18 2019-03-05 Uber Technologies, Inc. Estimating time travel distributions on signalized arterials
US10971000B2 (en) 2012-10-18 2021-04-06 Uber Technologies, Inc. Estimating time travel distributions on signalized arterials
US9336448B2 (en) 2014-08-11 2016-05-10 Here Global B.V. Variable speed sign value prediction and confidence modeling
US10417509B2 (en) 2014-08-11 2019-09-17 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
US11069229B2 (en) 2014-10-08 2021-07-20 Here Global B.V. Probe based variable speed sign value

Also Published As

Publication number Publication date
SE9501919L (sv) 1996-07-01
DE69631629T2 (de) 2004-12-23
EP0771447B1 (fr) 2004-02-25
DE69631629D1 (de) 2004-04-01
SE9501919D0 (sv) 1995-05-19
SE503515C2 (sv) 1996-07-01
EP0771447A1 (fr) 1997-05-07

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