EP0750774B1 - Procede de detection du trafic et de situations de trafic sur des axes routiers, de preference sur des autoroutes - Google Patents

Procede de detection du trafic et de situations de trafic sur des axes routiers, de preference sur des autoroutes Download PDF

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Publication number
EP0750774B1
EP0750774B1 EP95910428A EP95910428A EP0750774B1 EP 0750774 B1 EP0750774 B1 EP 0750774B1 EP 95910428 A EP95910428 A EP 95910428A EP 95910428 A EP95910428 A EP 95910428A EP 0750774 B1 EP0750774 B1 EP 0750774B1
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Prior art keywords
traffic
trend
factor
measuring
speed
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EP95910428A
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German (de)
English (en)
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EP0750774A1 (fr
Inventor
Fritz Busch
Andrea Ghio
Johannes Konrad
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Siemens AG
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Siemens AG
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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 for traffic detection and traffic situation detection on highways according to the preamble of claim 1.
  • Procedures that display appropriate ads based on traffic measurements turn on the variable message sign.
  • the control is point based if it is on a certain point of the traffic flow (e.g. construction sites or Road narrowing), due to the route (generally under known as "line manipulation") if they are refers to a route, or network-related if it is the automatic rerouting from a normal route to an alternative route undertakes (alternating directions).
  • the control logic is to keep the display manageable relatively simple.
  • the prepared local measured values like generally smoothed traffic, smoothed Speed and local traffic density are predefined with Threshold values compared to make a statement or to control the variable message sign.
  • EP-A-0 171 098 describes a method for traffic detection and disclosed for traffic control on highways, which at least two measuring points for vehicle detection with traffic sensors having.
  • Traffic data is in the form of vehicle speeds taking into account the traffic volume determined, processed and evaluated. It will there the determined speed data of at least two, considered in a certain length of spaced measuring points and based on logical decisions with predefined ones Speed values compared.
  • Iokibe T. et al. is a Method known in which only the traffic volume measured and this together with experience of Traffic levels are assessed using fuzzy logic get an estimate of the expected traffic.
  • the object of the invention is an early and reliable, automatic detection of critical traffic situations, such as traffic disruptions caused by congestion or an accident, on motorways to the road users in good time before this Warning situation.
  • these are the respective measuring cross sections with traffic sensors installed for each lane, Traffic data recorded and in a processing device provided for this purpose processed for traffic control. From the regularly recorded traffic data: speed and traffic volume, are in a traffic data processing device certain traffic parameters derived. For this purpose, two adjacent measuring points form a measuring section, which has a certain distance, for example 3 km. From the traffic data from these measuring points The following traffic quantities are formed:
  • a speed density difference (vk-D) according to the relationship as specified in claim 1.
  • the speed density difference takes into account the speed and the traffic density of both measurement cross sections.
  • a second traffic parameter a trend factor is formed that is continuous from the ratio of the heavy traffic of the first and the second measuring point is formed, but only the values above one certain period, e.g. the last 30 minutes.
  • the third traffic parameter is the traffic volume trend the respective measuring point as a measure of the dynamic Situation development, i.e. the temporal development the traffic volume.
  • These three traffic parameters are based on fuzzy logic processed to detect critical traffic situations, to make a statement about the probability as an output variable for a critical traffic situation. This probability quantity is dependent on a predeterminable threshold value evaluated to a display recommendation to generate for the variable message signs.
  • fuzzy logic for traffic situation detection on highway roads has a number of advantages.
  • the evaluation the input data is very simple. Multiple inputs can can be linked further. This makes it possible for one Measure to use multiple inputs at the same time, even if they are not particularly meaningful individually. Leading on average at a faster response time.
  • a soft mindset of fuzzy logic it is possible instead of a rigid binary decision (Jam or no jam on a cross section) a soft one To determine transition in the form of a probability (e.g. the probability of a jam at this cross-section is 70%). That has the Advantage that this result with a correspondingly predetermined Threshold value can be evaluated so that a reliable display recommendation can be made.
  • the traffic volume (Q), which also traffic volume is called, and the traffic density (K) is used.
  • the Traffic strength gives the number of vehicles on a measurement cross section on, based on a unit of time, for example one hour.
  • the traffic density is a measure of the number of the vehicles related to a certain section of the route. It is operated with a so-called local traffic density, the the number of vehicles on the measurement cross section relates and takes into account the corresponding speed.
  • the traffic parameter speed density difference vk-D average speed is calculated from the local traffic data and the traffic density of two neighboring cross sections (Measuring points) according to that specified in claim 1 Formula.
  • the first term of the speed density difference refers to the measuring cross section MQi, the second to the downstream measuring cross section MQ (i + 1).
  • To the traffic sizes different measurement cross sections compare to they are each set to the adjustable maximum values related to the traffic quantities of the cross sections (max. free speed and max. Traffic density). Is the traffic condition undisturbed at the measuring cross-section, i.e. the speed is not small and the traffic density is not great then the corresponding term is very small in the range Values. If there is an unstable traffic condition on the measurement cross-section, i.e. the speed is low and the traffic density large, the value of the term concerned increases on. Conclusions can thus be drawn from the difference between the two terms be pulled to the current traffic condition.
  • the trend factor (FT) is used as an indicator of a fault. It monitors the inflow and outflow of vehicles in the measurement section (MA) that have a specific Route length, for example 3 km, and is formed by the two measuring points (MQi and (MQi + 1)). In the event of a critical traffic situation, more vehicles drive into and out of the measuring section, the trend factor (FT) thereby increases exponentially.
  • the calculation of the Trend factor is based on the generally unsmoothed traffic volumes, i.e. the heavy traffic on the two cross sections. This will result in higher accuracy and faster Response achieved. To the influence of measurement errors too decrease, the trend factor is only based on the last measuring interval, that means a period of for example 30 minutes.
  • the third traffic parameter serves to assess the dynamic situation development.
  • the calculation is based on the generally unsmoothed recorded traffic data.
  • the traffic strength trend will also considered at the two measuring cross sections.
  • the input variables of the fuzzy logic are influenced by many factors dependent, in particular on the measuring point distance, the route geometry, i.e. Incline or descent, environmental conditions, such as Wetness, snow, black ice, day or night, and possible others Influences. So the influences are not just stationary, but also of a dynamic nature. Therefore, in continuing education the traffic parameters of the method according to the invention calibrated so that the fuzzy system inputs (Traffic parameters) always independent of external influences can rate right away. The sizes are dependent on this their past values dynamically calibrated.
  • this traffic data becomes a calibration factor for the speed density difference and a calibration factor formed for the trend factor and in a calibration facility, between the traffic data preparation and the fuzzy editing is arranged with the current one Relating traffic parameters.
  • the actual Velocity density difference is determined by the velocity density difference calibration factor and the respective one current trend factor divided by the trend calibration factor.
  • the speed density difference depends on environmental conditions such as wet, fog, day / night etc.
  • This fuzzy input variable is therefore evaluated by the dynamic calibration factor.
  • the value of this factor can be used as a threshold for the speed density difference above which there is a high probability of a critical traffic situation (disruption).
  • the calibration factor is only calculated if the speed density difference is below a certain threshold, for example 0.3.
  • the factor is made up of the mean, the standard deviation from the speed density difference and its defined threshold. The calculation of the mean value and the standard deviation is only carried out on the basis of the relative maxima of the speed density-difference curve.
  • vk_D medium ⁇ vk_D + (1- ⁇ ) vk_D middle age
  • vk_D ⁇ ⁇ (vk_D-vk_D medium ) 2nd + (1- ⁇ ) ⁇ ⁇ vk_D old
  • vk_D middle age vk_D medium
  • vk_D old ⁇ vk_D
  • Calibration factor vk_D vk_D medium +2.
  • the current speed density difference is shown by this Divided calibration factor.
  • the characteristic value of the trend factor is sought, which you consider "small". This characteristic value becomes like this defines that it includes the set of all values of the trend factor, their relative cumulative frequency below one Threshold.
  • a frequency table is introduced whose classes are defined according to the table will.
  • a class is a defined range of values of the Trend factor, with all classes together covering the entire value range describe the trend factor. For every measuring interval the current trend factor is assigned to a class that the respective class is then incremented. For every interval can be used to determine the measured value for which the relative Total frequency below the specified threshold lies.
  • classification is more graded for very small and large values, a finer gradation is chosen for the important calibration range: class 0 1 2nd 3rd ......... 36 37 Characteristic value range -1..0.15 > 0.15 > 0.225 > 0.275 ......... > 1,975 > 2,025 F T ⁇ 0.225 ⁇ 0.275 ⁇ 0.325 ⁇ 2.025 ⁇ 2,075 Classification
  • KFT characteristic normalized expression "small” of the trend factor
  • the current trend factor is determined by this calibration factor divided.
  • the method according to the invention for detecting critical traffic situations is used in a special embodiment of the invention for accident detection.
  • traffic parameters trend factor and traffic strength trend of the first measurement cross-section
  • a crowd formation is recognized in a crowd detection and a pulse probability quantity is derived.
  • an accident criterion is derived from the traffic parameter traffic strength trend of the second measurement cross section and the pulse probability variable with the help of the fuzzy decision, which together with the trend factor and the speed density difference enables an accident detection.
  • the traffic parameters, traffic strength trend at the measuring point MQ1 and at the measuring point MQ2 are used for the fuzzy accident decision, with which a preliminary investigation for an accident is carried out.
  • a bulk detection is carried out.
  • a crowd hires Vehicle collective with high traffic volume and traffic density represents which enters the measuring section.
  • the traffic parameter used for accident detection Trend factor leaves two interpretations for very large values to. There is an accident, i.e. about one longer period there are more vehicles in the measurement section retracted as extended, or is in the measuring section Bunch retracted. A bunch is like a density wave, such as in the event of a sudden cancellation of a Bottlenecks arise. To differentiate between these two cases as stated above, a bulk detection is carried out.
  • the fuzzy logic As an input variable of the fuzzy logic, the traffic volume trend, the pulse probability in the previous one Measurement interval, and the trend factor used.
  • As an output variable is a value for the probability of one Pulks are available in the considered measurement section.
  • the traffic volume trend on the downstream is now also decreasing Measurement cross section from at a high value for an accident criterion, so is the probability of an accident very high.
  • the traffic volume trend on the downstream measuring cross-section decreases the possibility of an accident, as well as when the pulse probability increases.
  • a The exception is the case if a Disorder was very likely.
  • the accident criterion essentially independent of the pulse probability and the traffic volume trend, because in the case of the already In the last measurement interval, a fault identified can both the pulse rate as well as the traffic volume trend rise again.
  • Accident detection is the decision level which ultimately results in the probability of a Accident leads. Depending on this size, a Warning activated on the display cross-section.
  • the variables become: accident criterion, Trend factor and speed density difference that The probability of an accident is derived, as already explained.
  • accident criterion there is most likely an accident.
  • the bigger (positive) the difference in speed density the more likely is an accident.
  • speed density difference Probability of an accident even more.
  • the trend factor has more influence.
  • the result evaluation is based on on the likelihood of an accident, a recommendation to display, e.g. Traffic jam warning, for the variable message signs derived and causes the display.
  • the AS auto Avenue here as a freeway with e.g. two tracks in shown a direction of travel, has two measurement cross-sections MQi and MQ (i + 1), which are arranged at a certain distance are and form a measuring section MA.
  • the traffic sensors VS e.g. Vehicle detectors, for example can be formed by induction double loops Traffic data VD recorded and a traffic data preparation VDA supplied. Speed is used as traffic data v, the traffic density K and the traffic volume Q detected and processed further.
  • the traffic parameters speed density difference vk-D, the trend factor FT and the high-traffic QTi and QTi + 1 determined separately on the measuring cross sections MQi and MQi + t and fed to fuzzy logic for further processing.
  • the fuzzy processing device is called FUB.
  • the probability quantity formed there, as already explained above Flat share for a critical traffic situation is evaluated in the result evaluation facility EBE on the basis of a Predeterminable threshold value SW evaluated to a control signal SG, for example as a display recommendation for a variable message sign To generate VWZ.
  • KFB becomes the traffic data VD or traffic parameters vk-D and FT to form a calibration factor for the speed density difference KFv and one Calibration factor used for the trend factor KFT.
  • KFB becomes the traffic data VD or traffic parameters vk-D and FT to form a calibration factor for the speed density difference KFv and one Calibration factor used for the trend factor KFT.
  • Fig. 3 the accident detection is shown schematically.
  • the input variables become a trend factor FT and traffic volume trend QTi at the measurement cross section MQi one Pulse probability quantity PWG using fuzzy logic derived.
  • This pulse probability quantity PWG is in a preliminary accident investigation STV with the traffic parameter Traffic strength trend QT (i + 1) of the measurement cross section MQ (i + 1) considered and derived an accident criterion STK.
  • This criterion STK is used together with the trend factor FT and the velocity density difference vk-D is considered to be to be able to close an accident.
  • This is with the accident detection STE indicated.
  • the Accident detection STE to an accident probability quantity SWG closed in a subsequent earnings assessment facility EBE is treated further.

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

Claims (5)

  1. Procédé de détection du trafic et de situations de trafic sur des axes routiers (AS), de préférence sur des autoroutes, comprenant des points de mesure installés à cet effet, appelés sections de mesure (MQ1,MQ2...), pour la détection de véhicules automobiles au moyen de capteurs de trafic (VS), et comprenant un dispositif de traitement de données de trafic (VDVE) pour commander le trafic, des données de trafic (VD), comme la vitesse (v) des véhicules, l'intensité du trafic (Q) - c'est le nombre de véhicules dans une section de mesure par rapport à une unité de temps - et la densité du trafic (K) - c'est le nombre de véhicules par rapport à un certain tronçon de la voie - étant relevées régulièrement aux postes de mesure (MQ1, MQ2,... ) et, à partir de ces données, certaines grandeurs caractéristiques de trafic étant calculées dans une préparation de données de trafic (VDA), en outre deux points de mesure voisins, par exemple les sections de mesure MQ1 et MQ2, ou, d'une façon plus générale, les sections de mesure Mqi et MQ (i+1), formant un tronçon de mesure (MA) d'une certaine longueur (I),
       caractérisé en ce que l'on calcule à partir des données de trafic (VD) provenant de deux de ces points de mesure les grandeurs caractéristiques de trafic suivantes :
    a) la différence des densités de vitesse (vk-D) selon la relation suivante : vk - D = Vfi-Vi Vfi + ki kmaxi - Vfi+1-Vi+1 Vfi+1 + ki+1 kmaxi+1 avec
    Vfi, Vf (i+1) :
    valeur maximale réglable de la vitesse à la section de mesure Mqi, MQ (i+1)
    kmaxi, kmax (i+1):
    valeur maximale réglable de la densité du trafic à la section de mesure MQi, MQ (i+1)
    ki:
    densité du trafic après la section de mesure MQi
    k (i+1) :
    densité du trafic avant la section de mesure MQ (i+1)
    vi, v (i+1) :
    vitesse moyenne à la section de mesure MQi, MQ (i+1)
    b) un facteur de tendance (FT), qui est calculé de manière continue à partir de la relation entre les intensités du trafic (Qi / Q (i+1)) de la première et de la deuxième section de mesure (MQi, MQ (i+1)), mais sur une certaine période (t) située dans la gamme des minutes,
    c) la tendance de l'intensité du trafic (QTi, QT (i+1)) des sections de mesure respectives (MQi, MQ (i+1)), la tendance étant déduite, à l'aide de la fonction de l'intensité du trafic (Q) sur le temps (courbe Q (t) ), de la montée de la tangente à la courbe ; ces trois grandeurs caractéristiques du trafic (vK-D ; FT ; QTi et QT (i+1)) sont traitées dans une logique floue (FUB) de détection de situations de trafic critiques dans la section de mesure (MA) considérée et amenées comme grandeurs de probabilité (WG) à un dispositif d'évaluation de résultat (EBE) monté en aval, dans lequel sont générés, en fonction de seuils (SW) réglables, des signaux de commande (SG) pour des panneaux de signalisation réglables (WVZ).
  2. Procédé selon la revendication 1, caractérisé en ce que les grandeurs caractéristiques de trafic différence des densités de vitesse (vk-D) et facteur de tendance (FT) sont calibrées de manière dynamique en fonction de leurs valeurs antérieures, à partir des données de trafic (VD) étant calculés un facteur de calibrage (KFv) pour la différence des densités de vitesse (vK-D) et un facteur de calibrage (KFT) pour le facteur de tendance (FT), et en ce que dans un dispositif de calibrage (KE) disposé entre la préparation de données de trafic (VDA) et le traitement à logique floue (FUB), la différence actuelle des densités de vitesse (vk-D) est divisée par le facteur de calibrage (KFv) pour la différence des densités de vitesse et le facteur de tendance actuel par le facteur de calibrage (KFT) pour le facteur de tendance.
  3. Procédé selon la revendication 2, caractérisé en ce que pour calibrer la différence des densités de vitesse (vk-D), celle-ci est évaluée, la valeur du facteur de calibrage (KFv) pour la différence des densités de vitesse étant un seuil pour la différence des densités de vitesse (vk-D), à partir duquel l'existence d'une situation de trafic critique est fort probable.
  4. Procédé selon la revendication 2, caractérisé en ce que pour calibrer le facteur de tendance (FT), une valeur caractéristique du facteur de tendance estimée "petite" est définie de telle sorte qu'elle englobe l'ensemble de toutes les valeurs du facteur de tendance, dont la fréquence cumulée relative est inférieure à un seuil, une table des fréquences présentant plusieurs classes étant réalisée avec des gammes de valeurs définies du facteur de tendance et le facteur de tendance actuel étant affecté à une classe pour en déduire le facteur de calibrage (KFT).
  5. Procédé selon l'une quelconque des revendications 1 à 4, caractérisé en ce qu'une perturbation représentant une situation de trafic critique est détectée et affichée, une formation de "pulk" (PE) étant détectée à partir du facteur de tendance (FT) et de la tendance de l'intensité du trafic (QTi) de la première section de mesure (MQi) et une grandeur de probabilité de "pulk" (PWG) étant calculée, laquelle est mise en rapport avec la tendance de l'intensité du trafic (QT (i-1)) de la deuxième section de mesure (MQ (i+1)) pour en déduire (STV) un critère de perturbation (STK), en outre une perturbation étant détectée (STE) à partir du facteur de tendance (FT) et de la différence des densités de vitesse (vk-D) ainsi que du critère de perturbation (STK) et une grandeur de probabilité de perturbation (SWG) étant calculée.
EP95910428A 1994-03-14 1995-03-01 Procede de detection du trafic et de situations de trafic sur des axes routiers, de preference sur des autoroutes Expired - Lifetime EP0750774B1 (fr)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
DE4408547A DE4408547A1 (de) 1994-03-14 1994-03-14 Verfahren zur Verkehrserfassung und Verkehrssituationserkennung auf Autostraßen, vorzugsweise Autobahnen
DE4408547 1994-03-14
PCT/DE1995/000265 WO1995025321A1 (fr) 1994-03-14 1995-03-01 Procede de detection du trafic et de situations de trafic sur des axes routiers, de preference sur des autoroutes

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EP0750774A1 EP0750774A1 (fr) 1997-01-02
EP0750774B1 true EP0750774B1 (fr) 1998-05-27

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US (1) US5696502A (fr)
EP (1) EP0750774B1 (fr)
AT (1) ATE166738T1 (fr)
DE (2) DE4408547A1 (fr)
FI (1) FI963627A (fr)
WO (1) WO1995025321A1 (fr)

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EP0750774A1 (fr) 1997-01-02
DE4408547A1 (de) 1995-10-12
WO1995025321A1 (fr) 1995-09-21
ATE166738T1 (de) 1998-06-15
FI963627A0 (fi) 1996-09-13
FI963627A (fi) 1996-09-13
US5696502A (en) 1997-12-09
DE59502343D1 (de) 1998-07-02

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