EP1480182B1 - Compteur d'essieux sans contact pour traffic routier - Google Patents

Compteur d'essieux sans contact pour traffic routier Download PDF

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Publication number
EP1480182B1
EP1480182B1 EP04450111A EP04450111A EP1480182B1 EP 1480182 B1 EP1480182 B1 EP 1480182B1 EP 04450111 A EP04450111 A EP 04450111A EP 04450111 A EP04450111 A EP 04450111A EP 1480182 B1 EP1480182 B1 EP 1480182B1
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EP
European Patent Office
Prior art keywords
axle counter
counter system
unit
measuring
signal
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.)
Expired - Lifetime
Application number
EP04450111A
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German (de)
English (en)
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EP1480182A3 (fr
EP1480182A2 (fr
Inventor
Franz Dipl.-Ing. Graf
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.)
Joanneum Research Forschungs GmbH
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Joanneum Research Forschungs GmbH
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Publication date
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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
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors

Definitions

  • the invention relates to a non-contact axle counting system for road traffic comprising at least one measuring device for detecting the changes in the environmental parameters, such as sound, temperature or vibration, produced by a moving vehicle to a measuring range.
  • Non-contact axle counting systems are known in which induction loops are integrated into the roadway. Such systems have the disadvantage that they must be installed in the roadway. Even in case of maintenance, the road would be dug up, which is not only associated with high costs, but also with long-lasting traffic obstructions.
  • No. 5,821,879 A shows such a non-contact axle counting system for multi-lane lanes.
  • the axle counting system includes an LED array on one lane side and a photodiode array on the opposite lane side so that a plurality of light barriers are formed. The signal interruptions recorded by these light barriers are directed to a signal evaluation system and from this the traffic volume is derived on each lane.
  • the object of the invention is therefore to provide a non-contact axle counting system which avoids the above disadvantages and allows for low installation and maintenance effort a reliable axle counting for multi-lane roads.
  • At least one measuring device for detecting the movement of a moving vehicle in a measuring range such.
  • changes in environmental parameters such as sound, temperature or vibration are provided.
  • measuring devices can conveniently and inexpensively on any existing superstructures of the roadway, such as bridges or signage, mounted and maintained, with an assignment of the measuring devices to lanes can be particularly easily implemented, and such a measuring device is by their Focusing on a suitable measuring range suitable to detect the vehicles without contact also in multi-lane road sections.
  • the at least one of the at least one measuring device is designed as a laser vibrometer.
  • Laservibrometer allow a high-precision non-contact detection of road vibrations, as caused by the weight and / or the self-vibration of moving vehicles on the tires on the road.
  • the at least one of the at least one measuring device is designed as a directional microphone, in particular as a series microphone system.
  • a directional microphone in particular as a series microphone system.
  • an analysis unit is provided for the analysis of measurement signals.
  • the interpretation of the measurement signals can be facilitated and improved.
  • the analysis unit comprises a signal amplification device for amplifying the measurement signals, in particular by a factor of 100 to 10,000. This means that even very weak signals can be made accessible to an evaluation.
  • the analysis unit comprises an A / D converter with a sampling rate of in particular 1 kHz to 50 kHz, or more particularly between 6 kHz and 16 kHz.
  • a sampling rate of in particular 1 kHz to 50 kHz, or more particularly between 6 kHz and 16 kHz.
  • the A / D converter has a resolution of 8 to 24 bits, preferably 12 to 20 bits. Such a resolution is sufficient for subsequent data analyzes and leads at the same time to still easily manageable amounts of data.
  • the analysis unit comprises a computing unit, which is arranged downstream of the A / D converter.
  • the digitized measurement signals can be prepared for a classification of the vehicles to be detected.
  • a memory and / or a user interface is connected to the arithmetic unit.
  • the memory allows the storage of parameters influencing the measurement, while corresponding measurement or environmental parameters can be changed via the user interface.
  • the arithmetic unit comprises a time segmentation device for subdividing discrete time signals into time signal blocks, in particular from a block length of 10 ms to 1500 ms.
  • a temporal segmentation device allows a higher resolution in the analysis of the measurement signals.
  • the arithmetic unit comprises a spectral analyzer for calculating the frequency spectrum of the measurement signals and / or an energy calculation unit for calculating the signal energy, in particular the short-term signal energy. From the data thus calculated, data patterns can be generated which are particularly well suited for a subsequent classification.
  • the arithmetic unit comprises a classifier, which is arranged downstream of the spectral analyzer and / or the energy calculation unit, and which classifies the data pattern resulting from the corresponding calculation results of the spectral analyzer and / or the energy calculation unit.
  • Classified data samples allow a particularly reliable determination of the number of axles of different vehicle types.
  • the classifier can be operated in a learning mode for generating mathematical models for specific vehicle classes.
  • the axle counting system can be calibrated to a large number of vehicle types to be determined, and can also be retrofitted cost-effectively in the event of a change in demand to new vehicle types.
  • the learning mode is operable using learning algorithms.
  • the learning algorithm can be operated using the Hebbian learning method, the backpropagation rule or the forward-backward algorithm.
  • the classifier can be operated in a recognition mode for the assignment of a current measurement signal to a vehicle class.
  • the analysis unit is arranged downstream of a communication unit to which the calculation result of the classifier is transferred.
  • a vehicle 10 is via its tires 12 at contact points 11 in mechanical interaction with the roadway 20. Due to the weight, the proper motion (vibration) and the relative movement of the vehicle 10 to the roadway 20 11 vibrations are now mainly in the region of the contact points excited. These are on the one hand sound vibrations caused mainly by the rolling of the tires 12 on the roadway 20, but also by engine and flow noise, on the other hand vibrations of the surface of the roadway 20 itself, the amplitude may be on the order of up to several millimeters , These physical quantities, which are subject to change if a motor vehicle passes the location of the measurement, can be detected by different measuring principles.
  • the amplitude of the radiated sound waves can be described in different ways - e.g. as the velocity of movement of the air molecules (sound velocity) or as pressure which is generally preferred for describing the amplitude.
  • the sound pressure results from the fluctuation of the air pressure above and below the atmospheric air pressure.
  • Measuring devices 1, which serve to convert the sound pressure into an electrical signal can be any suitable sound sensors, such as e.g. electroacoustic transducers. In this case, the measuring device 1 can also be designed as a directional microphone or as a series microphone system, which contributes to an improved spatial resolution of a measurement signal.
  • the vibrations of the surface of the traffic route can in principle be measured with accelerometers which work according to the piezoelectric effect - but not without contact. By means of optical methods, these vibrations can also be measured without contact.
  • For detecting the vibrations of the surface of the roadway 20 are measuring devices 1 in an embodiment of Laservibrometem especially good.
  • Laser vibrometers work on the principle of Doppler frequency shift. In this case, the laser light scattered back by a vibrating object (for example, the surface of a roadway 20) provides all information for the determination of surface velocity and absolute oscillation amplitudes. In contrast to other optical methods (eg laser scanners), it is not the distance of an existing object that is of interest, but the vibration velocity of its surface. With the help of this high-precision measurement, the least vibration excitations of the surface of the roadway 20 can be detected.
  • the measuring devices 1 for measuring the changes of the environmental influences measure the strength of the sound waves, preferably the sound pressure of the sound waves or the vibrations of the surface of the roadway, when vehicles 10 approach or pass the mounting location of a measuring device 1.
  • the measuring devices 1 are mounted above the roadway 20 on superstructures such as bridges, signaling devices or signage (see Fig. 2), so that the monitoring of several parallel lanes is easily possible.
  • the measuring devices 1 convert the sound or vibration signals into electrical energy.
  • the measuring devices 1 generate analog or digital measuring signals as a function of time.
  • the measurement signals generated by the measuring devices 1 are forwarded to the analysis unit 2 via separate signal lines or after modulation or coding via a common signal line.
  • the measurement signals can also be transmitted to the analysis unit 2 by a wireless connection (eg radio, infrared, etc.) or via local area networks (LAN) or wireless LAN (WLAN).
  • the analysis unit 2 in turn communicates via a communication unit 8, for example with a central unit (not shown) for collecting, evaluating or further processing the results supplied by the analysis unit 2.
  • a communication unit 8 for example with a central unit (not shown) for collecting, evaluating or further processing the results supplied by the analysis unit 2.
  • the connection between the communication unit 8 and the central unit can be wired or wireless.
  • FIG. 3 shows the block diagram of the hardware structure of the analysis unit 2.
  • the measurement signals are forwarded to the signal amplification device 3, which amplifies the measurement signals by a fixed factor, preferably between 100 and 10,000, or with an automatic adjustment.
  • the following analog / digital converter 4 converts the analog signals into discrete values.
  • the sampling rate of the A / D converter 4 may vary from system to system and is generally between 1 kHz and 50 kHz. Particularly suitable are sampling frequencies between 6 kHz and 16 kHz.
  • the resolution of the A / D converter 4 is in the range of 8 to 24 bits, with the range of 12 to 20 bits is preferably used.
  • the system has a computing unit 5, which is connected to the A / D converter 4 and the data memory 6.
  • the arithmetic unit 5 is used to execute the calculation steps that are applied to the digitized measurement signals.
  • the user interface 7 and the memory 6 are connected to the arithmetic unit 5. Through the user interface 7 inputs can be made by a user.
  • the input by the user may be made by any suitable device, such as a keyboard, a mouse, a touch screen, or any combination of these devices.
  • the result of the analysis is transferred via an output to the interface of the communication unit 8 and further processed there.
  • FIG. 4 shows, in the form of a block diagram, the analysis of the measurement signals as they occur in the arithmetic unit 5.
  • the measurement signals are supplied from the A / D converter 4 to a time segmentation 51 in order to subdivide the discrete time signals into time blocks, wherein the block length may be between 10 ms and 1500 ms.
  • the individual blocks are further extracted from the signal with an overlap. This overlap serves to increase the resolution and can take values between 20% and 70%.
  • the data are transferred on the one hand to the energy calculation unit 53 and also to the spectrum analyzer 52.
  • variable t stands for the time
  • T for the length of a signal block
  • i stands for the number of the block within the entire acoustic signal.
  • s i (t) denotes the measurement signal in the time domain of the i- th block
  • S i (w) the frequency spectrum of the i- th block.
  • the variable w corresponds to the instantaneous frequency.
  • the analysis unit 2 consists of a spectrum analyzer 52 and an energy calculation unit 53 for calculating the signal energy.
  • the spectral analyzer 52 transforms the individual signal blocks from the time domain into the frequency domain. By default, these transformations are performed using Fourier transform.
  • the so-called Fast Fourier Transformation (FFT) is particularly well suited for this purpose.
  • the Fast Fourier Transformation corresponds to a digital approximation of the Fourier transformation.
  • the output of the spectrum analyzer 52 corresponds to the power density spectrum of the signal at the input and describes the amount of energy at a particular frequency interpolation point.
  • the number of frequency nodes is dependent on the number of discrete samples taken for the Fourier transform from the temporal signal, and is directly related to the above-mentioned block length T.
  • the Fourier transform for calculating the spectral content a time signal can also other methods, such as Linear Prediction Coding is used, which is known from the literature.
  • Another component of the analysis unit 2 is the energy calculation unit 53.
  • the window length of the resulting waveform of the signal energy can be significantly influenced.
  • the use of large window lengths corresponds to a low-pass filtering with a low cut-off frequency and has the consequence that short-term fluctuations of the signal in the signal energy are not reflected.
  • short window lengths result in a course of the signal energy that largely follows the temporal structure of the acoustic signal.
  • the block lengths T for calculating the energy correspond to the above values and can be between 10 ms and 1,500 ms.
  • the calculation results obtained from the energy calculation and the spectrum analyzer 52 are subsequently processed in the classifier 54.
  • the classifier 54 is capable of determining certain data patterns generated by the Calculation results are formed to classify. That is to say, when vehicles 10 with different numbers of axles pass by, certain data patterns result for the calculated energy as well as for the spectrum, which the classifier 54 can classify by the learning patterns previously presented to it.
  • the classifier 54 can be operated in two modes.
  • the first mode is also called a learning mode.
  • the learning mode serves to generate a mathematical model for each class to be recognized.
  • a learning algorithm is a sequence of mathematical calculation steps to iteratively approximate a mathematical model. Learning algorithms such as the Hebbian learning method, the backpropagation rule and the forward-backward algorithm are known from the literature.
  • the second mode is called the detection mode and represents the mode used in the normal operation of the detection system. During the recognition phase, one of the K signal classes is allocated to a currently present signal, and thus the number of axles of the currently passing vehicle 10 is determined. The result of the classification is passed on to the communication unit 8.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Length Measuring Devices With Unspecified Measuring Means (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Claims (17)

  1. Compteur d'essieux sans contact pour le trafic routier, comprenant au moins un dispositif de mesure (1) pour la détection des modifications des paramètres d'environnement, tels que le bruit, la température ou les vibrations, provoquées par le passage d'un véhicule (10) dans une zone de mesure, caractérisé en ce que le dispositif de mesure (1) au nombre d'un au moins est fixé au-dessus de la chaussée (20) et au-dessus de la hauteur du véhicule (10).
  2. Compteur d'essieux selon la revendication 1, caractérisé en ce que le ou au moins un des dispositifs de mesure (1) au nombre d'un au moins est conçu comme un vibromètre laser (1').
  3. Système de compteur d'essieux selon la revendication 1, caractérisé en ce que le au moins un des dispositifs de mesure (1) est conçu comme un microphone directionnel (1"), en particulier comme un système de microphones en série.
  4. Compteur d'essieux selon l'une des revendications 1 à 3, caractérisé en ce qu'il est prévu une unité d'analyse (2) pour l'analyse des signaux de mesure.
  5. Compteur d'essieux selon la revendication 4, caractérisé en ce que l'unité d'analyse (2) comprend un dispositif d'amplification des signaux (3) pour l'amplification des signaux de mesure, en particulier d'un facteur 100 à 10 000.
  6. Compteur d'essieux selon l'une des revendications 4 ou 5, caractérisé en ce que l'unité d'analyse (2) comprend un convertisseur analogique-numérique (4) ayant un taux d'échantillonnage de 1 kHz à 50 kHz en particulier, et notamment de 6 kHz à 16 kHz.
  7. Compteur d'essieux selon l'une des revendications 4 à 6, caractérisé en ce que le convertisseur analogique-numérique (4) a une résolution de 8 à 24 bits, de préférence de 12 à 20 bits.
  8. Compteur d'essieux selon l'une des revendications 6 ou 7, caractérisé en ce que l'unité d'analyse (2) comprend une unité de calcul (5) montée à la suite du convertisseur analogique-numérique (4).
  9. Compteur d'essieux selon l'une des revendications 6 à 8, caractérisé en ce que l'unité de calcul (5) est connectée à une mémoire (6) et/ou à une interface utilisateur (7).
  10. Compteur d'essieux selon l'une des revendications 6 à 9, caractérisé en ce que l'unité de calcul (5) comprend un dispositif de segmentation temporelle (51) destiné à segmenter des signaux de temps discrets en blocs de signal temporels, ayant en particulier une longueur de bloc de 10 ms à 1500 ms.
  11. Compteur d'essieux selon l'une des revendications 6 à 10, caractérisé en ce que l'unité de calcul (5) comprend un analyseur de spectre (52) pour le calcul du spectre de fréquence des signaux de mesure et/ou une unité de calcul de l'énergie (53) pour le calcul de l'énergie des signaux, en particulier de l'énergie des signaux de courte durée.
  12. Compteur d'essieux selon la revendication 11, caractérisé en ce que l'unité de calcul (5) comprend un classificateur (54) disposé à la suite de l'analyseur de spectre (52) et/ou de l'unité de calcul de l'énergie (53) et qui classifie les schémas de données issus des résultats de calcul de l'analyseur de spectre (52) et/ou de l'unité de calcul de l'énergie (53).
  13. Compteur d'essieux selon la revendication 12, caractérisé en ce que le classificateur (54) peut fonctionner dans un mode d'apprentissage pour générer des modèles mathématiques pour certaines catégories de véhicules.
  14. Compteur d'essieux selon la revendication 13, caractérisé en ce que le mode d'apprentissage peut être réalisé en utilisant des algorithmes d'apprentissage.
  15. Compteur d'essieux selon la revendication 14, caractérisé en ce que l'algorithme d'apprentissage peut être réalisé en utilisant le procédé d'apprentissage hebbien, la règle de rétro-propagation ou l'algorithme Forward-Backward.
  16. Compteur d'essieux selon les revendications 12 à 15, caractérisé en ce que le classificateur (54) peut être réalisé dans un mode de reconnaissance en vue de l'association d'un signal de mesure actuel à une catégorie de véhicule.
  17. Compteur d'essieux selon l'une des revendications 12 à 16, caractérisé en ce que l'unité d'analyse (2) est suivie d'une unité de communication (8) à laquelle le résultat du calcul du classificateur (54) est transmis.
EP04450111A 2003-05-20 2004-05-19 Compteur d'essieux sans contact pour traffic routier Expired - Lifetime EP1480182B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AT7772003 2003-05-20
AT0077703A AT412595B (de) 2003-05-20 2003-05-20 Berührungsloses achszählsystem für den strassenverkehr

Publications (3)

Publication Number Publication Date
EP1480182A2 EP1480182A2 (fr) 2004-11-24
EP1480182A3 EP1480182A3 (fr) 2006-01-18
EP1480182B1 true EP1480182B1 (fr) 2007-06-06

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EP04450111A Expired - Lifetime EP1480182B1 (fr) 2003-05-20 2004-05-19 Compteur d'essieux sans contact pour traffic routier

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EP (1) EP1480182B1 (fr)
AT (2) AT412595B (fr)
DE (1) DE502004004010D1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102014012285A1 (de) 2014-08-22 2016-02-25 Jenoptik Robot Gmbh Verfahren und Achsenzähl-Vorrichtung zur berührungslosen Achsenzählung eines Fahrzeugs sowie Achsenzählsystem für den Straßenverkehr

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Publication number Priority date Publication date Assignee Title
DE102006054981B3 (de) * 2006-11-22 2008-06-19 Rtb Gmbh & Co. Kg Klassifikationseinrichtung für Fahrzeuge
DE102020124526B4 (de) * 2020-09-21 2025-11-20 Deutsches Zentrum für Luft- und Raumfahrt e.V. Verfahren und Vorrichtung zur Verkehrsüberwachung mittels Fahrbahnschwingungen und Laservibrometrie
DE102022102006B4 (de) 2022-01-28 2023-11-02 Deutsches Zentrum für Luft- und Raumfahrt e.V. Erfassen von Verkehrsaktivitäten mit Schwingungssensoren von einem Fahrzeug aus
CN114575206A (zh) * 2022-03-03 2022-06-03 中国测绘科学研究院 一种铁路轨道姿态测量方法、装置及作业系统

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US4789941A (en) * 1986-07-18 1988-12-06 Bennett Nunberg Computerized vehicle classification system
EP0612050B1 (fr) * 1993-02-19 1997-11-05 Mitsubishi Jukogyo Kabushiki Kaisha Système de détection de véhicules
US5528234A (en) * 1994-02-01 1996-06-18 Mani; Siva A. Traffic monitoring system for determining vehicle dimensions, speed, and class
US5821879A (en) * 1996-08-05 1998-10-13 Pacific Sierra Research Corp. Vehicle axle detector for roadways
KR100459475B1 (ko) * 2002-04-04 2004-12-03 엘지산전 주식회사 차종 판단 시스템 및 그 방법

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102014012285A1 (de) 2014-08-22 2016-02-25 Jenoptik Robot Gmbh Verfahren und Achsenzähl-Vorrichtung zur berührungslosen Achsenzählung eines Fahrzeugs sowie Achsenzählsystem für den Straßenverkehr

Also Published As

Publication number Publication date
ATA7772003A (de) 2004-09-15
AT412595B (de) 2005-04-25
EP1480182A3 (fr) 2006-01-18
DE502004004010D1 (de) 2007-07-19
ATE364213T1 (de) 2007-06-15
EP1480182A2 (fr) 2004-11-24

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