EP2790165A1 - Qualitätsbestimmung bei der Datenerfassung - Google Patents

Qualitätsbestimmung bei der Datenerfassung Download PDF

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Publication number
EP2790165A1
EP2790165A1 EP13162865.3A EP13162865A EP2790165A1 EP 2790165 A1 EP2790165 A1 EP 2790165A1 EP 13162865 A EP13162865 A EP 13162865A EP 2790165 A1 EP2790165 A1 EP 2790165A1
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Prior art keywords
detection signal
error
model
sensor
prediction
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English (en)
French (fr)
Inventor
Ralf Junghans
Karl Grüner
Kyandoghere Kyamakya
Alireza Fasih
Fadi Al Machot
Ahmad Haj Mosa
Mouhannad Ali
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SWARCO Traffic Systems GmbH
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SWARCO Traffic Systems GmbH
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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
    • G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • 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
    • G08G1/042—Detecting movement of traffic to be counted or controlled using inductive or magnetic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals

Definitions

  • the present invention relates to quality determination in data acquisition.
  • the present invention relates to a method, an apparatus and a computer program product for quality determination in data acquisition.
  • Inductive loops and other well-known data suppliers like video cameras with virtual loops, infrared sensors, radar sensors and the like are called traffic detectors or sensors, respectively, and are especially used in traffic control systems for delivering a good picture of the traffic situation based on the cross section and lane-related measurement principle of traffic parameters.
  • each vehicle driving over/through these physical/virtual sections on given lanes is recognized and causes the electronic detector component to generate a pulse for each vehicle presence in form of digital information.
  • inductive loop detectors generate digital information out of the analogue signal response.
  • the analogue and digital circuits of the detector component together with the connected loop-wire located in the road concrete/bitume may also be capable of using special parameters as measurement mechanism.
  • the obtained digital information is provided to the traffic control software within the intersection traffic controller and to any traffic monitoring system.
  • this problem is solved by picking up the digital pulse information directly at the sensor output.
  • a sensor reliability data model is trained offline to gather all significant characteristics of the sensor component, the physical object in the road surface/concrete, environmental influences, all this for enabling a reliable assessment of the data quality of the single sensor.
  • mutual interferences are also trained in the sensor reliability data model.
  • the resulting model or rather the set of models offers for the first time the possibility of monitoring (in real- or quasi real-time and proactively) both the current state and the evolution of sensor data quality especially for sensors delivering a digital pulse stream caused induced by the presence of vehicles in traffic.
  • the present invention presents a global and comprehensive concept for a robust "traffic detectors' quality management".
  • the overall concept relates to all traffic sensors types, independently of whether they are point-sensors or area-sensors.
  • sensors types are: loop detectors systems, video-based systems, radar-based systems (all forms of radars are meant: radio-wave based; infrared-light-based, light-based, sound-based), and (earth) magnet fields based systems.
  • a signal processing apparatus for processing at least one detection signal, the apparatus comprising:
  • a method for processing at least one detection signal comprising:
  • the method further comprises
  • a computer program product comprising computer-executable computer program code which, when the program is run on a computer (e.g. a computer of an apparatus according to any one of the aforementioned apparatus-related exemplary aspects of the present invention), is arranged to cause the computer to carry out the method according to any one of the aforementioned method-related exemplary aspects of the present invention.
  • a computer e.g. a computer of an apparatus according to any one of the aforementioned apparatus-related exemplary aspects of the present invention
  • Such computer program product may comprise or be embodied as a (tangible) computer-readable (storage) medium or the like on which the computer-executable computer program code is stored, and/or the program may be directly loadable into an internal memory of the computer or a processor thereof.
  • Fig. 1 is an overview of a scenario to which the present invention is applicable.
  • Fig. 1 shows an example of a crossroad that is equipped with various traffic lights and sensors.
  • reference signs TS1 to TS4 indicate traffic signal lights and reference signs D1 to D10 indicate inductive loop detectors.
  • reference sign TC denotes a traffic controller for traffic light signaling including a traffic control module 10. As shown in Fig. 1 , analogue signal from the various inductive loop detectors are input into the traffic control module 10.
  • Fig. 2 is a block diagram illustrating the traffic control module 10 shown in Fig. 1 .
  • the traffic control module 10 comprises a detection module 11, a RoSiT (Robust Sensors in Traffic) module 20 and traffic programming module 12.
  • analogue signals from the inductive loop detectors D1 to D10 are input to the detection module 11 and the detection module 11 outputs digitized signals to the RoSiT module 20. Further, the RoSiT module 20 outputs an indication regarding loop data quality to the traffic programming module 12.
  • the RoSiT module 20 comprises a pattern recognition module 21, an environmental influences module 22, an error classification module 23, a quality assessment and forecast module 24, an error correction module 25, a detector parameter recommendation module 26, and a truck detection module 27.
  • the pattern recognition module 21, the environmental influences module 22, the error classification module 23, the error correction module 25, the detector parameter recommendation module 26, and the truck detection module 27 each input data to the quality assessment and forecast module 24, which outputs loop data quality information to the traffic programmers module 12.
  • the present invention comprises the following aspects. Each of the below aspects will be described in more details later on.
  • the first aspect concerns the from and granularity of the detector's signal raw data observation level. That is, according to a first aspect, solely microscopic detector data is used for quality management.
  • a third aspect deals with the issue of how to differentiate and classify the error types observed. That is, according to the third aspect, in case of errors/faults presence, these faults are classified. In this regard, it is proposed to robustly classify and identify OCCURING error/faults types, the error types depending on the underlying sensor type.
  • the type of vehicle observed is classified. That is, from the pattern collected out of non-erroneous data, the presence of either a car or a truck is classified (this classification is independent of the sensor type).
  • a fifth aspect concerns the issue of how to perform erroneous detector data correction, whenever possible.
  • the fifth aspect while involving sensor type related error types knowledge, sensor topology, environmental information, and observed error types, the errors are robustly corrected and a cleaned data stream is generated.
  • the sixth aspect relates to how to build a black box model of the error rate related detector's behavior in dependence of both environmental information (weather and temperature) and traffic volume.
  • the seventh aspect it is described how to perform short, middle and long term detector error rate performance prediction while involving the black-box model. That is, based on archived pattern history and environmental conditions information we do predict detector faults rate for short, middle and long term. This does also give indication about the remaining detector life time.
  • An eighth aspect deals with how to either automatically tune or recommend contextually appropriate detector 'sensitivity' values and/or 'measurement time' values and/or other equivalent or relevant settings in order to minimize detector's error rate performance.
  • a detector tuning recommendation that takes environmental information and a detector reliability physics model into consideration
  • the recommendation concerns measurement time and/or sensitivity level, and/or other equivalent detector settings.
  • the processing related to all of the above described 8 aspects is performed in real-time and does reach a detection rate above 96% according to very extensive tests that did involve real field data.
  • microscopic level it is referred to the level at which the detector raw data have been transformed into an information about presence or non-presence of a vehicle on the road.
  • the atomic information (or atomic data stream) considered in the present invention and on which all further processing steps are based is that about
  • the first step in the global detectors quality management is to transform the detector raw data into an "atomic data stream".
  • a pre-processing module for generating/guessing/synthesizing the related 'atomic data stream' can be designed.
  • Such a module to be calibrated once by real sample observations may eventually contain some form of stochastic intelligence.
  • the atomic data stream is to be observed and analyzed in order to determine whether it contains errors or not. In this point, the overall methodology of this determination is presented.
  • the output of this processing is information-related to the presence or not of errors in the observed atomic data stream. Either a single cross-section or a set of consecutive cross-sections is/are considered simultaneously depending on their logical interdependency as determined/fixed by the underlying sensor network topology.
  • a reference clean or errorless data stream of appropriate significant length is collected or generated. This corresponds to an observation from typical portions from several typical hours up to days.
  • the collected reference data stream is transformed into various images of different sizes (using sliding windows of different sizes) while involving the logical-topological information.
  • the different images are transformed though nonlinear processing (radon transformation and CNN processing) out of which a set of reference classes is determined and saved.
  • the discrete time CNN classification allows a comparison with the references classes.
  • Portions of the "atomic data stream" containing errors are identified in and marked as such.
  • marked atomic data stream portions are processed and errors pattern are differentiated and then clustered.
  • a semi-supervised labeling involving for example rough-set theory is then performed offline by a human expert. This labeling depends on the underlying sensor type and is done once. For loop detectors, for example and just for illustrative purposes, the labeling will differentiate 6 error classes: weak-signal, pulse break-up, chattering, splash-over, over-counting and under-counting.
  • the error classes are of course not limited to the above mentioned 6 error classes.
  • the labeling will depend on their inherent physics and on the sensor network topological information.
  • the offline phase 1 is used to record reference "error containing data streams or data stream pairs" of different lengths while taking the following into consideration: reference logical-topological contexts, weather and environmental conditions and sensor type or sensor types combinations.
  • a clustering of the different error types is performed by involving for example rough-set theory.
  • a human expert is used once in order to label the different types/classes of errors observed/identified/known. The latter is done once for every sensor type or sensors type combinations.
  • corresponding signatures are fixed/extracted by using either a nonlinear feature extractor like CNN or by a statistical measure performed over sliding windows.
  • the signatures related to each error type are saved for further use in the online phase.
  • This OFFLINE phase is performed once and the output is the set of signatures for all relevant error types for a given sensor type or sensor types combination. These signatures will then be used in phase 2 (ONLINE) in order to detect/ classify errors (types).
  • the phase 2 is an online process. It assumes that the signatures of all possible errors are known (while considering related logical-topological and weather + environment information).
  • CNN CNN based, SVM based, or other
  • Phase 1 of Aspect 3 has been done once and thus has already provided the signatures of all possible errors, to skip all steps of Aspect 2 coming after Aspect 2/Phase2/(1).
  • the online data streams of different lengths would then be collected from here and processed for error classification.
  • Aspect 2 is merely de facto skipped and considered or integrated as an offline part of Aspect 3.
  • This Online phase 2 can be directly performed on the online data-stream while skipping most of the online phase (Phase 2) of Aspect 2 as explained in the note "Alternative to (3)".
  • Clean portions of the "atomic data stream" (i.e. not containing errors), originated from a single detector system, are processed in order to classify the type of vehicle observed.
  • This classification relates to vehicle length and respective headways as sole differentiating features. Thus, it is mainly differentiated between trucks and cars. Further differentiations are possible so far they are related to vehicle length.
  • the main classification is based on the features sets constituted by the presence data pulse lengths and the related distribution of the respective headways.
  • a shock wave pattern is then extracted and involved in a support vector machine based classification.
  • the classification output is whether the vehicle is a car or a truck, and whether it has been in movement or stopping, the latter for example due to red traffic light phase.
  • This phase 1 is an offline process. It is done once and intends to produce the signatures of a truck presence within the atomic data streams while taking related context information into consideration.
  • the reference data streams involved here must be error free.
  • the obtained various signatures of the truck's presence in a traffic stream are then saved for use in the online truck detection process.
  • This offline phase results in a set of signatures indicating the presence and the position of a truck within a traffic stream from different perspectives.
  • This phase 2 is an online process.
  • the online data streams are assumed to be error free. If they are not error free, the stream should be taken after error correction or the portions containing errors should be skipped for the truck detection.
  • a CNN based classifier is preferred due to the high performance. Other classifiers can however also been used.
  • This online phase detects the presence of a truck within a traffic stream and gives also the exact position.
  • the atomic 'presence pulse' corresponding to a truck will be identified.
  • Error correction is based on a reasoning based on some prior knowledge formulated logically by a human expert and that depends on error classes related to a given detector type and on fixed sensor network topological configurations.
  • the topological information is related to how detectors lay as a group in a common cross-section or are situated with a given spatial separation within a direct longitudinal neighborhood.
  • the prior knowledge that expresses the essence of the rules for the real-time, logic-programming-based reasoning correction is based on a plausibility matrix fixing the possible either simultaneous or consecutive error occurrences depending on the underlying topology and while considering the detector-type related variety of error classes.
  • the output of the correction process is, for each given error (detected by the concept of Aspect 3), that:
  • This phase 1 is an offline process.
  • Aspect 2 and Aspect 3 knowledge from Aspect 2 and Aspect 3 is used by a human expert to formulate logic-programming rules that are then integrated in a logic-programming reasoner to be used in real-time for error correction.
  • the reasoner will involve an appropriate ontology that includes the context information indicated in Aspect 2: logical-topology, weather and environment, data streams lengths, error types, etc.
  • the ontology and the reasoner will be both designed during this offline phase.
  • ASP Answer Set Programming
  • This phase 2 is an online process.
  • the reasoner will determine whether the error can be corrected. If yes, it will correct it. If not it will either state a default recommendation or state that neither default recommendation nor correction is possible due to lack of information.
  • Each default recommendation should be endued with a probability value. This latter value is obtained offline as explained in Phase (1) of Aspect 5.
  • the reasoner will preferably involve a solver based on ASP (Answer Set Programming) to ensure a fast speed under real-time constraints.
  • ASP Automatic Set Programming
  • This Online phase involves the use of an ultrafast logic-programming reasoner for online error corrections and/or for default recommendations for cases a sure correction is not possible.
  • a black-box model of the detector's performance is constructed with the objective of probabilistically describing the error rate related detector's behavior. In the essence, the intention is to determine the detector's reliability physics in dependence of both internal and external parameters.
  • Internal detector parameters are different settings determining and/or related to its core functioning principle. External parameters are traffic volume, detector's network topology, weather and environmental related ones.
  • Potential/candidate internal parameters are for example: sensor sensitivity level, measurement time level, etc. These parameters may differ depending on the specific sensor system type.
  • Potential/candidates for external parameters are: weather, temperature, traffic level, proportion of trucks in the traffic, etc.
  • a robust detectors' quality management requires the capability to predict the detector's reliability in three time horizons: short term (range of some minutes up to one hour); middle term (range of some hours, up to some days, and up to a week); long term (range of several weeks, up to several months, and up to some years).
  • the short term prediction is of relevance for managing the impact of detector quality on traffic management.
  • the middle term prediction is mainly of relevance for short term traffic infrastructure maintenance activities.
  • the long term prediction is mainly of relevance to predict the operationally acceptable lifetime of a given detector or detector group.
  • the prediction does involve the prior knowledge that is compressed in the black-box model obtained in Aspect 6. Further, for both short-term and middle term detector's-performance prediction, a concept involving mainly hidden markov models and the black-box model is used. For both short and middle term, all external parameters are either known or can be easily predicted (e.g. traffic volume) or obtained from external sources (e.g. weather information).
  • the available detector's data history to be involved in the long-term prediction should be at least two times longer than the future time-frame ahead for prediction. For example, to predict for the next three weeks (or months), the available observed history should be longer than 6 weeks (or months). Thus, the appropriate level of data aggregation should be selected in a way that the available history should satisfy this length requirement.
  • the envelope's prediction involves two major steps: a) time series filtering through a quadratic trend estimation and a consecutive smoothing; and b) estimate a cubic trend of the later-resulting time series and use it for future prediction.
  • Step 1 Offline Short Term Prediction
  • this process is aiming at predicting the error rate in order to evaluate the SHORT TERM performance of the sensor system.
  • the aggregation time unit is hours; one predict for the next couple of hours (1 to 4 hours).
  • the black box reliability physics model is taken unto consideration.
  • the short term prediction considers the history of error rates associated with the conditions (weather, temperature and traffic volume).
  • State space models stocastic and probabilistic are proposed for this prediction. For instance, hidden Markov model (HMM), Bayesian network and Kalman filter.
  • the sensor system internal parameter tuning control system (see Aspect 8) will need the prediction result as one of the inputs if a tuning assessment for the corresponding time horizon is needed.
  • this process is aiming at predicting the error rate in order to evaluate the MIDDLE TERM performance of the sensor.
  • the aggregation time unit is days and weeks. According to these aggregations, the error rate time series is smoother than in short term, and the environmental conditions have due to the aggregation a relatively smaller impact on error rates.
  • the proposed middle term prediction uses a stochastic state space model (e.g. Auto Regression Moving Average Model, Local Trend Model, etc.).
  • the sensor system internal parameter tuning control system (see Aspect 8) will need the prediction result as one of the inputs if a tuning assessment for the corresponding time horizon is needed.
  • this process is aiming at LONG TERM predicting the error rate in order to estimate amongst others the lifetime of a given sensor.
  • This threshold should be sensor system type specific. If for example an average of 10% error rate (@ 500 vehicles) is no more acceptable, then it will be fixed as a threshold on which basis the potential remaining life time of a sensor can be estimated based on the long term prediction functionality.
  • a future 'time horizon' of 3 months and more is taken, for example.
  • the Long term prediction model estimates the trend of the worst cases of error rates. It involves similar performance worst cases observed in the recent related history of the historical data.
  • This online process enables a future prediction of the sensor system performance. It allows to know in advance whether the sensor system may reach performance regions that are unacceptable.
  • the reliability physics model is an input used to extensively train the new sensor system internal parameter tuning control model of Fig. 4 .
  • the tuning can be calculated for the current time or for a future time interval. Respective related inputs will be then needed.
  • This online sensor system tuning model controller is in essence a REASONER that builds on knowledge from the sensor system reliability physics model. It ensures an adaptivity of the sensor system to keep the error rates performance the lowest possible at any moment.
  • a point sensor is typically stationed/placed at a fixed location along/on/under a roadway and watches (i.e. counts, records, classify, etc.) vehicles passing at this particular location in the time domain.
  • the fixed location may be:
  • point sensors are: loop detectors, radars, ultrasonic radars, Lidars (light detection and ranging) and video cameras.
  • area sensors are: aerial photography or satellite imagery, and mobile sensors such as automatic vehicle location and global positioning system.
  • the inductive-loop detector is the most utilized sensor in a traffic management system.
  • the principal components of an inductive-loop detector are:
  • Cars passing over or that stop within the detection area of an inductive-loop detector do decrease the inductance of the loop.
  • the electronics unit senses this event as a decrease in frequency and sends a pulse to the controller signifying the passage or presence of a vehicle.
  • Video-based traffic sensor systems :
  • Video-based detectors use advanced image processing schemes running on an appropriate microprocessor to analyze a video image input. Different approaches are used by video detection sensors. Some analyze the video image of a target area on the pavement. A change in the image of the target area as a vehicle passes through the target area is analyzed. Another approach does rather identify when a target vehicle enters the camera field of view and tracks the target vehicle through this field of view. Finally, other video sensors use a combination of these two approaches.
  • a radar operates according to the following principle: " The radar dish or antenna transmits pulses of radio waves or microwaves which bounce off any object in their path. The object returns a tiny part of the wave's energy to a dish or antenna which is usually located at the same site as the transmitter" (Source: http://en.wikipedia.org/wiki/Radar; latest access: Sept. 10th, 2012 ). Through an appropriate processing of the reflected energy the radar can be used to determine presence, range, altitude, speed and direction of targeted objects. In traffic detection, the radar is used as a point sensor which can detect both presence and speed of a vehicle in a fixed sport (or cross-section) on the road.
  • detector's signal raw data is meant the time series generated by a point detector. It is constituted of the succession of vehicle presence detection pulses. The time series contains beginnings and ends of the successive pulses, which are separated by so-called time headways. An area sensor will generate simultaneously many of such time series for fixed areas or locations.
  • a black box model can be defined as follows.
  • a black box is a module with known inputs, known outputs, a known function but with an unknown internal mechanism.
  • a black box module a) acts predictably, b) can be used without knowledge of its internal details, c) hides information from the rest of the system.
  • a black box is defined by "what" it does and not by “how” it does it.
  • For building a black box model on has to start from measurements of the behavior of the system and the related external influences (inputs to the system) and try to determine a mathematical relation between them without going into the details of what is actually happening inside the system.
  • Atomic information refers to refers to a minimal amount of information which makes it possible to distinguish two particular items. Atomic information is mono valuate and does refer to only one thing. Failure to capture atomic information means inaccurate, unreliable or missing data.
  • a location is a generally rectangular area on the lane surface of approx. 1m x 2m of The selection of these positions serve different needs, for example a) provide a trigger or parameter on which a vehicle actuated traffic management system is based for extending the current green time or for giving green to a particular approach; b) measure queue length; etc.
  • HMM is defined as follows:
  • HMM Hidden Markov Model
  • Bayesian network (or - other naming of the same: Bayes network, belief network, Bayes(ian) model, probabilistic directed acyclic graphical model) is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph.
  • a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases (Source: Wikipedia).
  • Bayesian nets are a network-based framework for representing and analyzing models involving uncertainty. They are different from other knowledge-based systems concepts because uncertainty is handled in mathematically rigorous yet efficient and simple way.
  • the general probabilistic inference problem here is in fact to find the probability of an event given a set of evidences. This can be done in Bayesian nets with sequential applications of Bayes Theorem.
  • Support vector machines are concepts used for classification. They belong to the class of supervised learning models with associated learning algorithms that analyze data and recognize patterns. The fundamental SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output. Thus, SVM is a non-probabilistic binary linear classifier.
  • an SVM training algorithm Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other.
  • An SVM model is a representation of provided examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
  • SVMs can efficiently perform non-linear classification using what is called a kernel function, and thereby implicitly mapping their inputs into high-dimensional feature spaces.
  • Fuzzy logic is an approach to computing based on "degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based. Fuzzy logic includes 0 and 1 as extreme cases of truth (or “the state of matters” or “fact”) but also includes the various states of truth in between (i.e. very unlikely' (gray areas of probability)). Software based on application of fuzzy-logic (as compared with that based on Formal Logic) allows computers to mimic human reasoning more closely, so that decisions can be made with incomplete or uncertain data.
  • a fuzzy inference system is a system that uses fuzzy set theory to map inputs (features in the case of fuzzy classification) to outputs (classes in the case of fuzzy classification).
  • a fuzzy inference is an extended form of formal inferences that enables qualitative and/or quantitative evaluation of the degree of confidential level for a given causality on the basis of fuzzy expressions.
  • the presence detection of a vehicle at a fixed location is generally expressed at the detector/sensor output in form of a quadratic pulse in time.
  • the width of this quadratic pulse does generally correlate with the speed of the vehicle which presence is detected.
  • a fast vehicle will generate a pulse with a smaller width; a long vehicle like a truck will result in a longer pulse width.
  • the generated 'presence pulse' (from a given sensor/detector) is named "weak signal” if its width is too small/short and thereby does significantly deviates from what should be expected normally (i.e. according to the current context related to maximum speed, current speed and average vehicle length).
  • pulse breakup if instead of one pulse, two consecutive pulses are generated whereby one or both of the following situations are additionally true:
  • the generated 'presence pulse' or rather 'pulse-series' is named "chattering" if instead of one pulse, multiple (more than three) consecutive pulses are generated whereby both of the following situations are additionally true:
  • the generated 'presence pulse' (from a given sensor/detector) is named “splash-over" if both of the following are true:
  • the sensor/detector does generate a single presence pulse for 2 or more consecutive vehicles passing by.
  • the resulting pulse length will be higher than normal and an undercounting of the effective number of vehicles will be a further consequence. This generally happens when the headways between slowly moving vehicles is relatively small, as it is for example the case during traffic jams.
  • Rough set theory is a relatively new mathematical tool for imperfect data analysis.
  • the foundation of the rough set philosophy is the assumption that to every object of the universe of discourse some information (data, knowledge) is associated. Objects characterized by the same information are indiscernible (similar) in view of the available information about them.
  • the indiscernibility relation generated in this way is the mathematical basis of rough set theory.
  • Rough set theory can be approached as an extension of the classical set theory, for use when representing incomplete knowledge.
  • Rough sets can be considered as sets with fuzzy boundaries; that is, sets that cannot be precisely characterized using the available set of attributes.
  • the basic concept of the rough set theory is the notion of approximation space.
  • the signal considered is the one formed by the succession of the different consecutive presence pulses over time over a given location at a cross-section.
  • Traffic waves also called stop waves or traffic shocks, are travelling disturbances in the distribution of cars on a highway. Traffic waves usually travel backwards in relation to the motion of the cars themselves, or "upstream”.
  • the shock wave pattern is a particular distribution of both consecutive pulse-lengths and headways that do reflect the existence of a traffic wave in the underlying traffic stream.
  • a method for filtering a time series data is a tool to clean as much noise from it as possible as the data should be made compatible for further analysis.
  • T t ⁇ 0 + ⁇ 1 ⁇ t + ⁇ 2 ⁇ t 2 + ... + ⁇ p ⁇ t p
  • p is a positive integer.
  • the coefficients ⁇ i are obtained from a regression process over the underlying time series.
  • a sensor's sensitivity indicates how much the sensor's output changes when the measured quantity changes. For many sensor systems, the possibility exists of varying or setting the sensitivity, either manually or automatically.
  • a high measurement time will result in a lower measurement update frequency.
  • traffic sensing microscopic data refer to single-vehicle related data. In this case here, these will be the time series of individual vehicle presence pulses generated by the traffic detectors.
  • macroscopic data refer to an aggregation of single-vehicle related data. In this case here, these will be the time series of aggregated numbers of individual vehicle presences per time unit, for example per hour.
  • the macroscopic quantities are the flow rate (the number of cars passing a cross section per unit of time, q), the density (the number of cars per unit of distance, k), and the space mean speed.
  • characteristics of individual vehicles can be obtained for cross sections. Then the characteristics of the distributions of the measured data can be examined (mesoscopic traffic flow characteristics) which also differ by flow condition. For example, the distributions of time headways are well studied because they can, for instance, be used to estimate capacity.
  • a pattern is a particular signature that is expressed by 'elementary features' of an object.
  • the object is the time series of consecutive detector pulse signals.
  • the 'elementary features' are for example a part or all microscopic data attributes or a combination of them.
  • a pattern is defined by the common denominator among the multiple instances of an entity.
  • the pattern of relevance here are for example the different faulty signal types.
  • detector systems do offer the possibility of setting, manually or dynamically/automatically two key parameters of the sensor system or their related equivalent: sensitivity, measurement time, and eventually some more, depending on the specific sensor system.
  • Tuning refers to setting (manually or dynamically or a recommendation) these key parameters in ranges that put the sensor systems in the best sensing conditions resulting in the best sensor performance and consequently in the lowest error/faults rate.
  • the classification error is a measure of the misclassification rate or proportions.
  • a measurement error is the difference between the true value of a measurement and the value obtained during the measurement process.
  • a classification error is a type of measurement error by which the respondent does not provide a true response to a survey item. This can occur in one of two ways: a false negative response or a false positive response. A 'false negative response' corresponds to when the measurement system indicates that an event did not occur although it did.
  • a sensor reliability data model that is trained offline to gather all significant characteristics of the sensor component, the physical object in the road surface/concrete, environmental influences, all this for enabling a reliable assessment of the data quality of the single sensor.
  • the topological information including lane direction, signaling and the relative position of the sensors to each other at a complete intersection, mutual interferences are also trained in the sensor reliability data model.
  • the resulting model or rather the set of models offers for the first time the possibility of monitoring (in real- or quasi real-time and proactively) both the current state and the evolution of sensor data quality especially for sensors delivering a digital pulse stream caused induced by the presence of vehicles in traffic.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102015203115A1 (de) * 2015-02-20 2016-09-08 Siemens Aktiengesellschaft Ermitteln von fehlerhaften Konfigurationen von Detektoren und Signalen von Lichtsignalanlagen
CN107067114A (zh) * 2017-04-20 2017-08-18 桂林电子科技大学 基于回答集程序的调机路径规划方法
WO2020132134A1 (en) * 2018-12-18 2020-06-25 Bonny Banerjee Device and for environment state estimation and prediction
GB2593482A (en) 2020-03-24 2021-09-29 Daimler Ag A method for associating a traffic light detection for an at least partially autonomous motor vehicle, as well as an assistance system
EP3937151A1 (de) * 2020-07-08 2022-01-12 Siemens AG Österreich Vorrichtung und verfahren zur steuerung eines verkehrsflusses in einem verkehrsnetz durch einen optimalen signalphasenplan
CN114036990A (zh) * 2022-01-06 2022-02-11 青岛鼎信通讯股份有限公司 一种低压台区拓扑信号模糊增强方法
US11869358B2 (en) 2021-10-29 2024-01-09 Nortak Software Ltd. System and method for warning of a presence of a mobile target

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020154032A1 (en) * 1996-12-03 2002-10-24 Inductive Signature Technologies, Inc. Automotive vehicle classification and identification by inductive signature
US20040252193A1 (en) * 2003-06-12 2004-12-16 Higgins Bruce E. Automated traffic violation monitoring and reporting system with combined video and still-image data
AU2009236035A1 (en) * 2008-11-24 2010-06-10 Intelematics Australia Pty Ltd Method for Dynamically Integrating Traffic Data
US20120161987A1 (en) * 2010-12-23 2012-06-28 Electronics And Telecommunications Research Institute Apparatus and method for detecting vehicles

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020154032A1 (en) * 1996-12-03 2002-10-24 Inductive Signature Technologies, Inc. Automotive vehicle classification and identification by inductive signature
US20040252193A1 (en) * 2003-06-12 2004-12-16 Higgins Bruce E. Automated traffic violation monitoring and reporting system with combined video and still-image data
AU2009236035A1 (en) * 2008-11-24 2010-06-10 Intelematics Australia Pty Ltd Method for Dynamically Integrating Traffic Data
US20120161987A1 (en) * 2010-12-23 2012-06-28 Electronics And Telecommunications Research Institute Apparatus and method for detecting vehicles

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LAWRENCE R. RABINER: "A tutorial on Hidden Markov Models and selected applications in speech recognition", PROCEEDINGS OF THE IEEE, vol. 77, no. 2, February 1989 (1989-02-01), pages 257 - 286

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102015203115A1 (de) * 2015-02-20 2016-09-08 Siemens Aktiengesellschaft Ermitteln von fehlerhaften Konfigurationen von Detektoren und Signalen von Lichtsignalanlagen
DE102015203115B4 (de) 2015-02-20 2022-06-15 Yunex Gmbh Ermitteln von fehlerhaften Konfigurationen von Detektoren und Signalen von Lichtsignalanlagen
CN107067114A (zh) * 2017-04-20 2017-08-18 桂林电子科技大学 基于回答集程序的调机路径规划方法
CN107067114B (zh) * 2017-04-20 2020-07-28 桂林电子科技大学 基于回答集程序的调机路径规划方法
WO2020132134A1 (en) * 2018-12-18 2020-06-25 Bonny Banerjee Device and for environment state estimation and prediction
GB2593482A (en) 2020-03-24 2021-09-29 Daimler Ag A method for associating a traffic light detection for an at least partially autonomous motor vehicle, as well as an assistance system
EP3937151A1 (de) * 2020-07-08 2022-01-12 Siemens AG Österreich Vorrichtung und verfahren zur steuerung eines verkehrsflusses in einem verkehrsnetz durch einen optimalen signalphasenplan
US11869358B2 (en) 2021-10-29 2024-01-09 Nortak Software Ltd. System and method for warning of a presence of a mobile target
CN114036990A (zh) * 2022-01-06 2022-02-11 青岛鼎信通讯股份有限公司 一种低压台区拓扑信号模糊增强方法

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