A vehicle classification based on inductive loop detectors
21 May 2001-Vol. 1, pp 460-464
TL;DR: In this article, the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes is discussed, and the case of extremely short loop (10 cm) which allows detection of the number of axles is also analyzed.
Abstract: The class of vehicle is one of more important parameters in the process of road traffic measurement. Up to now, strip piezoelectric sensors and video systems have been used. The use of very cheap inductive loop detectors for vehicle classification is also possible. Such vehicle classification systems are based on magnetic profiles recorded from inductive loops. The magnetic profile is sensitive to the loop dimensions. This paper presents a discussion concerning the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes. As characteristics describing the magnetic profile of the vehicle have been used: magnetic profiles in time domain (normalized in amplitude), probability density function and magnetic profiles in vehicle length domain. For real time applications, the conversion of the measured signal into a vector of numerical parameters (a few only) is also proposed. The influence of loop dimensions on a chosen signal parameter was investigated. The case of extremely short loop (10 cm), which allows detection of the number of axles, was also analyzed.
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Journal Article•
TL;DR: A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor, allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.
Abstract: This report describes the prototype design, development, analysis and performance of a traffic surveillance system that is based on wireless sensor networks. Vehicle classification and reidentification schemes for low-cost, low-power platforms with limited computation resources were developed and tested. Both acoustic and magnetic sensors were tested. A robust real-time vehicle detection algorithm for both sensors was developed, and the magnetic sensor was shown to be superior to the acoustic sensor. The detection accuracy was shown to be comparable to that of inductive loop detectors while also having a much higher configuration flexibility, thus allowing the wireless sensor network system to be scalable and deployable everywhere in the traffic networks.
186 citations
TL;DR: This research refines unconventional techniques for estimating speed at a single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector's measurements.
Abstract: Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector's measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.
137 citations
Cites methods from "A vehicle classification based on i..."
...There have also been efforts to use new loop detector sensors to measure the inductive vehicle signature for vehicle classification, e.g., Reijmers (1979) and Gajda et al (2001)....
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TL;DR: A novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads is presented.
Abstract: This paper presents a novel inductive loop sensor that can detect vehicles under a heterogeneous and less-lane-disciplined traffic and thus can be used to support a traffic control management system in optimizing the best use of existing roads. The loop sensor proposed in this paper detects large (e.g., bus) as well as small (e.g., bicycle) vehicles occupying any available space in the roadway, which is the main requirement for sensing heterogeneous and lane-less traffic. To accomplish the sensing of large as well as small vehicles, a multiple loop system with a new inductive loop sensor structure is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle, motor cycle, scooter, car, and bus but also enables accurate counting of the number of vehicles even in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed and tested. Field tests indicate that the prototype successfully detected all types of vehicles and counted, correctly, the number of each type of vehicles. Thus, the suitability of the proposed sensor system for any type of traffic has been established.
92 citations
Cites background from "A vehicle classification based on i..."
...Research papers discussing improvement of loop detectors for better speed measurement [8], [9] and classification [10]–[16] for lane-disciplined traffic conditions were reported....
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TL;DR: A new algorithm for ILD using back-propagation neural networks is suggested to improve the vehicle-classification accuracy compared to the conventional method based on ILD.
Abstract: Vehicle class is an important parameter in the process of road-traffic measurement. Currently, inductive-loop detectors (ILD) and image sensors are rarely used for vehicle classification because of their low accuracy. To improve the accuracy, the authors suggest a new algorithm for ILD using back-propagation neural networks. In the developed algorithm, the inputs to the neural networks are the variation rate of frequency and frequency waveform. The output is five classified vehicles. The developed algorithm was assessed at test sites, and the recognition rate was 91.5%. The results verified that the proposed algorithm improves the vehicle-classification accuracy compared to the conventional method based on ILD
91 citations
Cites background from "A vehicle classification based on i..."
...Such a signal is known as the magnetic profile of the vehicle and is the base of the vehicleclassification data-processing algorithm [1]....
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...Vehicle class is important for access control (areas closed for some vehicle classes, limits of velocity, and weight for different vehicle classes), statistic purposes, and weighing of vehicles in motion [1]–[3]....
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TL;DR: A new model that uses an error-filtering algorithm to improve the accuracy of speed measurements and it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy.
Abstract: Vehicle speed is an important parameter in measurements of road traffic. At present, double-loop detectors are generally used for vehicular speed measurement. However, these detectors incur errors caused by scanning time, spacing between double loops, irregular vehicle trajectories, and the presence of multiple vehicles in the detection zone. This paper suggests a new model that uses an error-filtering algorithm to improve the accuracy of speed measurements. In the field tests, all percent errors of the vehicular speeds measured by the proposed model were within the error tolerance limit (plusmn5%). Furthermore, the variance of percent errors was reduced. Therefore, it can be concluded that the proposed model significantly improves vehicle-speed-measuring accuracy
89 citations
References
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TL;DR: This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem with the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands.
Abstract: The vehicle reidentification problem is the task of matching a vehicle detected at one location with the same vehicle detected at another location from a feasible set of candidate vehicles detected at the other location. This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem. Lexicographic optimization is a preemptive multi-objective formulation, and this lexicographic optimization formulation combines lexicographic goal programming, classification, and Bayesian analysis techniques. The solution of the vehicle reidentification problem has the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands. Implementation of this approach using conventional surveillance infrastructure permits the development of new algorithms for ATMIS (Advanced Transportation Management and Information Systems). Freeway inductive loop data from SR-24 in Lafayette, California, demonstrates that robust results can be obtained under different traffic flow conditions.
145 citations
01 Jan 1993
TL;DR: In this article, the authors describe traffic control systems for freeways which use section-related traffic variable measurement techniques, where the feature sequence of a vehicle series of one measurement site is correlated with the feature sequences of the adjacent measurement sites.
Abstract: This paper describes traffic control systems for freeways which use section-related traffic variable measurement techniques. To do this, the feature sequence of a vehicle series of one measurement site is correlated with the feature sequence of the adjacent measurement site. The evaluation unit for feature extraction is implemented on a VME-bus master CPU. Field experience reported from a traffic control system on the freeway B14 between Fellbach and Stuttgart show a more reliable and quicker automatic incident detection in comparison with local measurement techniques.
47 citations
01 Jan 1993
TL;DR: Practical experiences which are reported from a traffic control system on the freeway B14 between Fellbach and Stuttgart show a more reliable and quicker automatic incident detection in comparison with local measurement techniques.
Abstract: Traffic control systems for freeways are described which use section-related traffic variable measurement techniques. For deriving section-related variables feature sequence of a vehicle series of one measurement site is correlated with the feature sequence of the adjacent measurement site. As features signature characteristics of the inductive loop detuning curve are used. The evaluation unit for feature extraction is implemented on a VME-bus master CPU. Practical experiences which are reported from a traffic control system on the freeway B14 between Fellbach and Stuttgart show a more reliable and quicker automatic incident detection in comparison with local measurement techniques.
38 citations