An Efficient Multiple-Loop Sensor Configuration Applicable for Undisciplined Traffic
01 Sep 2013-IEEE Transactions on Intelligent Transportation Systems (IEEE)-Vol. 14, Iss: 3, pp 1151-1161
TL;DR: The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability, and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined conditions.
Abstract: This paper presents an effective multiple-inductive-loop pattern suitable for heterogeneous and less lane-disciplined traffic and its performance evaluation. Vehicle detection system based on conventional inductive loops works well only for lane-based and homogeneous traffic. A multiple-loop system for sensing vehicles in a heterogeneous and less lane-disciplined condition has been reported recently. The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability. Each loop has a unique resonance frequency and the excitation source given to the loops is programmed to have frequency components covering all the loop resonance frequencies. When a vehicle goes over a loop, the corresponding inductance and resonance frequency will change. The shift in frequency or its effect in any/every loop can be simultaneously monitored, and the vehicles can be detected and identified as a bicycle, a motorcycle, a car, a bus, etc., based on the signature. Another advantage of this scheme is that the loops are in parallel resonance; hence, the power drawn from the source will be minimal. A prototype multiple-loop system has been built and tested based on the proposed scheme. The developed system detected, classified, and counted vehicles accurately. Moreover, the system also computes and provides the speed of the vehicle detected using a single set of multiple loops. The accuracy of the speed measurement has been compared with actual values and found to be accurate and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined (e.g., Indian) conditions.
Citations
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TL;DR: In this article, a novel intelligent transportation system (ITS) using the cellular network, GPS probes, and limited ITS infrastructure for edge-level speed estimation under heterogeneous traffic condition is proposed.
Abstract: This paper proposes a novel intelligent transportation system (ITS) using the cellular network, GPS probes, and limited ITS infrastructure for edge-level speed estimation under heterogeneous traffic condition. The erroneous vehicle position data taken from cellular network are processed in real time to compute edge level vehicle flow, space occupancy, and congestion with a mean error of less than 10%. For edge-level speed estimation, two models of ITS infrastructure deployment are proposed: the COngestion COverage MOdel (COCOMO) and the Edge COverage MOdel (ECOMO). The GPS Probes’ speed data are used to extrapolate speed estimations from an infrastructure edge to the associated infrastructureless edge(s). The infrastructure requirement of COCOMO is constant, whereas that of ECOMO depends upon diversity in the congestion profile of edges. The COCOMO and ECOMO permit edge-level speed estimation with the 90 percentile error of 10%–22% and 10%–13%, respectively. The communication and storage requirement of the proposed ITS and the utility of generated traffic information are analyzed.
31 citations
TL;DR: It is shown that some spectral features extracted from the Fourier Transform of inductive signatures do not depend on the vehicle speed, which is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops.
Abstract: Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.
30 citations
11 May 2015
TL;DR: This paper presents a simple digitizer suitable for differential variable inductive/reluctance sensors that uses a ratio-metric approach in the computation and hence the output is less sensitive to variation in the parameters.
Abstract: This paper presents a simple digitizer suitable for differential variable inductive/reluctance sensors. The proposed scheme uses two digital I/O pins, a counter and a comparator of a microcontroller and obtains a digital output directly proportional to the measurand which is sensed using a differential variable inductive/reluctance sensor possessing either a linear or an inverse transfer characteristic. The scheme uses a ratio-metric approach in the computation and hence the output is less sensitive to variation in the parameters such as excitation voltage, reference voltage, offset of the comparator, etc. A prototype of the proposed system has been built and tested using standard variable inductors that emulated a differential inductive sensor following an inverse characteristic. The output recorded was linear across the full range and worst-case error noted was less than 0.3 %. For the prototype developed, the time taken to complete a measurement was 200 µs. The prototype digitizer has been interfaced with a commercially available LVDT and tested. The worst-case error observed in this test was 0.77%. Also, the same digitizer has been employed to get a digital readout from a differential variable reluctance based displacement sensor. The worst-case error was less than 0.83%. The test results establish the efficacy of, the simple and cost effective, scheme developed.
27 citations
Cites background or methods from "An Efficient Multiple-Loop Sensor C..."
...A DSP [6] based scheme reported for differential variable reluctance transducers is such an example....
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...The developed digitizer is useful for differential variable reluctance transducers for displacement measurement [6], [16], inductive differential pressure transducers [8], etc....
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...Sensors with multiple coils/loops [6] are also in use....
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...IEEE I2MTC, Austin, Texas, May 2010, pp. 219-223,. [5] A. Flammini, D. Marioli, E. Sisinni and A. Taroni, “A multichannel DSP-based instrument for displacement measurement using differential variable reluctance transducer,” IEEE Trans....
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...Among the methods listed above, the DSP [6] and dual-slope [8] based schemes can be used to obtain a direct digital output from a differential reluctance/inductive sensor but they are complex and expensive....
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01 Jan 2016
TL;DR: This paper proposes a novel intelligent transportation system (ITS) using the cellular network, GPS probes, and limited ITS infrastructure for edge-level speed estimation under heterogeneous traffic condition.
26 citations
Cites methods from "An Efficient Multiple-Loop Sensor C..."
...4 based RF transceivers [9], acoustic sensors [10], multiple-inductive-loop sensor [11], and magnetic sensor system [12] are used in literature....
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TL;DR: In this article, a vehicle detection method in heterogeneous and lane-less traffic by extracting a binary image from a discrete sensor array is presented, which is formed with a logic 1 or 0, which are recorded based on the occupancy status of the vehicles in an observed zone.
Abstract: Nowadays, providing a low-cost traffic management system in developing countries or in heterogeneous and lane-less traffic conditions is highly essential. It can help to manage traffic congestion, save fuel, save travel time, and enhance user safety. By keeping these as an objective, this article presents a vehicle detection method in heterogeneous and lane-less traffic by extracting a binary image from a discrete sensor array. The binary image is formed with a logic 1 or 0, which are recorded based on the occupancy status of the vehicles in an observed zone. The proposed method is demonstrated with virtual loops in video and with an array of micro-LiDARs. The width and length of the vehicle are obtained from the binary image , which is extracted from virtual loops in a video recording and classified the vehicles. Similarly, the width and height information is obtained using an array of micro-LiDARs and classified the vehicles. The proposed method can easily be implemented with minimal storage, minimum cost, less bandwidth, and less computation complexity than the conventional methods, such as image processing or video processing-based vehicle classification. The proposed classification methods are mathematically derived, implemented, and measured performance over real traffic scenarios. It can be adopted automatically to high or light traffic scenarios by adjusting the distance between observation zones. The detection accuracy of 98% is observed while extracting data from video and 91.3% while using micro-LiDARs. The proposed works are compared with existing techniques.
19 citations
References
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TL;DR: Algorithm for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence is presented.
Abstract: This paper presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level, and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. We also briefly describe an interactive camera calibration tool that we have developed for recovering the camera parameters using features in the image selected by the user.
833 citations
"An Efficient Multiple-Loop Sensor C..." refers background in this paper
...However, the existing loop detectors are suited for lanedisciplined and homogeneous traffic [13]–[15]....
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25 Aug 2001
TL;DR: This study suggests that real-time speed and travel time estimates derived from single-loop detector data assuming a common g-factor for all detectors in the district can be in error by 50 percent, and so they are of little value to travelers.
Abstract: Presents the PeMS algorithms for the accurate, adaptive, real-time estimation of the g-factor and vehicle speeds from single-loop detector data. The estimates are validated by comparison with independent, direct measurements of the g-factor and vehicle speeds from 20 double-loop detectors on I-80 over a three-month period. The algorithm is used to process data from all freeways in Caltrans District 12 (Orange County, CA) over a 20-month period beginning January 1998. Analysis of those data shows that the g-factors for different loops in the district differ by as much as 100 percent, and the g-factor for the same loop can vary up to 50 percent over a 24-hour period. Many transportation districts now post real-time speed and travel time estimates on the World Wide Web. Those estimates often are derived from single-loop detector data assuming a common g-factor for all detectors in the district. This study suggests that those estimates can be in error by 50 percent, and so they are of little value to travelers. The use of the PeMS algorithm will reduce those errors.
194 citations
21 May 2001
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.
148 citations
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
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
Additional excerpts
...They can be also used to calculate the speed of the vehicle using the time taken for crossing a known distance [5] and to classify the vehicles based on the signatures using suitable algorithms [6]–[12]....
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