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Proceedings ArticleDOI

A simple multiple loop sensor configuration for vehicle detection in an undisciplined traffic

TL;DR: In this article, an inductive loop vehicle detection system suitable for heterogeneous and less-lane disciplined traffic is presented. But it works well only for lane based and homogeneous traffic.
Abstract: This paper presents an inductive loop vehicle detection system suitable for heterogeneous and less-lane disciplined traffic. Vehicle detection system based on conventional inductive loop principle has been in use but works well only for lane based and homogeneous traffic. A multiple loop system that is suitable for sensing vehicles in a heterogeneous and less-lane disciplined condition has been reported recently. This paper proposes a new measurement scheme for the multiple loop system. According to the new scheme, all the inductive loops are connected in series and only two cables are required, instead of two per each loop, between the measurement unit and multiple loop system, there by reduces the system complexity. Each loop has a unique resonance frequency and the excitation given to the loops connected in series is programmed to have frequency components covering all the resonance frequencies of the loops. When a vehicle goes over a loop the corresponding inductance and resonance frequency will change. The shift in frequency or its effect for individual loops can be monitored simultaneously and the vehicles can be sensed and identified as bicycle, motor-cycle, Car, Bus, etc. A prototype multiple loop system has been built and tested based on the proposed measurement scheme. The system developed sensed, classified and counted the vehicles accurately.
Citations
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Journal ArticleDOI
TL;DR: Radio Frequency Identification (RFID) is introduced which can be coupled with the existing signaling system that can act as a key to smart traffic management in real time and will lead to reduced traffic congestion.
Abstract: Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement and bad traffic management has lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on economy, the environment and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. There are various methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor network, etc. All these methods are effective methods of smart traffic management. But the problem with these systems is that the installation time, the cost incurred for the installation and maintenance of the system is very high. Hence a new technology called Radio Frequency Identification (RFID) is introduced which can be coupled with the existing signaling system that can act as a key to smart traffic management in real time. This new technology which will require less time for installation with lesser costs as compared to other methods of traffic congestion management. Use of this new technology will lead to reduced traffic congestion. Bottlenecks will be detected early and hence early preventive measures can be taken thus saving time and money of the driver.

50 citations


Cites background from "A simple multiple loop sensor confi..."

  • ...Inductive loop detection is useful in knowing the vehicle presence, passage, occupancy and even the number of vehicles passing through a particular area ([6,7])....

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Journal ArticleDOI
Qifan Wei1, Bo Yang1
TL;DR: An adaptable roadside vehicle detection and speed estimation system for various traffic conditions on urban roads based on tri-axial anisotropic magnetoresistive sensors and wireless sensor network that has a significant increase in accuracy, reliability, and practicability compared with the fixed threshold algorithm.
Abstract: This paper presents an adaptable roadside vehicle detection and speed estimation system for various traffic conditions on urban roads based on tri-axial anisotropic magnetoresistive sensors and wireless sensor network. The system consists of one master node and two sensor nodes, which are placed along the roadsides and can measure the earth’s local magnetic field disturbance caused by passing vehicles. A dynamic threshold detection algorithm is proposed for vehicle detection, especially considering the actual variable traffic condition. The vehicle speed is estimated on the basis of the maximum values and the cross correlation of effective parts extracted from two sensor signals. We have tested the vehicle information at several roads under different traffic conditions. Validation study has revealed a high detection accuracy of 97.92% when using a dynamic threshold compared with 92.3% when using a fixed threshold. And, the average accuracy of speed estimation can reach up to 97.11% on the roads. The proposed algorithm has a significant increase in accuracy, reliability, and practicability compared with the fixed threshold algorithm.

30 citations


Cites background from "A simple multiple loop sensor confi..."

  • ...Nowadays, vehicle detection systems commonly use inductive loop detector [1], video camera [2] and radar detector [3], etc....

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Journal ArticleDOI
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.

28 citations


Cites background from "A simple multiple loop sensor confi..."

  • ...This loop structure has the advantage of being sensitive to various types of vehicles such as a bicycle, a motorcycle, a car, a bus, etc....

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Proceedings ArticleDOI
01 Aug 2016
TL;DR: Non Functional requirement analysis is done for the Internet of Things based traffic management unit which indicates the traffic density and the design components are selected to deploy the design unit.
Abstract: Non Functional requirement analysis is done for the Internet of Things (IoT) based traffic management unit which indicates the traffic density. Few quality characteristics of the design are analyzed during the development process. Design model decisions are governed by these Non Functional Requirement (NFR) design parameters. These quality characteristics considered are cost, sensitivity, design complexity, storage capacity, development process, response criteria and environmental impact. Having analyzed the quality attributes the design components are selected to deploy the design unit. Considerable effort needs to be put at the system design level to streamline the Internet of Things based design process. A Non Functional Requirement Analysis template documentation and checklist form is generated in this approach.

15 citations


Cites methods from "A simple multiple loop sensor confi..."

  • ...The IoT infrastructure mainly consists of the data acquisition unit in the IoT based Traffic Density Indication (TDI)....

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Proceedings ArticleDOI
25 Oct 2012
TL;DR: Results from a prototype system developed show that the RF based algorithm provides better accuracy compared to the threshold based and signature based methods.
Abstract: This paper presents a suitable algorithm to classify vehicles detected by a multiple inductive loop system, developed for measuring traffic parameters in a heterogeneous and no-lane disciplined traffic. The proposed classification scheme employs Random Forest (RF) algorithm. This scheme is suited not only for classifying the detected vehicles as bicycle, motorcycle, scooter, car and bus but also for counting them accurately under a mixed traffic condition. The algorithm has been implemented and tested. Its performance has also been compared with other algorithms based on threshold values and signature patterns. The threshold, signature and RF based algorithms use the number of loops a vehicle occupies as an important factor for classification. Results from a prototype system developed show that the RF based algorithm provides better accuracy compared to the threshold based and signature based methods.

15 citations

References
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Proceedings ArticleDOI
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


"A simple multiple loop sensor confi..." refers background in this paper

  • ...They can also calculate speed accurately from the measured time for crossing a known distance [5] and classify the vehicles using suitable algorithms [6]-[9]....

    [...]

Journal ArticleDOI
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


"A simple multiple loop sensor confi..." refers background in this paper

  • ...They can also calculate speed accurately from the measured time for crossing a known distance [5] and classify the vehicles using suitable algorithms [6]-[9]....

    [...]

Journal ArticleDOI
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


"A simple multiple loop sensor confi..." refers background in this paper

  • ...They can also calculate speed accurately from the measured time for crossing a known distance [5] and classify the vehicles using suitable algorithms [6]-[9]....

    [...]

Journal ArticleDOI
TL;DR: A novel vehicle-classification algorithm that uses the time-variable signal generated by a single inductive loop detector to strengthen the classification accuracy by emphasizing the undercarriage height variation of the vehicle.
Abstract: This paper presents a novel vehicle-classification algorithm that uses the time-variable signal generated by a single inductive loop detector In earlier studies, the noisy raw signal was fed into the algorithm by reducing its size with rough sampling However, this approach loses the original signal form and cannot be the best exemplar vector The developed algorithm suggests three contributions to cope with these problems The first contribution is to clear the noise with discrete Fourier transform (DFT) The second contribution is to transfer the noiseless pattern into the Principal Component Analysis (PCA) domain PCA is exploited not only for decorrelation but for explicit dimensionality reduction as well This goal cannot be achieved by simple raw data sampling The last contribution is to expand the principal components with a local maximum (Lmax) parameter It strengthens the classification accuracy by emphasizing the undercarriage height variation of the vehicle These parameters are fed into the three-layered backpropagation neural network (BPNN) BPNN classifies the vehicles into five groups, and the recognition rate is 9421% This recognition rate has performed best, compared with the methods presented in published works

67 citations

Patent
23 Jan 1975
TL;DR: In this paper, an inductive loop is used to detect vehicles in the immediate vicinity of the loop, where the vehicle's presence is dependent on whether or not the vehicle is over the loop.
Abstract: Apparatus for use in combination with an inductive loop for detecting metal objects, e.g. vehicles, in the immediate vicinity of said loop. The loop may, for example, be a coil of wire buried in a roadway in a plane parallel to the roadway surface. Oscillator circuitry is operatively connected to the loop with the frequency of oscillation being determined by the loop inductance, which in turn is dependent on whether or not the vehicle is over the loop. The loop frequency is monitored by digital circuitry including a loop counter which counts loop oscillator cycles and a duration counter which measures the time duration of a fixed number of loop oscillator cycles. The measured time duration is compared with an adaptable reference duration to ascertain whether the loop oscillator frequency has increased or decreased. The presence of a vehicle over the loop decreases loop inductance, increases loop frequency, and thus reduces the measured time duration of a fixed number of loop cycles. A reduction in the measured time duration by an amount greater than a preselected threshold value produces an output signal or "call" to indicate the vehicle's presence.

57 citations