A multiple loop vehicle detection system for heterogeneous and lane-less traffic
10 May 2011-pp 1-5
TL;DR: A novel inductive loop sensor which detects large as well as small vehicles and help a traffic control management system in optimizing the best use of existing roads is presented.
Abstract: This paper presents a novel inductive loop sensor which detects large (e.g., bus) as well as small (e.g., bicycle) vehicles and help a traffic control management system in optimizing the best use of existing roads. To accomplish the sensing of large as well as a small vehicle, a multiple loop inductive sensor system is proposed. The proposed sensor structure not only senses and segregates the vehicle type as bicycle or motor cycle or car or bus but also enables accurate counting of the number of vehicles that too in a mixed traffic flow condition. A prototype of the multiple loop sensing system has been developed using a virtual instrumentation scheme 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 multi loop sensor system for any type of traffic has been established.
Citations
More filters
[...]
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.
73 citations
Cites background from "A multiple loop vehicle detection s..."
[...]
[...]
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.
24 citations
Cites background from "A multiple loop vehicle detection s..."
[...]
[...]
[...]
[...]
TL;DR: The architecture and design of a Portable Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement, show that it can evaluate a Vehicle Detection System conveniently and objectively.
Abstract: In an upcoming smart transportation environment, performance evaluations of existing Vehicle Detection Systems are crucial to maintain their accuracy. The existing evaluation method for Vehicle Detection Systems is based on a wired Vehicle Detection System reference and a video recorder, which must be operated and analyzed by capable traffic experts. However, this conventional evaluation system has many disadvantages. It is inconvenient to deploy, the evaluation takes a long time, and it lacks scalability and objectivity. To improve the evaluation procedure, this paper proposes a Portable Vehicle Detector Evaluation System based on wireless sensor networks. We describe both the architecture and design of a Vehicle Detector Evaluation System and the implementation results, focusing on the wireless sensor networks and methods for traffic information measurement. With the help of wireless sensor networks and automated analysis, our Vehicle Detector Evaluation System can evaluate a Vehicle Detection System conveniently and objectively. The extensive evaluations of our Vehicle Detector Evaluation System show that it can measure the traffic information such as volume counts and speed with over 98% accuracy.
22 citations
Cites methods from "A multiple loop vehicle detection s..."
[...]
[...]
TL;DR: This paper implements a vehicle speed measuring system using the Hyperledger Fabric blockchain platform, demonstrating that blockchain-based measuring systems can impact the way measuring instruments are used in consumer relations while improving security and simplifying metrological regulation and control.
Abstract: In recent years, measuring instruments have become quite complex due to the integration of embedded systems and software components and the increasing aggregation of new features. Consequently, metrological regulation and control require more efforts from notified bodies, becoming slower and more expensive. In this paper, we evaluate the use of blockchains as a resource to overcome such challenges. We start with a conceptual model for implementing measuring instruments in a distributed blockchain-based architecture and compare it with traditional measuring instruments and distributed measuring models discussed in previous works. We also made a security analysis, demonstrating that blockchain-based measuring systems can impact the way measuring instruments are used in consumer relations while improving security and simplifying metrological regulation and control. We implement a vehicle speed measuring system using the Hyperledger Fabric blockchain platform. We evaluate the security and performance of our blockchain-based measuring system by executing tests with data from real speed meter sensors. The results are promising and validate the feasibility of our idea. Finally, we point out the main challenges related to our approach, suggesting alternatives and potential issues to be addressed by future works.
19 citations
Cites background or methods from "A multiple loop vehicle detection s..."
[...]
[...]
[...]
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.
16 citations
Cites background from "A multiple loop vehicle detection s..."
[...]
[...]
References
More filters
[...]
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.
136 citations
"A multiple loop vehicle detection s..." refers background in this paper
[...]
[...]
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
85 citations
"A multiple loop vehicle detection s..." refers background in this paper
[...]
[...]
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
80 citations
"A multiple loop vehicle detection s..." refers background or methods in this paper
[...]
[...]
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
[...]
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
57 citations
Related Papers (5)
[...]