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.
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TL;DR: The cross-layer LoRa architecture is introduced, which is devised to facilitate its cognitive analysis and dynamically allocating network and information resources, which complements the limitations of the standard LoRa protocol.
Abstract: While traffic congestion has been pointed out as everyday driving stress, few attempts are specialized in traffic management by using current IoT technology. In order to help alleviate traffic stress from drivers, this article proposes a cross-layer LoRa architecture and a machine-learning algorithm for smart town’s traffic management systems. LoRa is selected since it has strengths in range and power when compared to other wireless communication technologies. We introduce the cross-layer LoRa architecture, which is devised to facilitate its cognitive analysis. By dynamically allocating network and information resources, it complements the limitations of the standard LoRa protocol. We also have designed the logistic regression algorithm-which runs above its cognitive engine. The proposed algorithm outputs traffic coefficients based on density and travel time. This algorithm has achieved 97% of accuracy in the simulation. With further research, we believe the proposed system could be an excellent solution for smart traffic management.
7 citations
01 Jan 2018
TL;DR: Overall objective of this work is to identify and segment the signatures of different vehicles from the noisy data, which is the first step for classified counting of vehicles.
Abstract: Inductive loop detectors (ILD) are one of the most popular traffic detectors in use. It works based on the principle of mutual inductance and detects vehicles by measuring the change in inductance due to its presence on top of the sensor. The change in voltage measured is usually called as vehicle signature and is the raw output from the detector system. Proper processing of output data will lead to accurate information about the type and nature of the vehicles movement. This processing needs careful attention, and this is particularly true when it is used under the heterogeneous and lane-less traffic conditions. Overall objective of this work is to identify and segment the signatures of different vehicles from the noisy data, which is the first step for classified counting of vehicles. This work proposes a simple and effective threshold-based approach following a two step procedure for ILD data segmentation. In the first step threshold value for segmentation is determined through a statistical characterization of the historical data corresponding to no-vehicle region. Consequently in the second step, standard deviation is estimated for complete raw data using the mean absolute deviation measure using moving window of data. The developed algorithm was tested, and results showed high accuracy in vehicle count. A guideline for selecting the optimal value of the threshold is also presented.
6 citations
Cites background or methods from "A multiple loop vehicle detection s..."
...ILD used in this work consists of an inductive loop and supporting electronic circuitry, as shown in Figure 1 [7]....
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...Basic circuitry used for data acquisition [7]...
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...[7] have proposed a new loop design which can detect large and small vehicles reliably....
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TL;DR: This paper presents the data interaction standard, which has been applied by two IEEE standards, and the architecture, which consists of configuration subsystem, data collection simulation subsystem, Web service registration center, and so on.
Abstract: Virtual instruments is a program that implements functions of an instrument by software which could replace the work of real instruments to save resources. The functions of these sensor-based systems are limited and they commonly cannot manage related information, such as sensors and monitoring objects, due to special requirements. The procedure of development and integration often suffers from low efficiency because of non-standard technologies. To solve the aforementioned problems, an integrated system architecture based on complex virtual instruments (CVI) is proposed, which could not only extend the function of virtual instruments but also ease the development procedure of the system. By analyzing the characters of existing virtual instruments systems, this paper presents the data interaction standard, which has been applied by two IEEE standards, and the architecture, which consists of configuration subsystem, data collection simulation subsystem, Web service registration center, and so on. We also offer a light universal client which could dynamically load the DynamicLinkLibrary of different CVIs, in order to replace the complex integration procedure by scheduling different components. To valid the architecture, three existing systems are reconstructed by the proposed prototype subsystems. The result shows that the proposed architecture is efficient and feasible.
6 citations
Cites background from "A multiple loop vehicle detection s..."
...Researchers in [11] use the sensor to detect the vehicle in lane-less traffic....
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TL;DR: The current study proposes a solution that uses electronic circuit modification to convert the existing serially connected loops to carry out lane-by-lane detection and proposes an improved loop design that can be used for vehicle classification and wrong way detection.
Abstract: Inductive loop detectors (ILDs) are one of the most widely deployed traffic sensors. At present, for lane-by-lane detection, ILDs require separate connecting cables for each loop (each lane) and se...
5 citations
TL;DR: Various sectors such as agriculture, transportation, garbage collection, security issues, sensors, etc are discussed along with the key technologies including RFID, IP, EPC, Wi-Fi, Bluetooth, and ZigBee and the future enhancements in IoT are discussed.
5 citations
References
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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 multiple loop vehicle detection s..." refers background in this paper
...Research papers discussing improvement of loop detectors for better speed measurement [5] and classification [6]-[9] 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
"A multiple loop vehicle detection s..." refers background in this paper
...Research papers discussing improvement of loop detectors for better speed measurement [5] and classification [6]-[9] for lane disciplined traffic conditions were reported....
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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
"A multiple loop vehicle detection s..." refers background or methods in this paper
...The speed of the vehicle can be obtained accurately by using double-loop detector system [5]....
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...Research papers discussing improvement of loop detectors for better speed measurement [5] and classification [6]-[9] for lane disciplined traffic conditions were reported....
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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