A Multiple Inductive Loop Vehicle Detection System for Heterogeneous and Lane-Less Traffic
01 May 2012-IEEE Transactions on Instrumentation and Measurement (IEEE)-Vol. 61, Iss: 5, pp 1353-1360
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.
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TL;DR: A novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM), which achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions.
Abstract: Advancements in sensing and the Internet of Things (IoT) technologies generate a huge amount of data. Mobility on demand (MoD) service benefits from the availability of big data in the intelligent transportation system. Given the future travel demand or origin–destination (OD) flows prediction, service providers can pre-allocate unoccupied vehicles to the customers’ origins of service to reduce waiting time. Traditional approaches on future travel demand and the OD flows predictions rely on statistical or machine learning methods. Inspired by deep learning techniques for image and video processing, through regarding localized travel demands as image pixels, a novel deep learning model called multi-scale convolutional long short-term memory network (MultiConvLSTM) is developed in this paper. Rather than using the traditional OD matrix which may lead to loss of geographical information, we propose a new data structure, called OD tensor to represent OD flows, and a manipulation method, called OD tensor permutation and matricization, is introduced to handle the high dimensionality features of OD tensor. MultiConvLSTM considers both temporal and spatial correlations to predict the future travel demand and OD flows. Experiments on real-world New York taxi data of around 400 million records are performed. Our results show that the MultiConvLSTM achieves the highest accuracy in both one-step and multiple-step predictions and it outperforms the existing methods for travel demand and OD flow predictions.
81 citations
Cites background from "A Multiple Inductive Loop Vehicle D..."
...(GPS) for vehicle localization and navigation [7], vehicular communication of road map data for autonomous driving [8], traffic flow estimation by inductive loop or camera to count and classify vehicles for traffic management [9], etc....
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TL;DR: A cooperative traffic control framework for optimizing the global throughput and travel time for multiple intersections is proposed and the approach outperforms existing schemes in that it achieves a high global throughput, reduces the average waiting time, lowers the total travel time, and decreases average CO2 emissions.
Abstract: Traffic congestion is a critical concern in most cities. Inefficient traffic control wastes time and fuel, and causes harmful carbon emissions, road accidents, and many economic problems. This paper proposes a cooperative traffic control framework for optimizing the global throughput and travel time for multiple intersections. Adjacent intersections are considered in analyzing their joint passing rates and attempting to maximize the number of vehicles traveling through a road network. The proposed framework provides fairness for each road segment and realizes the green wave concept for arterial roads. This paper extends previous studies by considering the passing rates of continuous road segments and coordinating traffic signals of multiple intersections. The simulation results show that the approach outperforms existing schemes in that it achieves a high global throughput, reduces the average waiting time, lowers the total travel time, and decreases average CO2 emissions. To verify the feasibility of the proposed framework, a wireless access in vehicular environments/dedicated short-range communications-based prototype for lane-level dynamic traffic control is designed and implemented.
66 citations
Cites methods from "A Multiple Inductive Loop Vehicle D..."
...This sensor is used in SCOOT [6], SCATS [7], and the multiple inductive loop vehicle detection system [9]....
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...Real-time traffic information is generally collected by dedicated detectors, such as induction loops [8], [9], magnetic sensors, and video cameras [10], [11], to obtain the number of vehicles approaching or exiting an intersection....
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TL;DR: Simulation results show that the proposed framework outperforms existing works by significantly increasing the number of vehicles passing an intersection while keeping average waiting time low for vehicles on non-arterial roads.
Abstract: Traffic congestion in modern cities seriously affects our living quality and environments. Inefficient traffic management leads to fuel wastage in volume of billion gallons per year. In this paper, we propose a dynamic traffic control framework using vehicular communications and fine-grained information, such as turning intentions and lane positions of vehicles, to maximize traffic flows and provide fairness among traffic flows. With vehicular communications, the traffic controller at an intersection can collect all fine-grained information before vehicles pass the intersection. Our proposed signal scheduling algorithm considers the flows at all lanes, allocates more durations of green signs to those flows with higher passing rates, and also gives turns to those with lower passing rates for fairness provision. Simulation results show that the proposed framework outperforms existing works by significantly increasing the number of vehicles passing an intersection while keeping average waiting time low for vehicles on non-arterial roads. In addition, we discuss our implementation of an Zigbee-based prototype and experiences.
48 citations
Cites background from "A Multiple Inductive Loop Vehicle D..."
...Adaptive traffic controls [7] rely on collecting real-time traffic information by dedicated detectors, such as inductive loops [8], magnetic sensors [9], and video cameras [10]....
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TL;DR: A novel vehicle detection algorithm is proposed based on the short-term variance sequence transformed from raw magnetic signal, and the parking-sensitive module is introduced to enhance the robustness and adaptability of a detection method.
Abstract: Vehicle detection and identification techniques have been widely applied in traffic scene to acquire traffic information depending on various sensors, such as video camera, induction loop, and magnetic sensor. The quantity and category of vehicles are the key components of the intelligent transportation systems as they provide original data for further analysis. Compared with the inductive loop and video camera, magnetic sensor can measure magnetic field distortion caused by the movement of vehicles. The precise amount and category of a vehicle can be obtained through reasonable data analysis. A novel vehicle detection algorithm is proposed based on the short-term variance sequence transformed from raw magnetic signal. The parking-sensitive module is introduced to enhance the robustness and adaptability of a detection method. With abundant signal data, 42-D features are extracted from every vehicle signal comprising statistical features of whole waveform and short-term features of fragment signal. The Gradient Tree Boosting algorithm is employed to identify four vehicle categories. The effectiveness of the proposed approach is validated by the data collected at a freeway exit of Beijing. According to the experiential results based on 4507 vehicles, the vehicle detection algorithm proves to have 99.8% accurate rate and can be highly practical in site traffic environment. The 80.5% accuracy rate on vehicle identification approves the effectiveness of the proposed features on recognizing vehicles.
48 citations
01 Sep 2015
TL;DR: The proposed model makes use of radio transmitter-receiver to detect the presence of any ambulance/ fire brigade/ police vehicle and provide immediate right of way by traffic signal pre-emption and is a complete model, one solution to many of traffic congestion related problems.
Abstract: Through this paper we present the use of inductive loops as an instrument to measure traffic density. A microcontroller can be programmed to receive information about traffic density on different lanes, as measured by the inductive loops. Algorithms that not only ease congestion but also ensure the people in less congested lanes dont have to wait too long are discussed. Depending upon the traffic density a suitable algorithm can be executed to clear the congestion. A new design of inductive loop to suit our algorithm in case of multiple lane traffic has also been discussed here. Apart from causing delay, many times traffic congestion has resulted in loss of precious lives since help isnt able to reach the needy on time. In our proposed model we make use of radio transmitter-receiver to detect the presence of any ambulance/ fire brigade/ police vehicle and provide immediate right of way by traffic signal pre-emption. Lastly, there are many people who have a tendency of stopping way beyond the zebra crossing at a red signal. The use of infrared sensors to detect such vehicles and sound a buzzer to alert the traffic police has been presented. Overall, it is a complete model, one solution to many of traffic congestion related problems.
29 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 Inductive Loop Vehicle D..." refers background in this paper
...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
"A Multiple Inductive Loop Vehicle D..." refers background in this paper
...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 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 Inductive Loop Vehicle D..." refers background or methods in this paper
...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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...accurately obtained by using double-loop detector system [8]....
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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