Journal ArticleDOI
Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement
TLDR
It is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95% and is compact, portable, wireless, and inexpensive.Abstract:
This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway-the devices are placed next to the roadway and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5-27 m/s (11-60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by 0.25 m. Finally, it is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA.read more
Citations
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Journal ArticleDOI
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey
TL;DR: This article presents a review of state-of-the-art traffic monitoring systems focusing on the major functionality–vehicle classification and discusses hardware/software design, deployment experience, and system performance of vehicle classification systems.
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Kalman filter-based tracking of moving objects using linear ultrasonic sensor array for road vehicles
TL;DR: In this article, an empirical detection model was developed considering the shapes and surface materials of various detected objects, and eight sensors were arrayed linearly to expand the detection range for further application in traffic environment recognition.
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Real-Time Traffic Speed Estimation With Graph Convolutional Generative Autoencoder
James J.Q. Yu,Jiatao Gu +1 more
TL;DR: This paper presents a novel deep-learning model called graph convolutional generative autoencoder to fully address the real-time traffic speed estimation problem and demonstrates that the proposed technique can notably outperform existing trafficSpeed estimation and deep- learning techniques.
Journal ArticleDOI
A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring.
TL;DR: The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors, and that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors.
Journal ArticleDOI
Development of an IoT based real-time traffic monitoring system for city governance
TL;DR: An IoT based system model is proposed to provide real-time traffic updates on traffic congestion and unusual traffic incidents through roadside message units and thereby improve mobility.
References
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TL;DR: In this paper, the authors used magnetic sensor networks for traffic measurement in freeways and intersections, and reported that the vehicle detection rate was better than 99 percent (100 percent for vehicles other than motorcycles).
Vehicle detection and compass applications using amr magnetic sensors
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TL;DR: In this article, a review of magnetic sensing and applications for magnetic sensors is presented, focusing on magnetic sensing in vehicle detection and navigation that is based on magnetic fields, and discusses applications of magnetic sensors.
Traffic Detector Handbook: Third Edition - Volume II
TL;DR: The objective of this Handbook is to provide a comprehensive resource for selecting, designing, installing, and maintaining traffic sensors for signalized intersections and freeways, intended for use by traffic engineers and technicians having responsibility for traffic sensors, whether in-roadway or over- roadway sensors.