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Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement

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2 Author(s)
Saber Taghvaeeyan ; Univ. of Minnesota, Twin Cities, Minneapolis, MN, USA ; Rajesh Rajamani

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.

Published in:

IEEE Transactions on Intelligent Transportation Systems  (Volume:15 ,  Issue: 1 )