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

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.