  In a previous study, we presented VT-Lane, a three-step framework for
real-time vehicle detection, tracking, and turn movement classification at
urban intersections. In this study, we present a case study incorporating the
highly accurate trajectories and movement classification obtained via VT-Lane
for the purpose of speed estimation and driver behavior calibration for traffic
at urban intersections. First, we use a highly instrumented vehicle to verify
the estimated speeds obtained from video inference. The results of the speed
validation show that our method can estimate the average travel speed of
detected vehicles in real-time with an error of 0.19 m/sec, which is equivalent
to 2% of the average observed travel speeds in the intersection of the study.
Instantaneous speeds (at the resolution of 30 Hz) were found to be estimated
with an average error of 0.21 m/sec and 0.86 m/sec respectively for
free-flowing and congested traffic conditions. We then use the estimated speeds
to calibrate the parameters of a driver behavior model for the vehicles in the
area of study. The results show that the calibrated model replicates the
driving behavior with an average error of 0.45 m/sec, indicating the high
potential for using this framework for automated, large-scale calibration of
car-following models from roadside traffic video data, which can lead to
substantial improvements in traffic modeling via microscopic simulation.
