Hardware and
software for collecting microscopic trajectory data on naturalistic driving
behavior
Deepak Shrestha,
David J. Lovell, and Yorghos Tripodis
Journal of
Intelligent Transportation Systems, vol. 21(3), pp. 202-213
This paper presents a method to collect naturalistic microscopic longitudinal
vehicle trajectory data with a modest budget. The drivers studied are not aware
that they are participating in an experiment; hence one can collect
naturalistic driving behavior. This paper presents the hardware and software
developed, and we include a detailed example of a particular case study that
was conducted with data collected from the system. The case study examines drivers’
willingness to accept very short headways, and casts that behavior in light of
their subsequent lane-changing decisions. The data show a statistically
defensible connection between these behaviors. These phenomena are not new, but
highlight the importance of the data quality and of observing naturalistic
driving behavior, and this paper demonstrates a method to calibrate specific
parameters related to the behavior.