  In recent years, passively collected GPS data have been popularly applied in
various transportation studies, such as highway performance monitoring, travel
behavior analysis, and travel demand estimation. Despite multiple advantages,
one of the issues is data oscillations (aka outliers or data jumps), which are
unneglectable since they may distort mobility patterns and lead to wrongly or
biased conclusions. For transportation studies driven by GPS data, assuring the
data quality by removing noises caused by data oscillations is undoubtedly
important. Most GPS-based studies simply remove oscillations by checking the
high speed. However, this method can mistakenly identify normal points as
oscillations. Some other studies specifically discuss the removal of outliers
in GPS data, but they all have limitations and do not fit passively collected
GPS data. Many studies are well developed for addressing the ping-pong
phenomenon in cellular data, or cellular tower data, but the oscillations in
passively collected GPS data are very different for having much more various
and complicated patterns and being more uncertain. Current methods are
insufficient and inapplicable to passively collected GPS data. This paper aims
to address the oscillated points in passively collected GPS data. A set of
heuristics are proposed by identifying the abnormal movement patterns of
oscillations. The proposed heuristics well fit the features of passively
collected GPS data and are adaptable to studies of different scales, which are
also computationally cost-effective in comparison to current methods.
