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

An elaborated pattern-based method of identifying data oscillations from mobile device location data