Abstract
Compared with common data sets such as vehicle GPS and POI punch-in data, mobile trajectory data more specifically records people's real travel conditions. However, due to the lack of accuracy of the acquisition equipment, compared with the use of more professional GPS positioning equipment, mobile trajectory data has more data errors and lacks. Therefore, more data preprocessing steps are required before the mobile trajectory data is put into practical use. This chapter summarizes various existing techniques for noise removal of trajectory data, including mean filtering, median filtering, Kalman filtering, particle swarm filtering, and road network matching. In addition, the effect of trajectory filtering on mobile trajectory data is shown.
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