Abstract
Floating-car data (FCD) are never perfectly accurate due to various noises. To make FCD available, we propose a novel spatio-temporal particle filter ST-PF for FCD pre-processing. First we analyze the causes of errors and the shortcomings of previous studies. Second, we introduce the spatio-temporal constraints into the modeling of ST-PF. We also devise a novel iterating strategy for the recurrence of particle filtering based on sequential-importance sampling (SIS). We further design a series of experiments and compare the performances with that of other four traditional filters, namely, the mean filter, the median filter, the Kalman filter, and the original particle filter. The final results show ST-PF is much more effective for noise reduction and improvement of map-matching performance and shows a promising direction for FCD pre-processing.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants No. 41601421 and 41271408 and in part by the China Postdoctoral Science Foundation under Grant No. 2015M581158.
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Liu, X. et al. (2018). ST-PF: Spatio-Temporal Particle Filter for Floating-Car Data Pre-processing. In: Popovich, V., Schrenk, M., Thill, JC., Claramunt, C., Wang, T. (eds) Information Fusion and Intelligent Geographic Information Systems (IF&IGIS'17). Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-59539-9_15
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DOI: https://doi.org/10.1007/978-3-319-59539-9_15
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