Document - The trajectory interval forest classifier for trajectory classification

2023

Conference article Open Access

The trajectory interval forest classifier for trajectory classification

Landi C., Guidotti R., Nanni M., Monreale A.

GPS trajectory classification Mobility data analysis

GPS devices generate spatio-temporal trajectories for different types of moving objects. Scientists can exploit them to analyze migration patterns, manage city traffic, monitor the spread of diseases, etc. Many current state-of-the-art models that use this data type require a not negligible running time to be trained. To overcome this issue, we propose the Trajectory Interval Forest (TIF) classifier, an efficient model with high throughput. TIF works by calculating various mobility-related statistics over a set of randomly selected intervals. These statistics are used to create a tabular representation of the data, which can be used as input for any classical classifier. Our results show that TIF is comparable to or better than state-of-art in terms of accuracy and is orders of magnitude faster.

Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023

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BibTeX entry

@inproceedings{oai:it.cnr:prodotti:491501,
	title = {The trajectory interval forest classifier for trajectory classification},
	author = {Landi C. and Guidotti R. and Nanni M. and Monreale A.},
	doi = {10.1145/3589132.3625617},
	booktitle = {SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, Hamburg, Germany, 13-16/11/2023},
	year = {2023}
}