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CONFERENCE[OC31]
Making Sense of the Noise - Integrating Multiple Analyses for Stop and Trip Classification
Spang, R. P., Pieper, K., Oesterle, B., Brauer, M., Haeger, C., Mümken, S., Gellert, P. & Voigt-Antons, J.-N.
Paper presented at the Free and Open Source Software for Geospatial (FOSS4G 2022). Firenze, Italy
Abstract
We propose a robust pipeline for classifying stops and trips from noisy real-world GPS and accelerometer data. By combining density-based clustering, temporal smoothing, and sensor-fusion heuristics, the approach improves segmentation stability across heterogeneous devices and sampling rates. Open-source tools and benchmarks on daily-life datasets demonstrate higher precision/recall and practical defaults for mobility research.
Record
- Reference
[OC31]in the Publikationsverzeichnis - TypeConference paper
- Year2022
- Research lineDigital Health & Learning
- Identifier
2022-08-06-OC30
BibTeX
@inproceedings{voigtantons2022oc30,
author = {Spang, R. P. and Pieper, K. and Oesterle, B. and Brauer, M. and Haeger, C. and Mümken, S. and Gellert, P. and Voigt-Antons, J.-N.},
title = {Making Sense of the Noise - Integrating Multiple Analyses for Stop and Trip Classification},
year = {2022},
booktitle = {Paper presented at the Free and Open Source Software for Geospatial (FOSS4G 2022). Firenze, Italy},
doi = {10.5194/isprs-archives-XLVIII-4-W1-2022-435-2022},
}