The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-4/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 435–441, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-435-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4/W1-2022, 435–441, 2022
https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-435-2022
 
06 Aug 2022
06 Aug 2022

MAKING SENSE OF THE NOISE: INTEGRATING MULTIPLE ANALYSES FOR STOP AND TRIP CLASSIFICATION

R. P. Spang1, K. Pieper1, B. Oesterle1, M. Brauer1, C. Haeger2, S. Mümken2, P. Gellert2, and J.-N. Voigt-Antons3,4 R. P. Spang et al.
  • 1Quality and Usability Lab, Berlin Institute of Technology, Berlin, Germany
  • 2Institute of Medical Sociology and Rehabilitation Science, Charité - Universitätsmedizin Berlin, Berlin, Germany
  • 3University of Applied Sciences Hamm-Lippstadt, Germany
  • 4German Research Center for Artificial Intelligence (DFKI), Berlin, Germany

Keywords: GNSS, Analysis, Algorithm, Processing, Stop Trip Classification, Geometry

Abstract. Mobility research is mainly concerned with understanding mobility on a higher level, including environmental factors, e.g., measuring the time out of home or tracking revisited places. This requires preprocessing the raw data obtained from GPS sensors, like clustering significant locations and distinguishing these from periods on the go. We introduce a new stop and trip detection algorithm to transform a list of position records into intervals of dwelling and transit. The system is based on geometrical analyses of the signal noise: Imperfect GPS data tends to scatter around an actual dwell position in a star-like pattern, and this imperfection is what we leverage for our classification. The system contains four independent classification methods, comparing different aspects of the geometrical properties of a given trajectory. If available, accelerometer readings can be used to improve the system’s accuracy further. To evaluate the classifier’s performance, we recorded a large dataset containing gold-standard labels and compared the classification results of our system with the results of Scikit Mobility and Moving Pandas. Our Stop Go Classifier outperforms the traditional distance/time-threshold-based systems. The described system is available as free software.