<div class="csl-bib-body">
<div class="csl-entry">Kim, E.-K., Ebert, E., & Weibel, R. (2021). The Effect of Post-Processing in Stop-Move Detection of GPS Data: A Preliminary Study. In A. Basiri, G. Gartner, & H. Huang (Eds.), <i>LBS 2021: Proceedings of the 16th International Conference on Location Based Services</i> (p. 130). https://doi.org/10.34726/1759</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/18838
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dc.identifier.uri
https://doi.org/10.34726/1759
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dc.description
Published in “Proceedings of the 16th International Conference on
Location Based Services (LBS 2021)”, edited by Anahid Basiri, Georg
Gartner and Haosheng Huang, LBS 2021, 24-25 November 2021,
Glasgow, UK/online.
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dc.description.abstract
Stop-move detection has been an essential step to construct semantic
trajectories and extract meaningful activity sequences of moving objects.
Detecting stop and move segments accurately is critical because errors
occurred in stop-move detection can be propagated and amplified in later
steps in trajectory data analysis. In particular, post-processing that merges
or discards the detected stop-move segments can make an impact on the accuracy
and characteristics of detected stops and moves. Although many stopmove
detection algorithms exist and new methods are continuously proposed
in the field, studies on comparing the performance of the stop-move
detection methods are still scarce.
In this study, we evaluated the effect of post-processing in stop-move detection
with four selected existing stop-move detection algorithms—CandidateStops,
SOC, POSMIT, and MBGP—in two input-data scenarios: (1) original
data and (2) sampled data. The detected stops were assessed by two
quantitative measures that quantify the accuracy at different levels of aggregation
in space and time: (1) accuracy based on individual data points (i.e.,
F-measure) and (2) the shape of detected stops (i.e., shape compactness).
With the case study, we found that the impact of post-processing on the detection
results can vary by a selected algorithm and input data sparsity. The
results can potentially provide insights into how to adopt and maneuver the
stop-move detection methods for GPS data analysis.
en
dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
stop detection
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dc.subject
GPS data analysis
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dc.subject
semantic trajectory
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dc.subject
algorithm comparison
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dc.title
The Effect of Post-Processing in Stop-Move Detection of GPS Data: A Preliminary Study