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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21046

Title: Threshold settings for TRIP/STOP detection in GPS traces
Authors: Cich, Glenn
Knapen, Luk
Bellemans, Tom
Janssens, Davy
Wets, Geert
Issue Date: 2016
Citation: Journal of Ambient Intelligence and Humanized Computing, 7 (3), p. 395-413
Abstract: This paper presents two methods to extract stops and trips from GPS traces: the first one focuses on periods of non-movement (stops) and the second one tries to identify the longest periods of movement (trips). A stop corresponds to a location where the individual halts with the intention to perform an activity. In order to assert the quality of both methods, the results are compared to cases where the stops and trips are known by other means. First a set of traces was used for which the stops were identified by the traveler by means of a visual tool aimed at alignment of manually reported periods in the diary to automatically recorded GPS coordinates. Second, a set of synthetic traces was used. Several quality indicators are presented; they have been evaluated using sensitivity analysis in order to determine the optimal values for the detector’s configuration settings. Person traces (as opposed to car traces) were used. Individual specific behavior seems to have a large effect on the optimal values for threshold settings used in both the TRIP and STOP detector algorithms. Accurate detection of stops and trips in GPS traces is vital to prompted recall surveys because those surveys can extend over several weeks. Inaccurate stop detection requires frequent corrections by the respondent and can cause them to quit.
Notes: Cich, G (reprint author), Hasselt Univ, Transportat Res Inst IMOB, Wetenschapspk 5 Bus 6, Diepenbeek, Belgium glenn.cich@uhasselt.be; luk.knapen@uhasselt.be; tom.bellemans@uhasselt.be; davy.janssens@uhasselt.be; geert.wets@uhasselt.be
URI: http://hdl.handle.net/1942/21046
DOI: 10.1007/s12652-016-0360-9
ISI #: 000376597500009
ISSN: 1868-5137
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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