Threshold settings for TRIP/STOP detection in GPS traces

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...

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Veröffentlicht in:Journal of ambient intelligence and humanized computing Jg. 7; H. 3; S. 395 - 413
Hauptverfasser: Cich, Glenn, Knapen, Luk, Bellemans, Tom, Janssens, Davy, Wets, Geert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Automobile engines
Computational Intelligence
Data collection
Engineering
Identification
Methods
Original Research
Robotics and Automation
Sensitivity analysis
Sensors
Spacetime
User Interfaces and Human Computer Interaction
Trace segmentation
Sensitivity analysis
GPS recording
Stop detection
Trace generation
ISSN:1868-5137, 1868-5145
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-016-0360-9