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Image-based activity pattern segmentation using longitudinal data of the German Mobility Panel

Behren, Sascha von; Hilgert, Tim; Kirchner, Sophia; Chlond, Bastian; Vortisch, Peter

In this paper, we present an approach to segment people based on a visualization of the longitudinal week activity data from the German Mobility Panel. In order to perform segmentations, different clustering methods are commonly used. Most of the approaches require comprehensive prior knowledge about the input data, e.g., condensing information to cluster-forming variables. As this may influence the method itself, we used images with a high degree of freedom. These images show week activity schedules of people, including all trips and activities with their purposes, modes as well as their duration or their temporal position within the week. Thus, we answer the question whether using only this type of image data as input will produce reasonable clustering results as well. For the clustering, we extracted the images from an existing tool, processed them for the method and finally used them again to select the final cluster solution based on the visual impression of cluster assignments. Our results are meaningful as we identified seven activity patterns (clusters) using this visual validation. The approach is confirmed by the data-based analysis of the cluster solution showing also interpretable key figures for all patterns. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000127228
Veröffentlicht am 03.02.2021
DOI: 10.1016/j.trip.2020.100264
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Verkehrswesen (IFV)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2020
Sprache Englisch
Identifikator ISSN: 2590-1982
KITopen-ID: 1000127228
Erschienen in Transportation research interdisciplinary perspectives
Verlag Elsevier
Band 8
Seiten Art.-Nr. 100264
Nachgewiesen in Dimensions
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