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

• Main Authors: Sascha von Behren, Tim Hilgert, Sophia Kirchner, Bastian Chlond, Peter Vortisch
• Format: Article
• Language: English
• Published: Elsevier 2020-11-01
• Series: Transportation Research Interdisciplinary Perspectives
• Subjects: Clustering; Activity pattern; Visualization; GraDiV; German mobility panel; Germany
• Online Access: http://www.sciencedirect.com/science/article/pii/S2590198220301755

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. Thus, we show an approach taking into account many aspects of travel behavior as an input to clustering, while ensuring the interpretability of solutions. Usually, key figures from the data are used for validation, but this practice may obscure some aspects of the longitudinal data, which are visible when looking on the images as validation.