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Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes

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Identifying and Interpreting Clusters of Persons with Similar Mobility Behaviour Change Processes

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With the emergence of new mobility options and various initiatives to increase the sustainability of our travel behaviour, it is desirable to gain a deeper understanding of our behavioural reactions to such stimuli. Although it is now possible to use GPS-tracking to record people’s movement behaviour over a longer period, there is still a lack of computational methods which allow to detect and evaluate such behaviour change processes in the resulting datasets. In this study, we propose a data mining method for describing individual persons’ mobility behaviour change processes based on their movement trajectories and clustering participants based on the similarity of these behavioural adaptations. We further propose to use a decision tree classifier to semantically explain the derived clusters in a human-interpretable form. We apply our method to a real, longitudinal movement dataset.
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... The proportion of each cluster size, as well as the number of clusters, may well be available as a priori knowledge for clustering. Clustering is based on a similarity measure to group semblable data objects together, so it is commonly utilized in areas such as market segmentation, vehicle routing selection, and healthcare problems [23,24]. On these occasions, users employ certain conditions for various classification purposes. ...
... The idea behind this heuristic was to induce decision rules with which to identify the variables and categorical values that would allow us to assign individuals to clusters. Inducing decision rules has the additional advantage of generating human-readable information on clusters, as has recently been done in a variety of applications (see [51,52]). ...
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