Graph-based mobility profiling

Abstract

The decarbonization of the transport system requires a better understanding of human mobility behavior to optimally plan and evaluate sustainable transport options (such as Mobility as a Service). Current analysis frameworks often rely on specific datasets or data-specific assumptions and hence are difficult to generalize to other datasets or studies. In this work, we present a workflow to identify groups of users with similar mobility behavior that appear across several datasets. Our method does not depend on a specific clustering algorithm, is robust against the choice of hyperparameters, does not require specific labels in the dataset, and is not limited to specific types of tracking data. This allows the extraction of stable mobility profiles based on several small and inhomogeneous tracking data sets. Our method consists of the following main steps: Representing individual mobility using location-based graphs; extraction of graph-based mobility features; clustering using different hyperparameter configurations; group identification using statistical testing. The method is applied to six tracking datasets (Geolife, Green Class 1 + 2, yumuv and two Foursquare datasets) with a total of 1070 users that visit about 3′000’000 different locations with a total tracking duration of over 200′000 days. We can identify and interpret five mobility profiles that appear in all datasets and show how these profiles can be used to analyze longitudinal and cross-sectional tracking studies.