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Journal of Networking Technology

Detecting Travel Nature of Individuals during Movements Using Cluster-Based Model

Ye Hong, Yanan Xin, Dominik Bucher, Martin Raubal, Henry Martin
Institute of Cartography and Geoinformation, ETH Zurich, Switzerland., Institute of Advanced Research in Artificial Intelligence (IARAI), Austria Institute of Cartography and Geoinformation, ETH Zurich, Switzerland

Abstract: While travelling from place to places movement data is recorded that gives insights and the long-term change of individual travel behaviour. In this work we advocate a clustering-based framework to identify travel behaviour patterns and detect potential change periods on the individual level. Initially we draw the significant visits that outline individual characteristic movement. Next after using the trip data, and trip duration as travel behaviour dimensions, we compute the similarities of trips and group them into clusters using hierarchical clustering. The trip clusters represent dimensions of travel behaviours, and the change of their relative proportions over time reflect the development of travel preferences. Then we employed two models to find changes in travel behaviour patterns namely the Herfindahl- Hirschman index-based method and the sliding window-based method. We have experimented the design with the data from a large-scale longitudinal GPS tracking data study in which users had access to a Mobility-as-a-Service (MaaS) offer. The methods successfully identify significant travel behaviour changes for users. We found that the proposed design for behaviour change detection provides valuable insights for travel demand management and evaluating peopleÃ¢â‚¬â„¢s reactions to sustainable mobility options.

Keywords: Human Mobility, Travel Behaviour, Change Detection, Trip Clustering Detecting Travel Nature of Individuals during Movements Using Cluster-Based Model

DOI:https://doi.org/10.6025/jnt/2021/12/4/123-135

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