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A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior

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Book cover Geospatial Technologies for Local and Regional Development (AGILE 2019)

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Abstract

Travel mode choice analysis is a central aspect of understanding human mobility and plays an important role in urban transportation and planning. The emergence of passively recorded movement data with spatio-temporal and semantic information offers opportunities for uncovering individuals’ travel mode choice behavior. Considering that many of these choices are highly regular and are performed in similar manners by different groups of people, it is desirable to identify these groups and their characteristic behavior (e.g. for educational or political incentives or to find environmentally-friendly people). Previous research mainly grouped people according to “mobility snapshots”, i.e. mobility patterns exhibited at a single point in time. We argue that especially when considering the change of behavior over time, we need to investigate the behavioral dynamic processes resp. the change of travel mode choices over time. We present a framework that can be used to cluster people according to the dynamics of their travel mode choice behavior, based on automatically tracked GPS data. We test the framework on a large user sample of 107 persons in Switzerland and interpret their travel mode choice behavior patterns based on the clustering results. This facilitates understanding people’s travel mode choice behavior in multimodal transportation and how to design reasonable alternatives to private cars for more sustainable cities.

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Acknowledgements

This research was supported by the Swiss Data Science Center (SDSC), by the Swiss Innovation Agency Innosuisse within the Swiss Competence Center for Energy Research (SCCER) Mobility and by the Swiss Federal Railways SBB.

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Authors and Affiliations

  1. Institute of Cartography and Geoinformation, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland

    Pengxiang Zhao, Dominik Bucher, Henry Martin & Martin Raubal

Authors
  1. Pengxiang Zhao
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  2. Dominik Bucher
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  3. Henry Martin
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  4. Martin Raubal
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Corresponding author

Correspondence to Pengxiang Zhao .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Phaedon Kyriakidis

  2. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Diofantos Hadjimitsis

  3. Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus

    Dimitrios Skarlatos

  4. Department of Physical Geography and Ecosystem Science, Lund University GIS Center, Lund, Skåne Län, Sweden

    Ali Mansourian

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Zhao, P., Bucher, D., Martin, H., Raubal, M. (2020). A Clustering-Based Framework for Understanding Individuals’ Travel Mode Choice Behavior. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D., Mansourian, A. (eds) Geospatial Technologies for Local and Regional Development. AGILE 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-14745-7_5

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