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

  • Pengxiang ZhaoEmail author
  • Dominik Bucher
  • Henry Martin
  • Martin Raubal
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

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.

Keywords

Human movement data Travel mode choice behavior Autocorrelation Hierarchical clustering 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pengxiang Zhao
    • 1
    Email author
  • Dominik Bucher
    • 1
  • Henry Martin
    • 1
  • Martin Raubal
    • 1
  1. 1.Institute of Cartography and Geoinformation, ETH ZurichZurichSwitzerland

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