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Visualizing Travel Patterns with a GPS Dataset: How Commuting Routes Influence Non-Work Travel Behavior

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  • Xiaoguang Wang
  • Joe Grengs
  • Lidia Kostyniuk

Abstract

This paper examines the spatial patterns of non-work activities for 34 drivers in the Southeast Michigan region. Capitalizing upon a unique global positioning systems (GPS) dataset and GIS visualization techniques, this study quantifies the spatial distributions of non-work activities for drivers with different commuting distances, and for non-work activities that are chained in different types of travel (commute travel vs. non-commute travel). We find a strong dependence of non-work activity locations on commuting distances, and an influence of commuting routes on non-work activities chained in all types of travel. The results underline the importance of commuting routes in shaping the spatial configuration of non-work activities.

Suggested Citation

  • Xiaoguang Wang & Joe Grengs & Lidia Kostyniuk, 2013. "Visualizing Travel Patterns with a GPS Dataset: How Commuting Routes Influence Non-Work Travel Behavior," Journal of Urban Technology, Taylor & Francis Journals, vol. 20(3), pages 105-125, July.
    • Handle: RePEc:taf:cjutxx:v:20:y:2013:i:3:p:105-125
    DOI: 10.1080/10630732.2013.811986
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    Cited by:

    1. Bhat, Chandra R. & Astroza, Sebastian & Bhat, Aarti C. & Nagel, Kai, 2016. "Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 52-76.
    2. Zidan Mao & Dick Ettema & Martin Dijst, 2018. "Analysis of travel time and mode choice shift for non-work stops in commuting: case study of Beijing, China," Transportation, Springer, vol. 45(3), pages 751-766, May.

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