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An empirical model for trip distribution of commuters in The Netherlands: transferability in time and space reconsidered

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  • Thomas, T.
  • Tutert, S.I.A.

Abstract

In this paper, we evaluate the distribution of commute trips in The Netherlands, to assess its transferability in space and time. We used Dutch Travel Surveys from 1995 and 2004–2008 to estimate the empirical distribution from a spatial interaction model as function of travel time and distance. We find that the distribution function is not transferable in time and space. Instead, the propensity to travel longer to work has grown over time. As a result, the average commute length has increased. Contrary, land use changes appeared to have a much smaller effect on commuting distances in this period. The propensity to make longer commute trips also increases with city-size. However, this does not lead to an increase in average commuting distance with city-size, because it is offset by smaller distances between residences and jobs in large cities. The results are quite relevant for spatial interaction modeling. They indicate that the concept of one stable distribution function is insufficient to accurately describe aggregated travel behavior.

Suggested Citation

  • Thomas, T. & Tutert, S.I.A., 2013. "An empirical model for trip distribution of commuters in The Netherlands: transferability in time and space reconsidered," Journal of Transport Geography, Elsevier, vol. 26(C), pages 158-165.
    • Handle: RePEc:eee:jotrge:v:26:y:2013:i:c:p:158-165
    DOI: 10.1016/j.jtrangeo.2012.09.005
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    Cited by:

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    2. Oshan, Taylor M., 2020. "The spatial structure debate in spatial interaction modeling: 50 years on," OSF Preprints 42vxn, Center for Open Science.
    3. Arnott, Richard, 2013. "A bathtub model of downtown traffic congestion," Journal of Urban Economics, Elsevier, vol. 76(C), pages 110-121.
    4. Sarah Salem & Khandker M. Nurul Habib, 2019. "Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes," Transportation, Springer, vol. 46(2), pages 395-423, April.
    5. Oshan, Taylor M., 2020. "Potential and pitfalls of big transport data for spatial interaction models of urban mobility," OSF Preprints gwumt, Center for Open Science.
    6. Lenormand, Maxime & Bassolas, Aleix & Ramasco, José J., 2016. "Systematic comparison of trip distribution laws and models," Journal of Transport Geography, Elsevier, vol. 51(C), pages 158-169.
    7. Jin, Wen-Long, 2020. "Generalized bathtub model of network trip flows," Transportation Research Part B: Methodological, Elsevier, vol. 136(C), pages 138-157.

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