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Urban developments and daily travel distances: Fixed, random and hybrid effects models using a Dutch pseudo-panel over three decades

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  • Kasraian, Dena
  • Maat, Kees
  • van Wee, Bert

Abstract

As people require time to adjust their travel behaviour to changes in residential location and transport infrastructure, there is a need for long-term empirical studies quantifying the relationships between locations, individuals and travel behaviour. Such empirical evidence is critical for assessing previous and candidate future land use-transport policies. Existing research however, has mostly investigated travel behaviour during relatively short time periods and for a single transport mode. This paper examines the development of travel behaviour and its socio-demographic and location determinants, using Dutch National Travel Survey data from 1980 to 2010 among other sources, for the Randstad, the Netherlands. A pseudo panel analysis is conducted to investigate the effect of various indicators on average daily distance travelled by train, car and bicycle over three decades. Econometric models including pooled ordinary least squares, fixed and random effects and a hybrid model were tested to identify the best fit. The results indicate that average daily distance travelled rose until the mid-1990s before witnessing a decrease till 2010. Interestingly, half of the Randstad inhabitants have been travelling ≤26 km per day over the past thirty years. Furthermore, as people grow older, they increasingly travel more by train and bicycle. Finally, a rise in suburban inhabitants decreases the average distance travelled by train and increases that of bicycle, while a rise in rural inhabitants encourages higher distances travelled by car.

Suggested Citation

  • Kasraian, Dena & Maat, Kees & van Wee, Bert, 2018. "Urban developments and daily travel distances: Fixed, random and hybrid effects models using a Dutch pseudo-panel over three decades," Journal of Transport Geography, Elsevier, vol. 72(C), pages 228-236.
    • Handle: RePEc:eee:jotrge:v:72:y:2018:i:c:p:228-236
    DOI: 10.1016/j.jtrangeo.2018.09.006
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    1. Dargay, Joyce M., 2002. "Determinants of car ownership in rural and urban areas: a pseudo-panel analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 38(5), pages 351-366, September.
    2. Feng, Jianxi & Dijst, Martin & Wissink, Bart & Prillwitz, Jan, 2017. "Changing travel behaviour in urban China: Evidence from Nanjing 2008–2011," Transport Policy, Elsevier, vol. 53(C), pages 1-10.
    3. Verbeek, Marno & Nijman, Theo, 1992. "Can Cohort Data Be Treated as Genuine Panel Data?," Empirical Economics, Springer, vol. 17(1), pages 9-23.
    4. Deaton, Angus, 1985. "Panel data from time series of cross-sections," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 109-126.
    5. Baum-Snow, Nathaniel & Kahn, Matthew E., 2000. "The effects of new public projects to expand urban rail transit," Journal of Public Economics, Elsevier, vol. 77(2), pages 241-263, August.
    6. Lambert van der Laan, 1998. "Changing Urban Systems: An Empirical Analysis at Two Spatial Levels," Regional Studies, Taylor & Francis Journals, vol. 32(3), pages 235-247.
    7. Dena Kasraian & Kees Maat & Dominic Stead & Bert van Wee, 2016. "Long-term impacts of transport infrastructure networks on land-use change: an international review of empirical studies," Transport Reviews, Taylor & Francis Journals, vol. 36(6), pages 772-792, November.
    8. Yusak Susilo & Kees Maat, 2007. "The influence of built environment to the trends in commuting journeys in the Netherlands," Transportation, Springer, vol. 34(5), pages 589-609, September.
    9. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
    10. Chi-Hong (Patrick) Tsai & Corinne Mulley & Geoffrey Clifton, 2014. "A Review of Pseudo Panel Data Approach in Estimating Short-run and Long-run Public Transport Demand Elasticities," Transport Reviews, Taylor & Francis Journals, vol. 34(1), pages 102-121, January.
    11. Noreen C. McDonald, 2015. "Are Millennials Really the "Go-Nowhere" Generation?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 81(2), pages 90-103, April.
    12. Mundlak, Yair, 1978. "On the Pooling of Time Series and Cross Section Data," Econometrica, Econometric Society, vol. 46(1), pages 69-85, January.
    13. Weis, Claude & Axhausen, Kay W., 2009. "Induced travel demand: Evidence from a pseudo panel data based structural equations model," Research in Transportation Economics, Elsevier, vol. 25(1), pages 8-18.
    14. Kees Maat & Harry J.P. Timmermans, 2009. "A causal model relating urban form with daily travel distance through activity/travel decisions," Transportation Planning and Technology, Taylor & Francis Journals, vol. 32(2), pages 115-134, March.
    15. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Can cohort data be treated as genuine panel data?," Other publications TiSEM d4eada8f-b91c-4fe7-a58c-7, Tilburg University, School of Economics and Management.
    16. Chi-Hong (Patrick) Tsai & Corinne Mulley, 2014. "Identifying Short-Run and Long-Run Public Transport Demand Elasticities in Sydney A Pseudo Panel Approach," Journal of Transport Economics and Policy, University of Bath, vol. 48(2), pages 241-259, May.
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