The Influence of Spatial Factors on the Commuting Trip Distribution in the Netherlands

  • Tom ThomasEmail author
  • Bas Tutert
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 144)


Traffic flows are the result of movements of people and goods. They are modeled with the help of behavioral patterns that are supposed to remain relatively constant over time. In traditional transport modeling, some of these patterns are described by trip distribution functions, which represent the propensity to make trips with certain costs. The distribution functions (DF) are used to estimate a priori origin destination (OD) matrices.


Traffic Flow Gravity Model Travel Behavior Urbanization Level Origin Destination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research has been partly funded by Transumo.


  1. Albert R, Barabási A-L (2000) Topology of evolving networks. Phys Rev Lett 85:5234–5237CrossRefGoogle Scholar
  2. Arentze TA, Timmermans HJP (2004) A learning-based transportation oriented simulation system. Trans Res B Methodological 38:613–633CrossRefGoogle Scholar
  3. Ben-Akiva M, Lerman S (1985) Discrete choice analysis. MIT, MAGoogle Scholar
  4. Bhat CR, Guo J (2005) Update from Texas, The 84th annual meeting of the transportation research board, CD-Rom, January 9–13, Washington DCGoogle Scholar
  5. Black WR (1995) Spatial interaction modeling using artificial neural networks. J Trans Geography 3(3):159–166CrossRefGoogle Scholar
  6. Bradley M, Vovsha P (2005) Model for joint choice of daily activity pattern types of household members. The 84th annual meeting of the transportation research board, CD-Rom, January 9–13, Washington DCGoogle Scholar
  7. Chalasani VS, Engebretsen Ø, Denstadli JM, Axhausen KW (2004) Precision of geocoded locations and network distance. In: Arbeitsbericht Verkehrs- und Raumplanung, 256, IVT. ETH, Zurich, SwitzerlandGoogle Scholar
  8. Cörvers F, Hensen M (2003) The regionalization of labour markets by modeling commuting behaviour. Proceedings on 43rd ERSA, JyvaeskylaeGoogle Scholar
  9. de Ortuzar JD, Willumsen LG (2001) Modeling transport. Wiley, Chichester, UKGoogle Scholar
  10. de Vries JJ, Nijkamp P, Rietveld P (2004) Exponential or power distance decay for commuting? An alternative specification. Tinbergen Institute Discussion Paper, AmsterdamGoogle Scholar
  11. Fotheringham AS, O’Kelly ME (1989) Spatial interaction models: formulations and applications. Kluwer, LondonGoogle Scholar
  12. Furness KP (1970) Time function interaction. Traffic Eng Contr 7(7):19–36Google Scholar
  13. Gopal S, Fischer MM (1996) Learning in single hidden-layer feedforward network: backpropagation in a spatial interaction modeling context. Geogr Anal 28:38–55CrossRefGoogle Scholar
  14. Levinson DM, Kumar A (1996) Multi-modal trip distribution: structure and application. Trans Res Rec 1466:124–131Google Scholar
  15. Ma K-R, Banister D (2006) Extended excess commuting: a measure of the jobs-housing imbalance in Seoul. Urban Stud 43(11):2099–2113CrossRefGoogle Scholar
  16. Magidson J, Eagle T, Vermunt T (2003) New developments in latent class choice models, Proceedings of Sawtooth Software Conference, San Antonio, Texas, United States, pp 89–112Google Scholar
  17. McFadden D (2001) Economic choices. Am Econ Rev 91(3):351–378CrossRefGoogle Scholar
  18. Patuelli R, Reggiani A, Gorman SP, Nijkamp P, Bade F (2007) Network analysis of commuting flows: a comparative static approach to German data. Network Spatial Econ 7(4):315–331CrossRefGoogle Scholar
  19. Rijkswaterstaat, Dienst Verkeer en Scheepvaart (2004–2006). Mobiliteitsonderzoek Nederland 2004, 2005, 2006Google Scholar
  20. Ritsema van Eck J, van Oort F, Raspe O, Daalhuizen F, van Brussel J (2006) Vele steden maken nog geen randstad. Publication Ruimtelijk Planbureau, The NetherlandsGoogle Scholar
  21. Stopher P, FitzGerald C, Xu M (2007) Assessing the accuracy of the Sydney Household Travel Survey with GPS. Transportation 34(6):723–741CrossRefGoogle Scholar
  22. Walker J, Li J (2007) Latent lifestyle preferences and household location decisions. J Geogr Syst 9(1):77–101CrossRefGoogle Scholar
  23. Wilson AG (1970) Entropy in urban and regional modeling. Pion, LondonGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.University of TwenteEnschedeThe Netherlands

Personalised recommendations