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Spatial interaction models from Irish commuting data: variations in trip length by occupation and gender

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  • Morton O’Kelly

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  • Michael Niedzielski
  • Justin Gleeson
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    Abstract

    Core and peripheral contrasts in journey-to-work trip length can be interpreted as imputing the relative value of origin and destination accessibility (yielding theoretical proxies for rent and wages). Because the main variables are shown to be critically dependent on spatial structure, they may be interpreted as showing the shadow prices due to comparative location. There is also a unifying connection between these results and the existing literature on many dimensions: rent gradients, accessibility, and emissivity. In an empirical example, the advantages of a panoramic view of national commuting statistics are shown, using an Irish data set. Variations in the rates of participation in trip making by location, occupation, and gender are examined. Places that emit more trips than would be expected from their relative location are identified. Further, examining ways in which such emissivity is sensitive to a change in trip length highlights the regions where trips could possibly be adjusted to produce a shorter average trip length or which might be especially sensitive to reduction in employment. A careful reinterpretation of one of the key outputs from a calibrated spatial interaction model is shown to be consistent with the declining rent gradient expected from Alonso’s theory of land use. Copyright Springer-Verlag 2012

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    Bibliographic Info

    Article provided by Springer in its journal Journal of Geographical Systems.

    Volume (Year): 14 (2012)
    Issue (Month): 4 (October)
    Pages: 357-387

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    Handle: RePEc:kap:jgeosy:v:14:y:2012:i:4:p:357-387

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    Web page: http://www.springerlink.com/link.asp?id=103079

    Related research

    Keywords: Commuting; Journey-to-work; Spatial interaction; R12; R23;

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    References

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    1. Giuliano, Genevieve & Small, Kenneth A., 1993. "Is the Journey to Work Explained by Urban Structure?," University of California Transportation Center, Working Papers qt2ss7x5b1, University of California Transportation Center.
    2. Patricia L Mokhtarian & Gustavo O Collantes & Carsten Gertz, 2004. "Telecommuting, residential location, and commute-distance traveled: evidence from State of California employees," Environment and Planning A, Pion Ltd, London, vol. 36(10), pages 1877-1897, October.
    3. Yingling Fan & Asad Khattak & Daniel Rodríguez, 2011. "Household Excess Travel and Neighbourhood Characteristics," Urban Studies, Urban Studies Journal Limited, vol. 48(6), pages 1235-1253, May.
    4. Kang‐Rae Ma & David Banister, 2006. "Excess Commuting: A Critical Review," Transport Reviews, Taylor & Francis Journals, vol. 26(6), pages 749-767, May.
    5. Sunhee Sang & Morton O’Kelly & Mei-Po Kwan, 2011. "Examining Commuting Patterns," Urban Studies, Urban Studies Journal Limited, vol. 48(5), pages 891-909, April.
    6. Hamilton, Bruce W, 1982. "Wasteful Commuting," Journal of Political Economy, University of Chicago Press, vol. 90(5), pages 1035-51, October.
    7. Wheaton, William C., 1974. "Linear programming and locational equilibrium : The Herbert-Stevens model revisited," Journal of Urban Economics, Elsevier, vol. 1(3), pages 278-287, July.
    8. Hamilton, Bruce W, 1989. "Wasteful Commuting Again," Journal of Political Economy, University of Chicago Press, vol. 97(6), pages 1497-1504, December.
    9. Mark W Horner, 2002. "Extensions to the concept of excess commuting," Environment and Planning A, Pion Ltd, London, vol. 34(3), pages 543-566, March.
    10. T J Grigg, 1984. "Probabilistic versions of the short-run Herbert - Stevens model," Environment and Planning A, Pion Ltd, London, vol. 16(6), pages 715-732, June.
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    Cited by:
    1. Niedzielski, Michael A. & Horner, Mark W. & Xiao, Ningchuan, 2013. "Analyzing scale independence in jobs-housing and commute efficiency metrics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 58(C), pages 129-143.

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