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The Rational Locator Reexamined

Author

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  • Yao Wu
  • David Levinson

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

The Rational Locator Hypothesis posits that individuals can, if they choose, maintain approximately steady journey-to-work travel times by adjusting their home and workplace. This hypothesis was coupled with the observation of long-term stability in drive alone journey-to-work times in metropolitan Washington (those times were unchanged from 1957 through 1968 to 1988). Despite the increase of average commuting distance and congestion, trip duration remained constant or even declined when controlling for travel purpose and travel mode because of shifting a share of traffic from slow urban routes to faster suburban routes. This observation has significance, as it is important to know for travel demand analysis if there is an underlying budget, or even a regularity, as this helps us determine whether our forecasts are reasonable. To retest the underlying rationale for the hypothesis: that travel times are stable, both intra-metropolitan and inter-metropolitan comparisons of travel times are made. The intra-metropolitan analysis compared Washington DC data from 1968, 1988, and 1994, and Twin Cities data from 1990 and 2000. The results depend upon geography. For the larger Washington DC region, keeping the same geography shows little change in commute times, but using the larger 1994 area suggests an increase in commute times.However, the Twin Cities, starting from a much shorter commute time, shows a marked increase over the decade, using either the smaller or the larger geography. To explain the differences between the two areas, an inter-metropolitan analysis conducts a series of regressions on mean metropolitan travel time for the 65 largest metropolitan areas in theUnited States. The average commute time varies (positively) in these cities as a function of congestion and population density-both significant at the 99 percent confidence interval.Geographical area, population, and income were also significant at the 90 percent confidence interval. Despite the continuing observation of stability in drive alone commuting times in metropolitan Washington, we reject the theory of personal commuting budgets, as we find that not only are commuting times not generally stable over time at the intra-metropolitan area, but that commuting time clearly depends on metropolitan spatial structure.

Suggested Citation

  • Yao Wu & David Levinson, 2005. "The Rational Locator Reexamined," Working Papers 200503, University of Minnesota: Nexus Research Group.
  • Handle: RePEc:nex:wpaper:rationallocatorreexamined
    DOI: http://dx.doi.org/doi:10.1007/s11116-004-5507-4
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    File URL: http://hdl.handle.net/11299/179926
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    References listed on IDEAS

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    Cited by:

    1. António Ferreira & Peter Batey, 2011. "On Why Planning Should Not Reinforce Self-Reinforcing Trends: A Cautionary Analysis of the Compact-City Proposal Applied to Large Cities," Environment and Planning B, , vol. 38(2), pages 231-247, April.
    2. Rafael Henrique Moraes Pereira & Tim Schwanen, 2013. "Commute Time in Brazil (1992-2009): Differences Between Metropolitan Areas, by Income Levels and Gender," Discussion Papers 1813a, Instituto de Pesquisa Econômica Aplicada - IPEA.
    3. Mohíno, Inmaculada & Ureña, José M. & Solís, Eloy, 2016. "Transport infrastructure and territorial cohesion in rural metro-adjacent regions: A multimodal accessibility approach. The case of Castilla-La Mancha in the context of Madrid (Spain)," Journal of Transport Geography, Elsevier, vol. 57(C), pages 115-133.
    4. Ma, Kang-Rae & Kang, Eun-Taek, 2011. "Time–space convergence and urban decentralisation," Journal of Transport Geography, Elsevier, vol. 19(4), pages 606-614.
    5. Chunil Kim & Choongik Choi, 2019. "Towards Sustainable Urban Spatial Structure: Does Decentralization Reduce Commuting Times?," Sustainability, MDPI, vol. 11(4), pages 1-28, February.
    6. Longden, Thomas, 2016. "The Regularity and Irregularity of Travel: an Analysis of the Consistency of Travel Times Associated with Subsistence, Maintenance and Discretionary Activities," ETA: Economic Theory and Applications 243150, Fondazione Eni Enrico Mattei (FEEM).
    7. Hao Wu & Paolo Avner & Genevieve Boisjoly & Carlos K. V. Braga & Ahmed El-Geneidy & Jie Huang & Tamara Kerzhner & Brendan Murphy & Michał A. Niedzielski & Rafael H. M. Pereira & John P. Pritchard & A, 2022. "Urban access across the globe: an international comparison of different transport modes," Working Papers 2021-01, University of Minnesota: Nexus Research Group.
    8. Vale, David S., 2013. "Does commuting time tolerance impede sustainable urban mobility? Analysing the impacts on commuting behaviour as a result of workplace relocation to a mixed-use centre in Lisbon," Journal of Transport Geography, Elsevier, vol. 32(C), pages 38-48.
    9. Duranton, Gilles & Puga, Diego, 2015. "Urban Land Use," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 467-560, Elsevier.
    10. Mengying Cui & David Levinson, 2020. "Multi-Activity Access: How Activity Choice Affects Opportunity," Working Papers 2022-01, University of Minnesota: Nexus Research Group.
    11. Longden, Thomas, 2016. "The Regularity and Irregularity of Travel: an Analysis of the Consistency of Travel Times Associated with Subsistence, Maintenance and Discretionary Activities," ET: Economic Theory 243150, Fondazione Eni Enrico Mattei (FEEM).
    12. 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.
    13. Marc Barthelemy, 2016. "A global take on congestion in urban areas," Environment and Planning B, , vol. 43(5), pages 800-804, September.
    14. David Levinson, 2022. "Optimum Stop Spacing for Accessibility," Working Papers 2021-08, University of Minnesota: Nexus Research Group.
    15. Sweet, Matthias N., 2014. "Do firms flee traffic congestion?," Journal of Transport Geography, Elsevier, vol. 35(C), pages 40-49.
    16. Hao Wu & David Levinson, 2018. "Optimum Stop Spacing for Accessibility," Working Papers 171, University of Minnesota: Nexus Research Group.
    17. Amlan Banerjee & Xin Ye & Ram Pendyala, 2007. "Understanding Travel Time Expenditures Around the World: Exploring the Notion of a Travel Time Frontier," Transportation, Springer, vol. 34(1), pages 51-65, January.
    18. Dissanayake, Dilum, 2017. "Watching the clock on the way to work? Analysing trends in commuting activities, modes and gender differences in commute times, using hazard-based duration modelling methods," Journal of Transport Geography, Elsevier, vol. 65(C), pages 188-199.
    19. Wang, Donggen & Chai, Yanwei & Li, Fei, 2011. "Built environment diversities and activity–travel behaviour variations in Beijing, China," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1173-1186.
    20. António Ferreira & Els Beukers & Marco Te Brömmelstroet, 2012. "Accessibility is Gold, Mobility is Not: A Proposal for the Improvement of Dutch Transport-Related Cost-Benefit Analysis," Environment and Planning B, , vol. 39(4), pages 683-697, August.
    21. Martin P. Brosnan & David Levinson, 2014. "Accessibility and the Allocation of Time: Changes in Travel Behavior 1990-2010," Working Papers 000120, University of Minnesota: Nexus Research Group.
    22. Edmund J Zolnik, 2011. "The Effect of Sprawl on Private-Vehicle Commuting Outcomes," Environment and Planning A, , vol. 43(8), pages 1875-1893, August.
    23. Joly, I., 2011. "Test of the relation between travel and activities times : different representations of a demand derived from activity participation," Working Papers 201103, Grenoble Applied Economics Laboratory (GAEL).
    24. Wesley E. Marshall & Eric Dumbaugh, 2020. "Revisiting the relationship between traffic congestion and the economy: a longitudinal examination of U.S. metropolitan areas," Transportation, Springer, vol. 47(1), pages 275-314, February.

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    JEL classification:

    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • D10 - Microeconomics - - Household Behavior - - - General
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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