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High resolution accessibility computations

In: Accessibility and Spatial Interaction

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  • Thomas W. Nicolai
  • Kai Nagel

Abstract

Accessibility is a concept which either looks at how easy it is to reach a certain location from many other locations, or how easy it is to reach other locations or opportunities from a given starting point. This chapter looks at the latter, discussing that this is a quantity that can be defined separately for every point (x,y) in space, rather than treating accessibility as uniform within, say, zones. As a result, accessibility can also be seen as a continuous field A(x,y) in the two-dimensional environment. The chapter then continues to discuss how A(x,y) can be efficiently computed for regional scenarios. The approach combines interpolation of values computed on a grid with fast shortest-path tree computations and information caching for repeated sub-computations of the same quantities, using the econometric logsum term as an example of a possible indicator of accessibility. A Zurich scenario needs about two minutes of computing time on a regular desktop computer in order to compute A(x,y) at a resolution of 100 m x 100 m. As a sensitivity study, workplace accessibility maps are given for free speed car, congested car, bicycle and walking. One can for example observe that accessibility by bicycle is similar to congested car accessibility within the urban area, while it is worse outside and considerably worse when compared to free speed car transport. Similarly, walking accessibility is similar to bicycle and congested car transport in the innermost urban core, but considerably worse everywhere else.

Suggested Citation

  • Thomas W. Nicolai & Kai Nagel, 2014. "High resolution accessibility computations," Chapters, in: Ana Condeço-Melhorado & Aura Reggiani & Javier Gutiérrez (ed.), Accessibility and Spatial Interaction, chapter 4, pages 62-91, Edward Elgar Publishing.
  • Handle: RePEc:elg:eechap:15267_4
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    References listed on IDEAS

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    1. Gutiérrez, Javier & Condeço-Melhorado, Ana & Martín, Juan Carlos, 2010. "Using accessibility indicators and GIS to assess spatial spillovers of transport infrastructure investment," Journal of Transport Geography, Elsevier, vol. 18(1), pages 141-152.
    2. M. Rieser & K. Nagel, 2008. "Network breakdown “at the edge of chaos” in multi-agent traffic simulations," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 63(3), pages 321-327, June.
    3. Vandenbulcke, Grégory & Steenberghen, Thérèse & Thomas, Isabelle, 2009. "Mapping accessibility in Belgium: a tool for land-use and transport planning?," Journal of Transport Geography, Elsevier, vol. 17(1), pages 39-53.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    5. Juan Carlos Martín & Aura Reggiani, 2007. "Recent Methodological Developments to Measure Spatial Interaction: Synthetic Accessibility Indices Applied to High‐speed Train Investments," Transport Reviews, Taylor & Francis Journals, vol. 27(5), pages 551-571, February.
    6. Bono, Flavio & Gutiérrez, Eugenio, 2011. "A network-based analysis of the impact of structural damage on urban accessibility following a disaster: the case of the seismically damaged Port Au Prince and Carrefour urban road networks," Journal of Transport Geography, Elsevier, vol. 19(6), pages 1443-1455.
    7. M E Frost & N A Spence, 1995. "The Rediscovery of Accessibility and Economic Potential: The Critical Issue of Self-Potential," Environment and Planning A, , vol. 27(11), pages 1833-1848, November.
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    Cited by:

    1. Kuehnel, Nico & Ziemke, Dominik & Moeckel, Rolf & Nagel, Kai, 2020. "The end of travel time matrices: Individual travel times in integrated land use/transport models," Journal of Transport Geography, Elsevier, vol. 88(C).

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