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Predicting Arrival Delays: An Application of Spatial Analysis

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  • Diana, Tony

Abstract

Analysts have many tools available to forecast delays. However, spatial analysis does not readily come to mind when predicting delays. Based on the case study of Newark Liberty International airport (EWR), this study proposes to illustrate the potential application of prevalent geostatistical techniques to delay forecast. Arguably, there is a high degree of dependence among delays in a space or neighborhood defined by hour of operation and by day. Local spatial autocorrelation statistics can help determine how delays in a space are autocorrelated to other ones. Among the other spatial analytical techniques, kriging enables the interpolation of delay estimates at unobserved spaces based on the values at observed spaces. Error estimates can be mapped to define spatial patterns (spaces where delays are likely to be more intense). Finally, spatial error regression provides a method for analysts to test the reliability of their findings when spatial dependency in errors is not taken into account.

Suggested Citation

  • Diana, Tony, 2010. "Predicting Arrival Delays: An Application of Spatial Analysis," 51st Annual Transportation Research Forum, Arlington, Virginia, March 11-13, 2010 207260, Transportation Research Forum.
    • Handle: RePEc:ags:ndtr10:207260
    DOI: 10.22004/ag.econ.207260
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    References listed on IDEAS

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    1. Daraban, Bogdan & Fournier, Gary M., 2008. "Incumbent responses to low-cost airline entry and exit: A spatial autoregressive panel data analysis," Research in Transportation Economics, Elsevier, vol. 24(1), pages 15-24.
    2. Aisling Reynolds-Feighan, 2007. "Competing Networks, Spatial and Industrial Concentration in the US Airline Industry," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(3), pages 237-257.
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