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Use mobile location data to infer airport catchment areas and calibrate Huff gravity model in the New York metropolitan area

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  • Wang, Sen
  • Kong, Ningning Nicole
  • Gao, Yi

Abstract

Mobile location data have emerged as a pivotal asset for analyzing travelers' spatial behaviors and movement patterns. In the context of air travel, the data empower researchers to gain empirical insights into travelers' choices of airports. This study employs mobile location data to scrutinize the market shares and infer catchment areas of three primary hub airports within the New York Metropolitan Area. Our study, together with Teixeira and Derudder (2021), helps contribute to a better understanding of competitive airport dynamics in the New York Metropolitan Area. In addition, the mobile location data allow us to calibrate the two key components of the Huff Gravity Model, which is frequently used in existing studies focusing on airport competition and catchment areas in Multiple Airport Regions (MARs). Our investigation underscores that the application of the Huff Model should not follow a uniform approach across different scenarios. The dynamics of airport competition and ground access alternatives exhibit unique characteristics within each MAR. Furthermore, our study unveils inherent quality challenges associated with mobile location data. Future studies intending to incorporate mobile location data are advised to conduct preliminary assessments of data quality before embarking on empirical analyses.

Suggested Citation

  • Wang, Sen & Kong, Ningning Nicole & Gao, Yi, 2024. "Use mobile location data to infer airport catchment areas and calibrate Huff gravity model in the New York metropolitan area," Journal of Transport Geography, Elsevier, vol. 114(C).
    • Handle: RePEc:eee:jotrge:v:114:y:2024:i:c:s0966692323002624
    DOI: 10.1016/j.jtrangeo.2023.103790
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    References listed on IDEAS

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