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Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach

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  • Hu, Yujie
  • Wang, Changzhen
  • Li, Ruiyang
  • Wang, Fahui

Abstract

Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.—a geographic unit at which many national datasets including health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.

Suggested Citation

  • Hu, Yujie & Wang, Changzhen & Li, Ruiyang & Wang, Fahui, 2020. "Estimating a large drive time matrix between ZIP codes in the United States: A differential sampling approach," Journal of Transport Geography, Elsevier, vol. 86(C).
    • Handle: RePEc:eee:jotrge:v:86:y:2020:i:c:s0966692319310051
    DOI: 10.1016/j.jtrangeo.2020.102770
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    References listed on IDEAS

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    Cited by:

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    2. Hu, Yujie & Sherlock, Phillip & Huang, Jing & Knopf, Herman T. & Hall, Jaclyn M., 2024. "Unveiling spatial mismatch in childcare supply and demand: An excess commuting analysis of home-to-childcare distance in subsidized families," Journal of Transport Geography, Elsevier, vol. 116(C).

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