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Estimating traveler populations at airport and cruise terminals for population distribution and dynamics

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  • Warren Jochem
  • Kelly Sims
  • Edward Bright
  • Marie Urban
  • Amy Rose
  • Phillip Coleman
  • Budhendra Bhaduri

Abstract

In recent years, uses of high-resolution population distribution databases are increasing steadily for environmental, socioeconomic, public health, and disaster-related research and operations. With the development of daytime population distribution, temporal resolution of such databases has been improved. However, the lack of incorporation of transitional population, namely business and leisure travelers, leaves a significant population unaccounted for within the critical infrastructure networks, such as at transportation hubs. This paper presents two general methodologies for estimating passenger populations in airport and cruise port terminals at a high temporal resolution which can be incorporated into existing population distribution models. The methodologies are geographically scalable and are based on, and demonstrate how, two different transportation hubs with disparate temporal population dynamics can be modeled utilizing publicly available databases including novel data sources of flight activity from the Internet which are updated in near-real time. The airport population estimation model shows great potential for rapid implementation for a large collection of airports on a national scale, and the results suggest reasonable accuracy in the estimated passenger traffic. By incorporating population dynamics at high temporal resolutions into population distribution models, we hope to improve the estimates of populations exposed to or at risk to disasters, thereby improving emergency planning and response, and leading to more informed policy decisions. Copyright US Government 2013

Suggested Citation

  • Warren Jochem & Kelly Sims & Edward Bright & Marie Urban & Amy Rose & Phillip Coleman & Budhendra Bhaduri, 2013. "Estimating traveler populations at airport and cruise terminals for population distribution and dynamics," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(3), pages 1325-1342, September.
    • Handle: RePEc:spr:nathaz:v:68:y:2013:i:3:p:1325-1342
    DOI: 10.1007/s11069-012-0441-9
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    References listed on IDEAS

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    1. Kim, Wonkyu & Park, Yonghwa & Jong Kim, Byung, 2004. "Estimating hourly variations in passenger volume at airports using dwelling time distributions," Journal of Air Transport Management, Elsevier, vol. 10(6), pages 395-400.
    2. Neil M. Ferguson & Derek A. T. Cummings & Christophe Fraser & James C. Cajka & Philip C. Cooley & Donald S. Burke, 2006. "Strategies for mitigating an influenza pandemic," Nature, Nature, vol. 442(7101), pages 448-452, July.
    3. Neil M. Ferguson & Derek A.T. Cummings & Simon Cauchemez & Christophe Fraser & Steven Riley & Aronrag Meeyai & Sopon Iamsirithaworn & Donald S. Burke, 2005. "Strategies for containing an emerging influenza pandemic in Southeast Asia," Nature, Nature, vol. 437(7056), pages 209-214, September.
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    Cited by:

    1. Pujun Liang & Wei Xu & Yunjia Ma & Xiujuan Zhao & Lianjie Qin, 2017. "Increase of Elderly Population in the Rainstorm Hazard Areas of China," IJERPH, MDPI, vol. 14(9), pages 1-17, August.

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