Forecasting campground demand in US national parks

William L. Rice, So Young Park, Bing Pan, Peter Newman

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


Camping has grown from a recreational activity to an emerging tourism sector. In America's national parks, this growth is amplified by increasing visitation and an occupancy limited by a mission to preserve the nation's natural wonders. Forecasting future demand for campsites can not only aid administrators’ resource allocation, efficient management, and effective communication, but also provide valuable information to campers as they plan their vacations. This manuscript explores the unique nature of campground administration and tests a variety of forecasting methods to identify which best lends itself to the distinctive behavior of camping tourists and the unique nature of campsites. An in-depth study of five popular campgrounds finds an ensemble model most accurate prediction model. This article also launches the Annals of Tourism Research Curated Collection on Tourism Demand Forecasting, a special selection of research in this field.

Original languageEnglish
Pages (from-to)424-438
Number of pages15
JournalAnnals of Tourism Research
StatePublished - Mar 2019


  • Campground
  • Camping
  • Demand forecasting
  • Machine learning
  • National park
  • Neural network autoregression
  • k-nearest neighbors


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