R/Birmingham.R

In AirportProblems: Analysis of Cost Allocation for Airport Problems

#' Birmingham Airport data
#'
#' The data includes several variables describing 13,572 aircraft operations — 6,876 take-offs and 6,876 landings —
#' involving 11 different aircraft types at Birmingham Airport during the period 1968–1969.
#'
#' @usage Birmingham
#'
#' @format A data frame with 11 rows and 9 columns (variables):
#' \describe{
#' \item{type}{Aircraft type}
#' \item{i}{Aircraft index (subscript)}
#' \item{eta}{Number of movements}
#' \item{p}{Average fee per movement (in pounds)}
#' \item{d}{Estimated diversion cost per movement (in pounds)}
#' \item{b}{Average benefit per movement (in pounds)}
#' \item{l}{Runway user index}
#' \item{c}{Maintenance cost per movement (in pounds)}
#' \item{g}{Assumed capital cost (in pounds)}
#' }
#'
#' @source Littlechild, S. C. and Thompson, G. F. (1977). Aircraft landing fees: a game theory approach.
#' \emph{The Bell Journal of Economics}, 8, 186-204.
#' 
#' @examples
#' # Based on capital cost (g)
#' G <- multiclonesrules(Birmingham$g, Birmingham$eta, c("SEC", "CEC", "SM"), 
#' group_contribution = FALSE, agents_names = Birmingham$i, labels = FALSE)
#' (cost_allocation <- round(G, 2))
#' 
#' # Based on capital cost (g) + add maintenance cost per movement (c)
#' sweep(cost_allocation, MARGIN = 2, Birmingham$c, FUN = "+")
"Birmingham"