Censored regression analysis of multiclass passenger demand data subject to joint capacity constraints

Abstract

In most passenger transportation systems, demand for seats is not recorded after all spaces for a particular trip have been sold out or after a booking limit has been reached. Thus historical booking data is comprised of ticketsales notdemand — a condition known as censorship of the data. Data censorship is particularly complex when there are multiple classes of demand since the demand in one class can influence the degree of censorship in another. This paper examines the problem of simultaneously estimating passenger demand models for two or more correlated classes of demand that are subject to a common capacity constraint. It is shown that theEM method of Dempster et al. [5] can be adapted to provide maximum likelihood estimates of the parameters of the demand model under these circumstances. The problem of modelling demand for airline flights is discussed as a typical example of this estimation problem. Numerical examples show that, with reasonable sample sizes, it is possible to obtain good estimates even when 75% or more of the data have been censored.