Deriving public transport level of service weights from a multiple comparison of latent and observable variables

Perceived importance attribute rankings, obtained from samples of various types of public transport users, are used to determine the relative weights of their level of service vectors. A generalization of a recent linear programming methodology is proposed which requires transforming the ranking data into equivalent pairwise choices. The weights were compared with those obtained from discrete choice models estimated with stated preference data.