Accommodating taste heterogeneity in railway passenger choice models based on internet booking data

Abstract

This paper presents an application of advanced econometric techniques to railway passenger choices in the context of revenue management. Three modeling approaches; multinomial logit, latent class, and mixed logit models are applied to ticket purchase timing decisions and to three market segments. The analysis, based on internet booking data with limited individual variables, investigates heterogeneous characteristics of passenger behavior across railway markets that differ by the length of haul.The results quantify the importance of fare, advanced booking, departure time of day, and day of week in purchase timing decision. It shows that mixed logit model provides the best statistical fit for the long and medium distance markets, while the latent class model provides the best statistical fit for the short distance market. On the other hand, the latent class model is found to be superior to mixed logit model in term of prediction capability. Results also show that segmenting price sensitivity by booking period is more appropriate for this choice model application than segmenting by socioeconomic information. This research contributes to existing literature on revenue management by demonstrating that complex demand models successfully recover random heterogeneity when limited socio-demographic information is available. The results, coupled with an optimization algorithm, can be used by railway operators to support revenue management policies such as fare pricing or seat allocation.