Forecasting ridership for a new mode using binary stated choice data - methodological challenges in studying the demand for high-speed rail in Norway

Abstract

We discuss some methodological challenges and pitfalls when binary stated choice data is used to predict the potential ridership of new alternatives. Three challenges can be distinguished: (i) the appropriate translation of group scale parameters into nest parameters; (ii) the use of binary data for a full mode choice model; (iii) the use of stated choices involving transport modes which do not currently exist. The paper first critically examines the approach chosen in the market analysis underlying the official HSR-assessment study in Norway (Atkins 2012, Jernbaneverket 2012) and suggests more sophisticated methods, in particular joint RP/SP models with a cross-nested logit specification for a theoretically better justified forecasting procedure. We then examine this model estimation problem using similar data, providing some new insights regarding estimation of group scale parameters and their potential use in the construction of forecasting models for new alternatives that might be correlated with existing alternatives, using revealed preference and binary stated choice data.