Table of Contents

Handbook of Choice Modelling

Handbook of Choice Modelling

Elgar original reference

Edited by Stephane Hess and Andrew Daly

Choice modelling is an increasingly important technique for forecasting and valuation, with applications in fields such as transportation, health and environmental economics. For this reason it has attracted attention from leading academics and practitioners and methods have advanced substantially in recent years. This Handbook, composed of contributions from senior figures in the field, summarises the essential analytical techniques and discusses the key current research issues. It will be of interest to academics, students and practitioners in a wide range of areas.

Chapter 13: Capturing alternative decision rules in travel choice models: a critical discussion

Caspar G. Chorus

Subjects: economics and finance, environmental economics, transport, environment, environmental economics, transport, urban and regional studies, transport


Since its inception 40 years ago (McFadden, 1974), the discrete choice paradigm has grown to become one of the most used paradigms for the study of travel demand (for example, Small and Verhoef, 2007; Ortuzar and Willumsen, 2011). Not only has the transportation field served as a very fertile application area for choice modellers; the travel demand research community has over the years provided a range of important contributions to the discrete choice modelling toolbox as well (for example, Small and Rosen, 1981; Ben-Akiva and Lerman, 1985; Brownstone and Train, 1998; Walker and Ben-Akiva, 2002; Bhat, 2005). When inspecting the literature on discrete (travel) choice modelling, it becomes clear that some aspects of choice modelling have over the years received much more attention in our field than other aspects. For example, numerous research efforts have aimed at deriving realistic error term distributions – that is, distributions that imply realistic correlation structures and substitution patterns (for example, Ben-Akiva, 1974; McFadden, 1978; Small, 1987; McFadden and Train, 2000). As another example, during the past decade, a rapidly growing interest has emerged in embedding ‘psychological factors’ such as – latent – attitudes, in travel choice models. See Van Acker et al. (2011), Prato et al. (2012) and Chorus (2012a) for recent discussions of this literature. An aspect of discrete (travel) choice models that has received much less attention over the years is the assumed decision rule.

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