Journal of choice modelling 

About: Journal of choice modelling is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Discrete choice & Choice set. It has an ISSN identifier of 1755-5345. Over the lifetime, 345 publications have been published receiving 8631 citations. The journal is also known as: JOCM.

Discrete Choice Experiments Are Not Conjoint Analysis

Abstract: We briefly review and discuss traditional conjoint analysis (CA) and discrete choice experiments (DCEs), widely used stated preference elicitation methods in several disciplines. We pay particular attention to the origins and basis of CA, and show that it is generally inconsistent with economic demand theory, and is subject to several logical inconsistencies that make it unsuitable for use in applied economics, particularly welfare and policy assessment. We contrast this with DCEs that have a long-standing, well-tested theoretical basis in random utility theory, and we show why and how DCEs are more general and consistent with economic demand theory. Perhaps the major message, though, is that many studies that claim to be doing conjoint analysis are really doing DCE.

Route choice modeling: past, present and future research directions

Abstract: Modeling route choice behavior is problematic, but essential to appraise travelers' perceptions of route characteristics, to forecast travelers' behavior under hypothetical scenarios, to predict future traffic conditions on transportation networks and to understand travelers' reaction and adaptation to sources of information. This paper reviews the state of the art in the analysis of route choice behavior within the discrete choice modeling framework. The review covers both choice set generation and choice process, since present research directions show growing interest in understanding the role of choice set size and composition on model estimation and flow prediction, while past research directions illustrate larger efforts toward the enhancement of stochastic route choice models rather than toward the development of realistic choice set generation methods. This paper also envisions future research directions toward the improvement in amount and quality of collected data, the consideration of the latent nature of the set of alternatives, the definition of route relevance and choice set efficiency measures, the specification of models able to contextually account for taste heterogeneity and substitution patterns, and the adoption of random constraint approaches to represent jointly choice set formation and choice process.

Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application

Abstract: The community of choice modellers has expanded substantially over recent years, covering many disciplines and encompassing users with very different levels of econometric and computational skills. This paper presents an introduction to Apollo, a powerful new freeware package for R that aims to provide a comprehensive set of modelling tools for both new and experienced users. Apollo also incorporates numerous post-estimation tools, allows for both classical and Bayesian estimation, and permits advanced users to develop their own routines for new model structures.

EM algorithms for nonparametric estimation of mixing distributions

Abstract: This paper describes and implements three computationally attractive procedures for nonparametric estimation of mixing distributions in discrete choice models. The procedures are specific types of the well known EM (Expectation-Maximization) algorithm based on three different ways of approximating the mixing distribution nonparametrically: (1) a discrete distribution with mass points and frequencies treated as parameters, (2) a discrete mixture of continuous distributions, with the moments and weight for each distribution treated as parameters, and (3) a discrete distribution with fixed mass points whose frequencies are treated as parameters. The methods are illustrated with a mixed logit model of households' choices among alternative-fueled vehicles.

Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information

Abstract: We show how to combine statistically efficient ways to design discrete choice experiments based on random utility theory with new ways of collecting additional information that can be used to expand the amount of available choice information for modeling the choices of individual decision makers. Here we limit ourselves to problems involving generic choice options and linear and additive indirect utility functions, but the approach potentially can be extended to include choice problems with non-additive utility functions and non-generic/labeled options/attributes. The paper provides several simulated examples, a small empirical example to demonstrate proof of concept, and a larger empirical example based on many experimental conditions and large samples that demonstrates that the individual models capture virtually all the variance in aggregate first choices traditionally modeled in discrete choice experiments.