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In this article, heterogeneity of transit users in perceiving service quality is investigated. Users' perceptions of transit services are heterogeneous for many reasons: the qualitative nature of some service aspects, the different users' socioeconomic characteristics, the diversity in tastes and attitudes towards transit. In this research, heterogeneity is treated through a mixed logit model with a non-parametric distribution of the coefficients, allowing the asymmetry in user perception heterogeneity to be considered. Although the results presented apply to the specific population in the sample, represented by university students, the proposed methodology is general and transferable to other case studies.

Researchers and analysts are increasingly using mixed logit models for estimating responses to forecast demand and to determine the factors that affect individual choices. However the numerical cost associated to their evaluation can be prohibitive, the inherent probability choices being represented by multidimensional integrals. This cost remains high even if Monte Carlo or quasi-Monte Carlo techniques are used to estimate those integrals. This paper describes a new algorithm that uses Monte Carlo approximations in the context of modern trust-region techniques, but also exploits accuracy and bias estimators to considerably increase its computational efficiency. Numerical experiments underline the importance of the choice of an appropriate optimisation technique and indicate that the proposed algorithm allows substantial gains in time while delivering more information to the practitioner. Copyright Springer-Verlag Berlin/Heidelberg 2006Maximum simulated likelihood estimation, Trust-region algorithms, Monte Carlo samplings, Mixed logit models,

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