This paper analyzes the choice behavior of the high-speed rail Italian passengers, with a random utility latent class structure for modelling travelers' tastes. Latent class structures are widely used for addressing inter/intra-respondent taste variation in transport field. The simple Multinomial Logit (MNL) is commonly used as a kernel model (in the sense it will be clarified in the paper) into such latent class structures. The resulting random utility model (RUM) is well known in the literature as latent class Multinomial Logit (LC-MNL). In this study, we propose to test kernel models with different inherent distributional assumptions for random residuals of the utilities than that of the simple Multinomial Logit (MNL), namely the Gumbel distribution assumption (Extreme Value type I). Such different kernel RUMs are obtained by assuming random residuals of the utilities distributed as: Weibull (i.e. Extreme Value-type III), Exponential, Rayleigh, Pareto and q-Gumbel. Results on an estimation exercise, based on a dataset coming from a stated preference (SP) survey, show that some of these assumptions allow the resulting LC-RUM to outperform the well-known LC-MNL in terms of goodness of fit (GOF)

Evaluating the choice behaviour of high-speed rail passengers in Italy: A latent class structure with alternative kernel models to the Multinomial Logit

Tinessa F.;Simonelli F.;Marzano V.;
2020

Abstract

This paper analyzes the choice behavior of the high-speed rail Italian passengers, with a random utility latent class structure for modelling travelers' tastes. Latent class structures are widely used for addressing inter/intra-respondent taste variation in transport field. The simple Multinomial Logit (MNL) is commonly used as a kernel model (in the sense it will be clarified in the paper) into such latent class structures. The resulting random utility model (RUM) is well known in the literature as latent class Multinomial Logit (LC-MNL). In this study, we propose to test kernel models with different inherent distributional assumptions for random residuals of the utilities than that of the simple Multinomial Logit (MNL), namely the Gumbel distribution assumption (Extreme Value type I). Such different kernel RUMs are obtained by assuming random residuals of the utilities distributed as: Weibull (i.e. Extreme Value-type III), Exponential, Rayleigh, Pareto and q-Gumbel. Results on an estimation exercise, based on a dataset coming from a stated preference (SP) survey, show that some of these assumptions allow the resulting LC-RUM to outperform the well-known LC-MNL in terms of goodness of fit (GOF)
978-1-7281-7455-6
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11588/890865
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