Autonomous vehicles use sensing and communication technologies to navigate safely and efficiently with little or no input from the driver. These driverless technologies will create an unprecedented revolution in how people move, and policymakers will need appropriate tools to plan for and analyze the large impacts of novel navigation systems. In this paper we derive semiparametric estimates of the willingness to pay for automation. We use data from a nationwide online panel of 1,260 individuals who answered a vehicle-purchase discrete choice experiment focused on energy efficiency and autonomous features. Several models were estimated with the choice microdata, including a conditional logit with deterministic consumer heterogeneity, a parametric random parameter logit, and a semiparametric random parameter logit. We draw three key results from our analysis. First, we find that the average household is willing to pay a significant amount for automation: about $3,500 for partial automation and $4,900 for full automation. Second, we estimate substantial heterogeneity in preferences for automation, where a significant share of the sample is willing to pay above $10,000 for full automation technology while many are not willing to pay any positive amount for the technology. Third, our semiparametric random parameter logit estimates suggest that the demand for automation is split approximately evenly between high, modest and no demand, highlighting the importance of modeling flexible preferences for emerging vehicle technology.

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This paper introduces the package gmnl in R for estimation of multinomial logit models with unobserved heterogeneity across individuals for cross-sectional and panel (longitudinal) data. Unobserved heterogeneity is modeled by allowing the parameters to vary randomly over individuals according to a continuous, discrete, or discrete-continuous mixture distribution, which must be chosen a priori by the researcher. In particular, the models supported by gmnl are the multinomial or conditional logit, the mixed multinomial logit, the scale heterogeneity multinomial logit, the generalized multinomial logit, the latent class logit, and the mixed-mixed multinomial logit. These models are estimated using either the maximum likelihood estimator or the maximum simulated likelihood estimator. This article describes and illustrates with real databases all functionalities of gmnl, including the derivation of individual conditional estimates of both the random parameters and willingness-to-pay measures.

The relationship between train occupancy, comfort and perceived security is analysed, using data from a survey and stated choice (SC) study of users of Santiago's Metro (subway) system. Mode choice models where crowding is one of the main explanatory variables are estimated and crowding multipliers to measure its relevance on travel time disutility for sitting and standing are computed. An international comparison with previous studies from London, Paris, Singapore and Sweden is presented. The type of estimated models include Multinomial Logit, Mixed Logit, and Latent Class models. Results show that there is signicant heterogeneity in crowding perception across the population. Users classes with low and high crowding multipliers are identied, in which gender, age and income play a role. In the SC survey, occupancy levels were shown with three alternative forms of representation (text, 2D diagram or photo), however we did not nd relevant inuences of the dierent forms of representation on crowding perception.

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