The formulation of hybrid discrete choice models, including both observable alternative attributes and latent variables associated with attitudes and perceptions, has become a topic of discussion once more. To estimate models integrating both kinds of variables, two methods have been proposed: the sequential approach, in which the latent variables are built before their integration with the traditional explanatory variables in the choice model and the simultaneous approach, in which both processes are done together, albeit with a sophisticated but fairly complex treatment. Here both approaches are applied to estimate hybrid choice models by using two data sets: one from the Santiago Panel (an urban mode choice context with many alternatives) and another consisting of synthetic data. Differences between both approaches were found as well as similarities not found in earlier studies. Even when both approaches result in unbiased estimators, problems arise when valuations are obtained such as the value of time for forecasting and policy evaluation.

Agent-based models have gained wide acceptance in transportation planning because with increasing computational power, large-scale people-centric mobility simulations are possible. Several modeling efforts have been reported in the literature on the demand side (with sophisticated activity-based models that focus on an individual’s day activity patterns) and on the supply side (with detailed representation of network dynamics through simulation-based dynamic traffic assignment models). This paper proposes an extension to a state-of-the-art integrated agent-based demand and supply model—SimMobility—for the design and evaluation of autonomous vehicle systems. SimMobility integrates various mobility-sensitive behavioral models in a multiple time-scale structure comprising three simulation levels: ( a) a long-term level that captures land use and economic activity, with special emphasis on accessibility; ( b) a midterm level that handles agents’ activities and travel patterns; and ( c) a short-term level that simulates movement of agents, operational systems, and decisions at a microscopic granularity. In that context, this paper proposes several extensions at the short-term and midterm levels to model and simulate autonomous vehicle systems and their effects on travel behavior. To showcase these features, the first-cut results of a hypothetical on-demand service with autonomous vehicles in a car-restricted zone of Singapore are presented. SimMobility was successfully used in an integrated manner to test and assess the performance of different autonomous vehicle fleet sizes and parking station configurations and to uncover changes in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.