In many transportation systems, a mismatch between the associated design and planning decisions and the demand is typically encountered. A tailored system is not only appealing to operators, which could have a better knowledge of their operational costs, but also to users, since they would benefit from an increase in the level of service and satisfaction. Hence, it is important to explicitly allow for the interactions between the two in the model governing the decisions of the system. Discrete choice models (DCM) provide a disaggregate demand representation that is able to capture the impact on the behavior of these decisions by taking into account the heterogeneity of tastes and preferences of the users, as well as subjective aspects related to attitudes or perceptions. Despite their advantages, the demand expressions derived from DCM are non-linear and non-convex in the explanatory variables, which restricts their integration in optimization problems. In this paper, we overcome the probabilistic nature of DCM by relying on simulation in order to specify the demand directly in terms of the utility functions (instead of the choice probabilities). This allows us to define a mixed-integer linear formulation that characterizes the preference structure and the behavioral assumption of DCM, which can then be embedded in a mixed-integer linear programming (MILP) model. We provide an overview of the extent of the framework with an illustrative MILP model that is designed to solve a profit maximization problem of a parking services operator. The obtained results show the potential of the proposed methodology to adjust supply-related decisions to the users.

Behavioral models, Combinatorial optimization, Disaggregate demand, User-centric transportation planning,
Transportation Research. Part B: Methodological
Department of Econometrics

Pacheco Paneque, M. (Meritxell), Bierlaire, M. (Michel), Gendron, B. (Bernard), & Sharif Azadeh, S. (2021). Integrating advanced discrete choice models in mixed integer linear optimization. Transportation Research. Part B: Methodological, 146, 26–49. doi:10.1016/j.trb.2021.02.003