The problem of modeling individual decisions repeatedly over time is important to study dynamics in travel behavior, changes in preferences, and adoption of new services or technologies. Although different methods have been proposed to account for dynamics in pure discrete choice contexts, the case where both continuous and discrete decision variables evolve dynamically has not been fully solved. In this paper, we develop a methodology for the problem of vehicle ownership and usage in a finite time horizon, but the formulation is general and can be easily transferred to other contexts where discrete–continuous decision variables are modeled in time. The model specification is based on a recursive binary probit model that maximizes instantaneous and future utility components of discrete choices, and on a linear regression that models the continuous decision variable. Empirical results are obtained using simulated data; validation tests attest that trends in demand are correctly recovered.

中文翻译：

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离散和连续变量随时间的建模序列，并应用于车辆所有权和使用问题

随着时间的推移对个人决策重复建模的问题对于研究旅行行为的动态、偏好的变化以及新服务或技术的采用非常重要。尽管已经提出了不同的方法来解释纯离散选择上下文中的动态，但连续和离散决策变量都动态演化的情况尚未完全解决。在本文中，我们为有限时间范围内的车辆所有权和使用问题开发了一种方法，但该公式是通用的，可以轻松转移到其他上下文，其中离散-连续决策变量及时建模。模型规范基于递归二进制概率模型，该模型最大化离散选择的瞬时和未来效用分量，以及对连续决策变量建模的线性回归。使用模拟数据获得实证结果；验证测试证明需求趋势已正确恢复。