Improving the understanding and prediction of human behaviour over time.

开发准确的行为模型是许多学科为决策和政策制定提供信息的核心挑战。考虑到这一点，已经实施了不同的方法来根据环境对人类行为进行建模。例如，在交通研究领域，以行为理论为基础的建模技术估计可以捕捉到结果之间的因果机制，但这些方法没有充分利用数据的丰富性。相比之下，在数学中，更复杂、更稳健的结构可能具有更高的预测能力，但缺乏行为理论的基础。在此背景下，本研究旨在通过将一些处理纵向数据的数学方法纳入交通研究中通常使用的选择建模框架中，以提供一个更好的工具来评估随时间推移的行为结果，以指定具有更好预测能力的高质量模型。

Developing accurate behavioural models is a core challenge in many disciplines to inform decision and policy making. Considering this, diverse approaches have been implemented to model human behaviour according to the context. For instance, in the field of transport research, modelling techniques grounded in behavioural theories are estimated to capture the causal mechanisms between outcomes, but these methods do not fully exploit the richness of the data. In contrast, in mathematics, more complex and robust structures might have higher prediction capabilities but lack foundation in behavioural theories. In this context, the research aims to provide a better toolkit to evaluate behavioural outcomes over time by incorporating some of the mathematical methods to deal with longitudinal data into choice modelling frameworks typically used in transportation research, to specify high quality models with better forecasting capabilities.