Improving the understanding and prediction of human behaviour over time.

随着时间的推移，提高对人类行为的理解和预测。

• 批准号: 2745484
• 金额: --
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2745484
• 关键词: Improving; understanding; prediction; human; behaviour

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.

开发准确的行为模型是许多学科的核心挑战，为决策和政策制定提供信息。考虑到这一点，已经实施了不同的方法来根据上下文模拟人类行为。例如，在运输研究领域，基于行为理论的建模技术估计可以捕捉结果之间的因果机制，但这些方法没有充分利用丰富的数据。相比之下，在数学中，更复杂和更强大的结构可能具有更高的预测能力，但缺乏行为理论的基础。在这种情况下，研究的目的是提供一个更好的工具包，以评估随着时间的推移的行为结果，将一些数学方法来处理纵向数据到选择建模框架通常用于交通研究，指定高质量的模型，更好的预测能力。