Despite the general consensus in transport research community that model calibration and validation are necessary to enhance model predictive performance, there exist significant inconsistencies in the literature. This is primarily due to a lack of consistent definitions, and a unified and statistically sound framework. In this paper, we provide a general and rigorous formulation of the model calibration and validation problem, and highlight its relation to statistical inference. We also conduct a comprehensive review of the steps and challenges involved, as well as point out inconsistencies, before providing suggestions on improving the current practices. This paper is intended to help the practitioners better understand the nature of model calibration and validation, and to promote statistically rigorous and correct practices. Although the examples are drawn from a transport research background - and that is our target audience - the content in this paper is equally applicable to other modelling contexts.

翻译：尽管交通研究界普遍认为模型校准与验证是提升模型预测性能的必要环节，但现有文献中仍存在显著的不一致性。这主要源于缺乏统一定义，以及缺少统一且具有统计严谨性的框架。本文提出了模型校准与验证问题的一般性严格形式化表述，并阐明了其与统计推断的关联。我们系统梳理了相关步骤与挑战，指出了现有方法中的不一致性，并就改进当前实践提出了建议。本文旨在帮助实践者更深入理解模型校准与验证的本质，并推广具有统计严谨性的正确实践方法。虽然案例源自交通研究背景（且该领域是我们的目标读者群），但本文内容同样适用于其他建模场景。