Ensemble forecasting is a modeling approach that combines data sources, models of different types, with alternative assumptions, using distinct pattern recognition methods. The aim is to use all available information in predictions, without the limiting and arbitrary choices and dependencies resulting from a single statistical or machine learning approach or a single functional form, or results from a limited data source. Uncertainties are systematically accounted for. Outputs of ensemble models can be presented as a range of possibilities, to indicate the amount of uncertainty in modeling. We review methods and applications of ensemble models both within and outside of transport research. The review finds that ensemble forecasting generally improves forecast accuracy, robustness in many fields, particularly in weather forecasting where the method originated. We note that ensemble methods are highly siloed across different disciplines, and both the knowledge and application of ensemble forecasting are lacking in transport. In this paper we review and synthesize methods of ensemble forecasting with a unifying framework, categorizing ensemble methods into two broad and not mutually exclusive categories, namely combining models, and combining data; this framework further extends to ensembles of ensembles. We apply ensemble forecasting to transport related cases, which shows the potential of ensemble models in improving forecast accuracy and reliability. This paper sheds light on the apparatus of ensemble forecasting, which we hope contributes to the better understanding and wider adoption of ensemble models.

集合预测是一种建模方法，它使用不同的模式识别方法将数据源、不同类型的模型与替代假设相结合。目的是在预测中使用所有可用信息，而没有由单一统计或机器学习方法或单一功能形式或有限数据源导致的限制性和任意选择和依赖性。系统地考虑了不确定性。集成模型的输出可以表示为一系列可能性，以表明建模中的不确定性。我们回顾了交通研究内外的集成模型的方法和应用。综述发现，集合预报普遍提高了许多领域的预报准确性、鲁棒性、特别是在该方法起源的天气预报中。我们注意到集合方法在不同学科中高度孤立，并且在传输方面缺乏集合预测的知识和应用。在本文中，我们回顾并综合了具有统一框架的集合预测方法，将集合方法分为两大类且不相互排斥的类别，即组合模型和组合数据；这个框架进一步扩展到 即组合模型，组合数据；这个框架进一步扩展到 即组合模型，组合数据；这个框架进一步扩展到合奏的合奏。我们将集合预测应用于与运输相关的案例，这显示了集合模型在提高预测准确性和可靠性方面的潜力。本文阐明了集合预报的装置，我们希望它有助于更​​好地理解和更广泛地采用集合模型。