交通拥堵是诸多交通问题中出现最多、范围最广、影响最大的问题，解决此问题的关键是对交通拥堵的判别与分析。文章从传统交通流理论与机器学习两方面综述了交通拥堵判别的方法，前者通过指标、宏观基本图、元胞自动机、元胞传输以及双流模型等模型，运用物理学与数学的理论描述交通行为特性，模型合理简单、有明确的物理意义，但限制条件较多；后者通过概率图模型、机器学习模型，建立的模型实用准确、不受固定结构约束。文中结合具体的模型方法探讨了不同拥堵判别方法的研究思路、解决方案以及存在问题，对现有的传统交通流理论方法和机器学习方法进行总结，并指出未来的发展方向。

Traffic congestion is the most frequent, wide-ranging and influential problem among all the traffic problems. The key to this problem is to identify and analyze traffic congestion. This paper reviewed the methods of traffic congestion identification from the perspectives of traditional traffic flow theory and machine learning. Traditional traffic flow theory adopts models such as indicators, MFD, cellular automata, CTM and dual-flow models, using the theory of physics and mathematics to describe the traffic behavior characteristics. The models are reasonable and simple, with clear physical meaning and also with many restrictions. The probabilistic graphical model and machine learning model are practical and not constrained by fixed structures. This paper discussed the research ideas, solutions and existing problems of different congestion identification methods by combining the specific model methods. It summarized the existing traditional traffic flow theory methods and machine learning methods, and pointed out the future development direction.