The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

城市交通状态的分类及其应用是智能交通系统（ITS）的重要组成部分，它不仅可以帮助交通管理者掌握交通状况，分析交通拥堵状况，还可以为旅行者提供更多的交通信息，并帮助他们避免拥塞。因此，准确的交通状态分类方法对于城市交通管理将是非常实用的。这项研究的主要目的是使用机器学习方法（即FCM聚类方法，并且可以从相应的聚类标签确定分类结果）对城市交通状况进行分类。用这种方法，开发了两个部分。首先，提出了一种新的分类指标，即路网的充足程度，它将组成一个综合的分类指标系统，并包含其他参数，例如交通流量，速度和占用率。然后，从模糊隶属度函数改进和样本加权处理两个方面对传统的模糊c均值（FCM）聚类方法进行了改进，这些改进可以增强聚类性能。结果，开发了一种改进的机器学习方法（即改进的FCM聚类方法），并用于对真实交通流数据进行聚类分析。接下来，以上海为例，对研究过程进行了指导，包括数据处理，聚类分析和方法比较。其他方法（例如，支持向量机（SVM）方法，决策树方法，介绍了k最近邻（KNN）方法和传统的FCM聚类方法，以与改进的FCM聚类方法进行比较。讨论表明了该方法的优越性（例如，与传统的FCM聚类方法相比，改进方法的目标函数值降低了31.11％，并且聚类中心误差也呈下降趋势），并且它优于其他方法在分类性能上（例如，与传统的FCM方法，SVM方法，决策树方法和KNN方法相比，改进的FCM方法的整体分类精度分别提高了10.10％，5.45％，30.92％和35.66％）。此外，NMI，ROC曲线结果还说明了改进的FCM方法相对于其他方法的优越性。