交通拥堵是道路网络中总车流量大于道路承载量，导致交通流无法畅行的现象。基于交通流数据挖掘的拥堵模式对于解决道路交通拥堵问题具有重要的现实意义。然而，现有的研究工作未能对"交通拥堵"进行合理准确的定义，忽略了交通流数据本身具有的时空属性和交通拥堵概念本身的模糊性。该文首先将模糊集理论引入到交通拥堵的定义中，提出用模糊隶属度衡量交通拥堵程度；其次，在传统空间并置模式挖掘的基础上加入时间属性，提出时空并置模糊拥堵模式的概念；再次，在该概念的基础上提出了挖掘时空并置模糊拥堵模式的有效方法；最后，在实际数据集上对提出的方法进行了广泛的实验评估。实验结果表明：该方法在挖掘结果上优于现有方法。
Traffic congestion occurs when the total traffic volume on the road network exceeds the road capacity which disrupts normal traffic flow. The congestion patterns in traffic datasets need to be mined to effectively address urban traffic congestion problems. However, existing research work has failed to reasonably and accurately define "traffic congestion" and ignores the spatio-temporal attributes of the traffic flow data and the fuzziness of the traffic congestion concept. This paper presents the concept of spatio-temporal co-location fuzzy congestion patterns by introducing fuzzy set theory into the definition of traffic congestion to measure the degree of traffic congestion. The algorithm also adds the time attribute to the traditional spatial co-location pattern mining. Two algorithms are then presented for mining spatio-temporal co-location fuzzy congestion patterns. The methods are evaluated using real traffic datasets with the results showing that the methods provide better mining results than the existing methods.
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