为精确识别常发性拥堵传播路径，分析其传播机理，以达到疏导拥堵源头，阻断传播路径的目的，提出一种基于出租车GPS数据的拥堵传播机理研究方法。首先，在城市路网时空立方体数据模型框架下，采用车辆轨迹数与速度指标识别交通拥堵区域，基于常发性拥堵的相对时空稳定性，提出分时段的常发性交通拥堵网格识别方法；其次，建立拥堵时空传播树，针对交通拥堵传播的动态性，提出以频率加权的频繁传播关系集挖掘方法，构建频繁拥堵传播子树；再次，引入动态贝叶斯网络，通过贝叶斯估计进行参数学习，获取拥堵传播概率；最后，以西安市南二环路东段区域为例，运用所提出的方法进行实证分析，探讨拥堵传播路径及其概率。研究结果表明：基于时空立方体模型，采用车辆轨迹数与行程速度指标共同识别各时间帧内常发性拥堵网格，为拥堵传播机理的准确分析奠定了基础；利用STC算法构建拥堵传播树，提出考虑拥堵传播在时间上复现性特征的频繁项集挖掘方法，用以重构频繁拥堵传播子树、明确常发性拥堵传播路径；基于动态贝叶斯网络量化分析网格间拥堵传播可能性，为动态寻找拥堵传播网络中的关键路段，科学合理的制定缓堵方案及任务时序提供理论依据。

In order to accurately identify the propagation path of recurrent congestion and analyze its propagation mechanism to alleviate the traffic congestion at the source and block the propagation path, this study proposed a method of analyzing the congestion propagation mechanism based on taxi GPS data. Firstly, the number of vehicle trajectories and travel speed were used to identify the traffic congestion area based on the space-time cube model of urban road network. According to the relative spatio-temporal stability of recurrent congestion, a time-section recognition method of recurrent traffic congestion grid was proposed. Secondly, the spatio-temporal congestion propagation trees was constructed. Aiming at the dynamics of traffic congestion propagation, a method of mining frequency-weighted recurrent propagation relation set was proposed to construct recurrent congestion propagation subtrees. Thirdly, the Dynamic Bayesian Network was introduced to obtain the congestion propagation probability through Bayesian estimation. Finally, taking the eastern section of the South Second Ring Road in Xi'an as an example, the proposed method was used to conduct an empirical analysis to explore the congestion propagation path and its probability. The research results show that based on the space-time cube model, the recurrent congestion grids in each time frame identified by the number of vehicle trajectories and travel speed lay the foundation for the accurate analysis of the congestion propagation mechanism. The congestion propagation trees constructed by using the STC algorithm, and the proposed frequent itemsets mining method considering temporal reproducibility characteristics of congestion propagation can be used to reconstruct the recurrent congestion propagation subtrees and clarify the propagation path of recurrent congestion. The possibility of congestion propagation between grids was analyzed based on the Dynamic Bayesian Network. It provides a theoretical basis for dynamically finding the key segment in the congestion propagation network, scientifically and reasonably formulating the congestion alleviation scheme and the task timeline.