Managing the frequent congestion of the road networks of large cities is a major challenge for city management organizations. In order to deal with these phenomena, it is essential to have an in-depth understanding of the processes that lead to the occurrence of congestion and its spilling over into contiguous areas of the city.

The professional literature has a long history of studying the propagation of congestion, but so far the authors have focused on discovering frequent patterns and determining propagation probabilities.

To the best of our knowledge, the Congestion Propagation Modeling Algorithm (CPMA) presented in this article is the first to be able to determine the expected value of propagation times for any propagation pattern as well as propagation probabilities using Markov chains.

Our method was subjected to a detailed performance analysis using a dataset extracted from real road networks, during which we examined how accurately our model determines the propagation times of congestion, which is especially important for municipal traffic management organizations if they want to prevent the propagation of a local congestion and proliferation in the city. According to our measurements, our method can determine the probability of congestion occurring several times a day and the length of congestion propagation times with an accuracy that allows effective intervention by traffic management authorities.

对大城市道路网络的频繁拥堵进行管理是城市管理组织面临的主要挑战。为了处理这些现象，必须对导致拥堵的发生及其蔓延到城市附近地区的过程有深入的了解。

专业文献在研究拥塞传播方面有着悠久的历史，但到目前为止，作者一直致力于发现频繁的模式和确定传播概率。

据我们所知，本文介绍的拥塞传播建模算法（CPMA）是第一个能够确定任何传播模式的传播时间的期望值以及使用马尔可夫链的传播概率的算法。

我们的方法使用了从真实道路网络中提取的数据集进行了详细的性能分析，在此过程中，我们检查了我们的模型确定拥堵传播时间的准确度，这对于市政交通管理组织要防止拥堵的传播特别重要。城市中的局部拥堵和扩散。根据我们的测量，我们的方法可以确定一天发生几次拥塞的可能性以及拥塞传播时间的长度，其精确度允许交通管理部门进行有效干预。