Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

中文翻译：

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道路交通网络区域补丁检测

道路交通网络（RTN）结构在复杂网络分析领域起着重要作用。本文通过复杂网络的社区检测，提出了一种基于RTN的区域补丁检测方法。首先，结合位置属性权重，地理距离权重和道路等级权重的影响因素，采用改进的Adapted PageRank算法，计算RTN中关键节点的候选排名结果。其次，将排序结果和最短路径距离作为两个重要的影响因素，用于选择RTN的关键点，然后将Adapted K-Means算法应用于RTN的区域补丁检测。最后，基于张屋市道路交通网络的实验数据，分析结果如下：彰武分为9个功能结构，以关键节点位置为核心。区域补丁结构按关键点划分，RTN实际上划分为9个小型功能社区。九个功能性的区域补丁构成了新的网络结构，维持区域补丁之间的连通性可以提高RTN的整体效率。