Regional Patch Detection of Road Traffic Network

Abstract EN

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.