Application of Social Network Analysis in Transportation Network Based on AIS Data

This article aims to explore ways to improve the efficiency and management level of maritime transportation by studying the key nodes in the Bohai Bay maritime transportation network and their impact on the network structure. Using the container ship data of the Bohai Bay in the first half of 2018, the Bohai Bay is divided into a 100×100 grid, with the ship trajectory points in the grid as nodes, the ship tracks as edges, and the frequency of the tracks as the weight of the edges. Build a network. The community detection method is used to identify the top four communities with the largest number of nodes, and the z-P matrix is used to divide the nodes in each community into different roles. In the experiment, the top 10 nodes in weighted degree, closeness centrality, and betweenness centrality, as well as provincial hubs and connector hubs were deleted respectively, and the average clustering coefficient and average path length of the community and the entire network before and after deletion of key nodes were analyzed. The change. The study found that the impact of deletion of key nodes on different communities and the entire network is significantly different, providing theoretical support and practical guidance for maritime traffic management.