Community Extraction Method of Road Networks Based on Betweenness Contribution

Abstract

In this study, we address the problem of detecting effective installation sites of signboards and identifying their influential zones for a given road network, under the setting that many residents can view them on their shortest paths to the destinations. To this end, based on a notion of group-betweenness centrality measure, we newly formalize this problem as a k-betweens problem and propose a community extraction method of road networks to identify the influential zones. In an existing method, the influential zone of each signboard is extracted by Voronoi tessellation against its installation site, which assumes that residents view the nearest signboard. In our proposed method, it is extracted by the proportion including the signboard on the shortest paths from the resident's departure point to various destinations. From experimental evaluations using artificial and real road networks, we confirmed that our method can extract effective installation sites of signboards such as intersection nodes near the entrance of the express highway, and their influential zones as communities according to the positional relationships with arterial roadways. Furthermore, by computing the degree of antagonism among communities using the entropy of betweenness contribution rates, we can quantify the effectiveness of installing the same signboards for residents of areas where the influential zones of multiple signboards overlap.