トポロジカル表現に基づいた地理的道路網 に対するネットワーク中心性の分析

• Published in: 人工知能学会論文誌 Vol. 40; no. 1; pp. A-O62_1 - 11
• Main Authors: 熊野, 雅仁, 齊藤, 真希, 木村, 昌弘
• Format: Journal Article
• Language: Japanese
• Published: 一般社団法人 人工知能学会 01.01.2025
• Subjects: critical streets; evacuation; road network analysis; salience; topological representation
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.40-1_A-O62

Aiming at enhancing geographical road network analysis from a network science perspective, we introduce a novel problem of analyzing the road network in a city, and consider providing a new network centrality metric that could be useful for that problem. The problem addresses vehicular evacuation in urban settings. During emergency and disaster scenarios of vehicular evacuation, the shortest distance routes might not always be the most optimal. Instead, routes that are easier to traverse can be more crucial, even if they involve detours. Also, destinations for evacuation do not necessarily have to be restricted to traditional facilities; broad and well-maintained streets might also serve as suitable alternatives. We focus on the streets as the basic units of the road network to be investigated, and consider a scenario in which people efficiently move from starting intersections around their current places to designated goal streets, following the routes of easiest traversal. For the road network, we employ its topological representation, where vertices and edges correspond to streets and intersections between them, respectively. We thus represent the road network as a vertex-weighted graph, where the weight of each vertex reflects its ease of traversal. By appropriately extending the recently developed edge-centrality metric, “salience”, to this vertex-weighted graph, we construct a new network centrality metric to detect critical streets for the newly introduced problem. Using a toy model of road network and real-world urban road networks obtained from OpenStreetMap, we experimentally reveal its distinctive characteristics by comparing it with several baselines. Moreover, we demonstrate that the proposed network centrality metric can successfully find critical streets for vehicular evacuation, which are difficult to detect using baseline methods.