基于社区检测的多模式公共交通网络关键区域与站点识别OA

Existing methods for identifying critical communities and key stations in transportation net-works often lack unified evaluation criteria and a systematic analytical framework.To address this,this study proposes a hierarchical identification framework based on the Leiden algorithm.First,an im-proved modularity function that integrates both passenger flow and topological features is constructed,systematically incorporating station passenger volumes and network topological characteristics into the Leiden algorithm's community detection process.Second,based on the community detection results,a multi-dimensional evaluation system is developed that simultaneously considers functional and topo-logical attributes to quantitatively assess the importance of critical communities and key stations.Fi-nally,the applicability of the proposed method is validated using real operational data from Beijing's integrated bus-metro network,by comparing variations in community structures and critical node dis-tributions across different day types.Results indicate that compared with non-working days and holi-days,the number of communities on weekdays decreases by 9.05%and 8.59%,respectively,while the number of large-scale communities(with more than 150 nodes)increases by 16.67%and 40%.Overall,community importance is predominantly driven by functional attributes,but weekday net-works exhibit a more balanced interplay between functional and topological significance.Furthermore,bus stations consistently represent a higher proportion of critical nodes due to their superior spatial cov-erage and service flexibility.This study provides theoretical insights and methodological support for the structural optimization and differentiated operational planning of multimodal transportation networks.