Identification of critical areas and stations in multimodal public transportation networks based on community detection

Existing methods for identifying critical communities and key stations in transportation networks 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 improved 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 topological attributes to quantitatively assess the importance of critical communities and key stations. Finally, 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 distributions across different day types. Results indicate that compared with non-working days and holidays, 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 coverage and service flexibility. This study provides theoretical insights and methodological support for the structural optimization and differentiated operational planning of multimodal transportation networks.