Speaker
Description
Modern public transport networks (PTNs) are not simply physical conduits for movement; they are vital infrastructures that shape socio-economic opportunities and influence the quality of urban life. Despite this significance, notable service gaps still exist between urban centers and peripheral or rural regions, which in turn intensify socio-economic inequalities, limit access to essential opportunities, and risk disrupting balanced regional development. Conventional studies based on OD (Origin-Destination) data or those that focus solely on physical connectivity cannot fully capture the actual service gaps emerging in multimodal PTNs, where rail, bus, air, and sea transport all operate concurrently. Furthermore, analytical approaches that rely on administrative boundaries often fail to account for diverse geographical and socio-economic contexts, making meaningful comparisons across areas of varying size and jurisdiction problematic.
In response to these shortcomings, this study proposes an “operational capacity-based multiplex network analysis” that models a nationwide multimodal PTN using 500 m × 500 m grid cells in which people actually reside and utilize public transport services, rather than arbitrary administrative units or individual station points. Through the concept of “connectivity strength,” which integrates both service frequency and transport capacity, the approach moves beyond the traditional notion of accessibility to more accurately capture the actual level of service experienced by users. Building on this framework, a nationwide PTN network was constructed in GTFS format (TMAP data) for rail, bus, air, and sea modes, enabling complex intermodal interactions to be assessed from the perspective of actual user transfers. Long-distance (inter-city) and short-distance (intra-city) travel were distinguished in order to calculate key graph-theoretic indicators such as centrality, connectivity, and accessibility, revealing structural vulnerabilities in different regions and transport modes. Socio-economic data, including population, employment, and POIs, were then integrated to identify service imbalances that remain invisible under a purely physical connectivity lens. Finally, a comparison between a “Level 1” physical network and a “Level 2” operational capacity-based network revealed low-frequency or low-capacity areas struggling with significant service shortfalls, leading to the derivation of a composite vulnerability index via TOPSIS and the identification of priority areas for improvements and investments.
The results showed that many areas perceived as physically well-connected suffered severe service vulnerabilities due to limited capacities and low service frequencies. This finding suggests that OD-based or simple connectivity-centric PTN planning alone may not effectively safeguard mobility equity or balanced development. By contrast, the proposed “service-oriented” multiplex approach highlights structural gaps rooted in heavy reliance on specific rural segments or transfer hubs, indicating potential routes for innovative policymaking in multimodal public transport. Moreover, this data-driven framework furnishes practical insights into promoting balanced regional growth and improving mobility equity, while offering valuable guidance for strategic enhancements of public transport services. It also holds promise for broader application in user-centric, service-focused PTN planning and assessment models in the future.
References
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Keywords | Multimodal Public Transport;Multiplex Network;Urban data science;Big Data; Spatial accessibility;Vulnerability |
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Best Congress Paper Award | Yes |