Identifying the Passenger Transport Corridors in an Urban Rail Transit Network Based on OD Clustering

Traditional passenger transport corridor identification methods fail to effectively capture the spatiotemporal dynamic characteristics of passenger flows in complex urban rail transit networks. This study proposes a novel passenger transport corridor identification method based on Origin–Destination...

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Vydáno v:Sustainability Ročník 17; číslo 20; s. 9127
Hlavní autoři: Zhou, Fangyi, Yao, Jing, Yin, Haodong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2025
Témata:
Algorithms
Behavior
Bibliometrics
Clustering
Efficiency
Forecasts and trends
Identification
Light rail transit
Literature reviews
Passengers
Planning
Public transportation
Route optimization
Similarity measures
Traffic assignment
Travelers
Urban transportation
China
ISSN:2071-1050, 2071-1050
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Shrnutí:Traditional passenger transport corridor identification methods fail to effectively capture the spatiotemporal dynamic characteristics of passenger flows in complex urban rail transit networks. This study proposes a novel passenger transport corridor identification method based on Origin–Destination (OD) clustering. The method enables more accurate identification of passenger groups with similar travel patterns and distributions through a customized clustering similarity function; simultaneously, it can obtain OD pairs with actual physical significance through OD clustering as the source of basic units for identifying passenger transport corridors. By analyzing the spatial distribution of passenger transport corridor constituent units (clustered ODs), the method determines whether the passenger transport corridor is a cross-line corridor. The method is validated using Beijing’s urban rail transit system as a case study, employing the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm with optimal parameters (eps = 0.46, minpts = 980), identifying 21 clusters and ultimately determining six passenger transport corridors, including four cross-line and two non-cross-line types. Furthermore, this study conducted sensitivity analysis on the eps parameter using 80 test configurations to examine its impact on clustering effectiveness metrics, validating the method’s stability. The results demonstrate that the identified corridors exhibit high passenger flow concentration characteristics and accurately reflect passengers’ transfer demands between different lines. This research provides a theoretical foundation for integrated public transportation connectivity and supports sustainable urban development through improved operational efficiency and reduced operational costs.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17209127