SwissRailNet: Four representations of a spatially embedded railway network dataset
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Author (Corporation)
Publication date
2025
Type of student thesis
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Type
01A - Journal article
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Data in Brief
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Volume
63
Issue / Number
112266
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Publisher / Publishing institution
Elsevier
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Abstract
Public transport networks are frequently studied in the field of network science, including applications in epidemic modeling, infrastructure optimization, and machine learning. This paper introduces four representations of a Swiss railway network. The dataset is derived from open data sources of Swiss public transport companies and is the result of several data processing steps as well as a manual validation. In all four representations, nodes represent train stations, embedded in geographic space, making the network an example of a spatial network influenced by physical constraints such as mountains and lakes. The edge definitions vary, however. Edges connect (1) any pair of nodes with a train connection for which no train change is required, (2) any pair of nodes with a non-stop train connection, and (3) any pair of nodes that correspond to physically adjacent stations. The fourth representation corresponds to a temporal network in which each edge is attributed with the start and duration of a train connection.
Keywords
Public transport, Spatial networks, Static networks, Temporal networks
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ISSN
2352-3409
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
Open access category
Gold
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Citation
Sterchi, M., Raubach, E., & Hilfiker, L. (2025). SwissRailNet: Four representations of a spatially embedded railway network dataset. Data in Brief, 63(112266). https://doi.org/10.1016/j.dib.2025.112266