Work

SwissRailNet: Four representations of a spatially embedded railway network dataset

Year: 2025

Type: data-paper

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.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.112266

Authors: Martin Sterchi, Emily Raubach, Lorenz Hilfiker

Institutions: FHNW University of Applied Sciences and Arts Northwestern Switzerland, University of Bern

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,220