swissrailnet — Swiss railway network, four representations (2025)

Description

Network representations of the Swiss railway network, built by M. Sterchi, E. Raubach and L. Hilfiker from the open data of Swiss public transport (opentransportdata.swiss): the timetable of all scheduled stops on Wednesday, March 5, 2025 ("Actual Data"), the list of service points valid on that day, and the numbers of passengers boarding and alighting at each station. The authors define four representations; in all of them, the nodes are the same 1,626 train stations (including a few stations in Italy and Germany operated by Swiss companies), and they differ in the definition of the edges:

space_of_changes: a directed edge from station $i$ to station $j$ if some train serves $j$ after $i$ on the same trip, so that one can travel from $i$ to $j$ without changing trains. The edges are weighted by the number of such connections on that day and their average duration.
space_of_stops: a directed edge from station $i$ to station $j$ if some train stops at $j$ immediately after $i$ (a non-stop connection), with the same weights.
space_of_stations: an undirected edge between geographically adjacent stations, i.e. stations next to each other along the railway tracks. It was derived from the non-stop connections and validated manually: 11 edges, e.g. of long tunnels and high-speed lines served without stop, were added, and 11 edges between stations that are not adjacent were removed.
space_of_stations_raw: the same as space_of_stations, but also including the 11 edges that were removed manually, which the authors published in a separate file, marked by an edge property.
temporal: every individual connection of the day, as in space_of_changes but without aggregation: a directed edge from $i$ to $j$ for each train trip that serves $j$ after $i$, with the time of departure from $i$ and the duration of the journey. There are many parallel edges.

The temporal network also has 83 self-loops, the connections of trips that return to their station (at two stations, presumably on loop lines), which do not appear in the aggregated networks. Times are rounded to the minute; connections taking less than a minute have a duration of 0. The dataset has two versions on figshare (July and October 2025), which contain the same nodes and edges; the second adds the node property traffic_valid, and its files are used here.

The networks have the following properties:

Nodes: name, the name of the station; bpuic, its official number (Swiss/European station code); canton and municipality, where it is located (empty for the stations abroad); company, the company responsible for it; latitude, longitude and elevation (m), its position; avg_daily_traffic, avg_daily_traffic_weekdays and avg_daily_traffic_weekends, the average daily numbers of railway passengers boarding and alighting at the station (over all days, Monday to Friday, and weekends and public holidays; NaN where not available; the value 49 indicates an unreliable measurement); traffic_valid, whether these numbers are available and reliable.
Edges of space_of_changes and space_of_stops: num_connections, the number of trains per day connecting the two stations; avg_duration, the average duration of the connection (minutes).
Edges of space_of_stations and space_of_stations_raw: distance_geodesic, the geodesic distance between the two stations (km); distance_exact, the distance along the tracks (km), available only for the lines of the Swiss Federal Railways (NaN otherwise); manually_added, whether the edge was added manually; in space_of_stations_raw also manually_removed, whether the edge was removed manually (and is therefore absent from space_of_stations).
Edges of temporal: time, the departure time (Unix time; the times run from midnight of March 5, 2025, Central European Time, into the morning of March 6); start, the departure time in minutes after midnight of March 5, as given in the data; duration, the duration of the connection (minutes).

Only the space_of_stations and space_of_stations_raw networks are laid out as spatial networks (without the partition of the stochastic block model).

Tags

Transportation Public Transport Spatial Weighted Multigraph Timestamps Metadata

Citation

M. Sterchi, E. Raubach, L. Hilfiker, "SwissRailNet: Four representations of a spatially embedded railway network dataset." Data in Brief 63, 112266 (2025) 10.1016/j.dib.2025.112266 Sci-Hub
M. Sterchi, E. Raubach, L. Hilfiker, "Four representations of the Swiss railway network." figshare, dataset, version 2 (2025) 10.6084/m9.figshare.29609615.v2 Sci-Hub

Upstream URL 403

https://doi.org/10.6084/m9.figshare.29609615
https://opentransportdata.swiss/en/

Upstream license

Creative Commons Attribution 4.0 International (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
space_of_changes	1,626	37,945	23.34	38.67	49.76	67.09	0.11	0.54	7	0.98	Directed	Unipartite	name bpuic canton municipality company latitude longitude elevation avg_daily_traffic avg_daily_traffic_weekdays avg_daily_traffic_weekends traffic_valid	num_connections avg_duration	168 KiB	320 KiB	318 KiB	270 KiB
space_of_stops	1,626	4,211	2.59	3.38	4.74	571.96	0.27	0.12	45	0.98	Directed	Unipartite	name bpuic canton municipality company latitude longitude elevation avg_daily_traffic avg_daily_traffic_weekdays avg_daily_traffic_weekends traffic_valid	num_connections avg_duration	95 KiB	127 KiB	124 KiB	109 KiB
space_of_stations	1,626	1,709	2.10	0.56	1.74	5071.46	0.06	0.01	141	0.98	Undirected	Unipartite	name bpuic canton municipality company latitude longitude elevation avg_daily_traffic avg_daily_traffic_weekdays avg_daily_traffic_weekends traffic_valid	distance_geodesic distance_exact manually_added	93 KiB	118 KiB	116 KiB	104 KiB
temporal	1,626	1,110,766	683.13	1484.03	49.76	84.14	0.15	0.60	7	0.98	Directed	Unipartite	name bpuic canton municipality company latitude longitude elevation avg_daily_traffic avg_daily_traffic_weekdays avg_daily_traffic_weekends traffic_valid	time start duration	843 KiB	4.0 MiB	3.5 MiB	3.9 MiB
space_of_stations_raw	1,626	1,720	2.12	0.59	1.80	3445.25	0.10	0.01	116	0.98	Undirected	Unipartite	name bpuic canton municipality company latitude longitude elevation avg_daily_traffic avg_daily_traffic_weekdays avg_daily_traffic_weekends traffic_valid	distance_geodesic distance_exact manually_added manually_removed	93 KiB	118 KiB	116 KiB	105 KiB

Ridiculograms^*

space_of_changes drawing — space_of_changes

space_of_stations drawing — space_of_stations

space_of_stations_raw drawing — space_of_stations_raw

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.