london_bikes — London bicycle sharing trips (2014)

Description

A network of the trips made with the London bicycle sharing scheme (Barclays Cycle Hire, now Santander Cycles) between 22 June and 31 July 2014, as compiled by Munoz-Mendez et al. from the Transport for London (TfL) Open Data. Each node is a docking station, and each directed edge is a trip, going from the station where a bicycle was checked out to the station where it was returned (trips that return to the same station are self-loops), so that the network is a multigraph with 1,469,851 trips between 750 stations.

The network has the following properties:

Nodes: station_id, the TfL identifier of the station; name, the name of the station; lat and lon, its geographic coordinates (NaN for the few stations missing from the station list in the source repository, which was obtained from the TfL API some time after the trips).
Edges: rental_id, the identifier of the rental; bike_id, the identifier of the bicycle; start and end, the times of check-out and return (Unix timestamps, from London local times given to the minute); duration, the duration of the trip in seconds.

Tags

Transportation Travel Multigraph Timestamps Metadata

Citation

F. Munoz-Mendez, K. Han, K. Klemmer, S. Jarvis, "Community Structures, Interactions and Dynamics in London's Bicycle Sharing Network." Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp '18), 1015-1023 (2018) 10.1145/3267305.3274156 Sci-Hub

Upstream URL OK

https://github.com/konstantinklemmer/bikecommclust

Networks

Tip: hover over a column header 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
london_bikes	750	1,469,851	1959.80	3187.48	399.56	5.56	0.35	0.87	3	1.00	Directed	Unipartite	station_id name lat lon	rental_id bike_id start end duration	13.8 MiB	20.8 MiB	20.1 MiB	21.7 MiB