Community Structures, Interactions and Dynamics in London's Bicycle Sharing Network

Abstract

Bikesharing schemes are transportation systems that not only provide an efficient mode of transportation in congested urban areas, but also improve last-mile connectivity with public transportation and local accessibility. Bikesharing schemes around the globe generate detailed trip data sets with spatial and temporal dimensions, which, with proper mining and analysis, reveal valuable information on urban mobility patterns. In this paper, we study the London bicycle sharing dataset to explore community structures. Using a novel clustering technique, we derive distinctive behavioural patterns and assess community interactions and spatio-temporal dynamics. The analyses reveal self-contained, interconnected and hybrid clusters that mimic London's physical structure. Exploring changes over time, we find geographically isolated and specialized communities to be relatively consistent, while the remaining system exhibits volatility, especially during and around peak commuting times. By increasing our understanding of the collective behaviour of the bikesharing users, this analysis supports policy appraisal, operational decision-making and motivates improvements in infrastructure design and management.

Effects of Free-Floating Shared Bicycles on Urban Public Transportation

“…The free-floating shared bicycle is a type of new public transportation, and the research on free-floating shared bicycles is limited. Some research on station-based shared bicycles is referred to in this study [7][8][9][10][11]. Some scholars have tried to analyze the usage of shared bicycles from the perspective of a time series [9,10,[12][13][14].…”

“…characteristics of shared bicycles, some studies have identified different purposes of cyclists, such as commuting and recreational usage [7,11,15]. Some studies have proposed the spatial-temporal clustering of bicycles to explore the collective behavior of bike sharing users [8,16,17]. It has been demonstrated that the analyses of the characteristics of shared bicycle trips are helpful for making decisions on the allocation and dispatching of shared bicycles [18][19][20].However, shared bicycles, as an alternative mode of public transportation [21], are embedded in the space where the original public transportation is located.…”

“…J. Geo-Inf. 2019,8, 323 11 of Spatial distribution of the number of OD points around the subway and bus stations.…”

“…J. Geo-Inf. 2019,8, 323 13 of Classification of the subway and bus stations according to the sink-source characteristics of the shared bicycles.…”

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

“…Fernando Munoz-Mendez et al, 2018 used 1,459,945 ordered O-D pairs from the London station-based BSS from June to July in 2014 and showed that bike usage reflected rush hours. Compared to daytime trajectories, night trajectories were irregular and significantly decreased in number [20]. Employing O-D data pairs from station-based BSS during several months in 2013 and 2014 in Chicago, Zhou et al, 2015 identified and verified clusters where shared bikes are densely used [21].…”