A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

Short- and long-term effects of COVID-19 on bicycle sharing usage

“…The number of citizens travelling or commuting in the city is an important determinant of BSS demand ( El-Assi et al, 2017 , Morton et al, 2021 ). Under normal circumstances, demand does not change dramatically across weeks, but the very fast spread of working from home (while 5.4% of the employees worked from home in Budapest in 2019, it was 21.3% in 2020 according to the Hungarian Central Statistical Office), the restrictive government measures, and the fear of the pandemic caused sizeable differences in this regard.…”

Evolvement patterns of usage in a medium-sized bike-sharing system during the COVID-19 pandemic

“…Comparing the result with shared scooter usage, the authors showed that bike-sharing users were highly sensitive to gas prices and special events, and demonstrated higher sensitivity to weather conditions than scooter users. Along the similar line, Morton et al. (2021) investigated features of the built environment, demographics, and system characteristics of bike-sharing in London, and found that higher usage of shared bikes can be contributed by better accessibility to rail stations, higher proportion of male and Caucasian residents, and greater capacity of docking stations.…”

Geographic Information System and Atomized Transportation Modes

“…Open-data policies have further accelerated burgeoning research in this area. Scholars have addressed many issues such as the impact of the COVID-19 pandemic [30], effects of the weather on bike-sharing ridership [31,32], analyzing the spatial demand of bikesharing [33], forecasting bike-sharing demand [34,35], and the impact of subway closure on bike-sharing [36,37]. Yet, these data are generally available at an origin-destination level, rather than the GPS trajectory for each journey.…”