An exploratory analysis of the trend in the demand for the London bike-sharing system: From London Olympics to Covid-19 pandemic
•Examined impact of several variables on trend in demand for London bike-sharing system.•Analysed the demand (daily cycle hires) over the period 2012–2020.•As unemployment rate decreases, the demand increases.•As the number of docking stations increases, the demand increases.•Examined how uncertaint...
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Veröffentlicht in: | Sustainable cities and society 2021-06, Vol.69, p.102871, Article 102871 |
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Sprache: | eng |
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Zusammenfassung: | •Examined impact of several variables on trend in demand for London bike-sharing system.•Analysed the demand (daily cycle hires) over the period 2012–2020.•As unemployment rate decreases, the demand increases.•As the number of docking stations increases, the demand increases.•Examined how uncertainty around the demand varies.
It is important to understand variations in the demand for bike-sharing systems to help guide policy aimed at promoting bike-sharing schemes. We conducted an exploratory analysis of the trend in the daily number of cycle hires in London (UK) from January 2012 to June 2020. In particular, we investigated the association between unemployment rate and the trend in the demand for the London cycle hire system. Other explanatory variables included in our analysis were weather-related (e.g., rainfall) and temporal factors (e.g., types of days) as well as the number of docking stations. The study employed a generalised negative binomial model in which the over-dispersion parameter varied across the sample. Using such a model for the first time in investigating the trend in bike-sharing systems, allowed us to understand how uncertainty around the demand varies in the system. We found that unemployment rate, being negatively associated with cycle hires, played an important role in the demand for the London bike-sharing system. Unlike temperature and the number of docking stations, rainfall, humidity and wind had a negative impact on cycle hires. Finally, our analysis revealed significant variations in the demand across various types of days (e.g. lockdown and weekdays). |
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ISSN: | 2210-6707 2210-6715 |
DOI: | 10.1016/j.scs.2021.102871 |