Mining bicycle sharing data for generating insights into sustainable transport systems

Bicycle sharing systems (bike-shares) are becoming increasingly popular in towns and cities around the world. They are viewed as a cheap, efficient, and healthy means of navigating dense urban environments. This paper is the first to take a global view of bike-sharing characteristics by analysing data from 38 systems located in Europe, the Middle East, Asia, Australasia and the Americas. To achieve this, an extensive database depicting the geographical location and bicycle occupancy of each docking station within a particular system has been created over a number of years to chart the usage in the chosen systems (and others) and provide a consistent basis on which to compare and classify them. Analysis of the variation of occupancy rates over time, and comparison across the system's extent, infers the likely demographics and intentions of user groups. A classification of bike-shares, based on the geographical footprint and diurnal, day-of-week and spatial variations in occupancy rates, is proposed. The knowledge of such patterns and characteristics identifiable from the dataset has a range of applications, including informing operators and policymakers about the maintenance of a suitable balance of bicycles throughout the system area (a nontrivial problem for many bike-shares), the location of new docking stations and cycle lanes, and better targeting of promotional materials to encourage new users. Within the context of transport research, the systems utilised here are part of relatively small, closed environments that can be more easily modelled and validated. Such work lays foundations for the analysis of larger scale transport systems by creating a classification of the different systems and seeks to demonstrate that bike-shares have a lot to offer both as an effective method of transport and a rich source of data.