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The latest bike-share systems enable users to monitor cycle availability and docking station spaces via near real-time online maps. These websites often specify and supply an applications pro- gramming interface (API) for external software developers to ac- cess the underlying data. In addition, a number of system operators release datasets pertaining to individual journeys made over a particular time period. Both types of data offer insights in the usage of particular bike-shares and provide a ready basis for utilisation in transport research. A small number of previous stud- ies have been undertaken and generally concern the characteristics of a single city’s system, often with a focus on user demographics. Jensen et al. (2010), for example, analysed 11.6 million journeys of the Vélo’v bicycle sharing system in Lyon, constructing a map showing the likely flows of the bicycles across the city. Several characteristics emerged; namely greatly enhanced usage during public transport strikes, and variations in average speeds through the day such as for example, a small but significant increase in speed just before 9 a.m. as cycle commuters hurry to complete their journeys before the start of normal working hours. One intriguing result was that the average speed during the morning commute was greatest on Wednesdays, the authors conjecturing that this was due to a greater proportion of users on Wednesdays being men, due to the tradition of at-home childcare by women on this day.