Abstract
Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
Highlights
The role of the Smart City is increasingly seen as being one which incorporates technology, sustainability and quality of life, and the Bike Sharing concept fits neatly under that rubric [1], combining, as it does, low-carbon and low–pollution transportation, sensing technologies, shared societal resources and public health benefits
The literature of bicycle sharing systems from around the world takes a number of different approaches to the rich datasets available. [4] explores a subset of the London system’s journey data to analyse spatial ‘‘tides’’ across the city. [5] uses stand occupation data in London to cluster similar stands by temporal behaviour, identifying ‘‘railway station-like’’ and ‘‘park-like’’ nodes in the system; this work is based on stand occupation data, and does not consider flows
Previous work by [6] has focused on network analysis and community detection in the Lyon bicycle sharing system, using spatio-temporal characteristics to cluster the network into communities
Summary
The role of the Smart City is increasingly seen as being one which incorporates technology, sustainability and quality of life, and the Bike Sharing concept fits neatly under that rubric [1], combining, as it does, low-carbon and low–pollution transportation, sensing technologies, shared societal resources and public health benefits (especially with respect to such key issues as obesity [2]) In this sense the humble bicycle cuts across a number of key issues of the 21st Century City, especially when seen through the Smart Cities lens. That in time-slicing journey data one needs to be extremely cautious about converting journeys into (flow) edges These authors circumvent the problem by dealing with flows in terms of numbers of bikes leaving origin i towards destination j at timeslice k. This does not represent the number of bikes on a route at a particular time (as these journeys take a finite length of time to complete), but simplifies the process of converting journeys into edge weights
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