Abstracting mobility flows from bike-sharing systems

Fabio Kon (), Éderson Cássio Ferreira, Higor Amario Souza, Fábio Duarte, Paolo Santi and Carlo Ratti
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Fabio Kon: University of São Paulo
Éderson Cássio Ferreira: University of São Paulo
Higor Amario Souza: University of São Paulo
Fábio Duarte: Massachusetts Institute of Technology
Paolo Santi: Massachusetts Institute of Technology
Carlo Ratti: Massachusetts Institute of Technology

Public Transport, 2022, vol. 14, issue 3, No 1, 545-581

Abstract: Abstract Bicycling has grown significantly in the past ten years. In some regions, the implementation of large-scale bike-sharing systems and improved cycling infrastructure are two of the factors enabling this growth. An increase in non-motorized modes of transportation makes our cities more human, decreases pollution, traffic, and improves quality of life. In many cities around the world, urban planners and policymakers are looking at cycling as a sustainable way of improving urban mobility. Although bike-sharing systems generate abundant data about their users’ travel habits, most cities still rely on traditional tools and methods for planning and policy-making. Recent technological advances enable the collection and analysis of large amounts of data about urban mobility, which can serve as a solid basis for evidence-based policy-making. In this paper, we introduce a novel analytical method that can be used to process millions of bike-sharing trips and analyze bike-sharing mobility, abstracting relevant mobility flows across specific urban areas. Backed by a visualization platform, this method provides a comprehensive set of analytical tools to support public authorities in making data-driven policy and planning decisions. This paper illustrates the use of the method with a case study of the Greater Boston bike-sharing system and, as a result, presents new findings about that particular system. Finally, an assessment with expert users showed that this method and tool were considered very useful, relatively easy to use and that they intend to adopt the tool in the near future.

Keywords: Bike-sharing; Mobility; Data science; Visualization; Open source software; Application case study (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12469-020-00259-5

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