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Big Data and Cycling

Romanillos, G; Zaltz Austwick, M; Ettema, D; De Kruijf, J; (2016) Big Data and Cycling. Transport Reviews , 36 (1) pp. 114-133. 10.1080/01441647.2015.1084067. Green open access

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Abstract

Big Data has begun to create significant impacts in urban and transport planning. This paper covers the explosion in data-driven research on cycling, most of which has occurred in the last ten years. We review the techniques, objectives and findings of a growing number of studies we have classified into three groups according to the nature of the data they are based on: GPS data (spatio-temporal data collected using the global positioning system (GPS)), live point data and journey data. We discuss the movement from small-scale GPS studies to the ‘Big GPS’ data sets held by fitness and leisure apps or specific cycling initiatives, the impact of Bike Share Programmes (BSP) on the availability of timely point data and the potential of historical journey data for trend analysis and pattern recognition. We conclude by pointing towards the possible new insights through combining these data sets with each other – and with more conventional health, socio-demographic or transport data.

Type: Article
Title: Big Data and Cycling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/01441647.2015.1084067
Publisher version: http://dx.doi.org/10.1080/01441647.2015.1084067
Language: English
Additional information: This is an Accepted Manuscript of an article published by Taylor & Francis in Transport Reviews on 29/09/15, available online: http://wwww.tandfonline.com/10.1080/01441647.2015.1084067.
Keywords: Big data, bike mobility, bikeshare, Cycling, GPS, spatial analysis
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: http://discovery.ucl.ac.uk/id/eprint/1472946
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