Visualising where commuting cyclists travel using crowdsourced data

Datos Bibliográficos

ID12295348
AutoresDavid Philip Mcarthur (0000-0002-9142-3126, Urban Big Data Centre, autor de correspondencia), Jinhyun Hong (0000-0003-2322-2637, Urban Big Data Centre)
Año2018
Volumen74
Páginas233-241
Fecha de publicación2018-12-06
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Transport Geography (JOURNAL)
Identificadores de la revistaISSN: 0966-6923 • E-ISSN: 1873-1236
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.jtrangeo.2018.11.018
OpenAlexW2905116833
IdiomaEN
Citas recibidas22
Referencias citadas38

Encouraging more cycling is increasingly seen as an important way to create more sustainable cities and to improve public health. Understanding how cyclists travel and how to encourage cycling requires data; something which has traditionally been lacking. New sources of data are emerging which promise to reveal new insights. In this paper, we use data from the activity tracking app Strava to examine where people in Glasgow cycle and how new forms of data could be utilised to better understand cycling patterns. We propose a method for augmenting the data by comparing the observed link flows to the link flows which would have resulted if people took the shortest route. Comparing these flows gives some expected results, for example, that people like to cycle along the river, as well as some unexpected results, for example, that some routes with cycling infrastructure are avoided by cyclists. This study proposes a practical approach that planners can use for cycling plans with new/emerging cycling data

Bike sharing · Cycling · Data collection · Geography · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility