Exploring the influence of road network structure on the spatial behaviour of cyclists using crowdsourced data. (September 2019)

• Record Type: Journal Article
• Authors: Orellana, Daniel; Guerrero, Maria L
• Other Names: Behnisch Martin guest-editor.; Hecht Robert guest-editor.; Herold Hendrik guest-editor.; Jiang Bin guest-editor.
• Is Part Of: Environment & planning. Volume 46:Number 7(2019)
• Page Start: 1314
• Page End: 1330
• Publication Date: 2019-09
• Subjects: Urban morphology -- Big Data -- cycling movement -- Space Syntax -- OpenStreetMap; City planning -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.11605
• DOI: 10.1177/2399808319863810
• Languages: English
• ISSNs: 2399-8083

Abstract

This study explores the effect of the spatial configuration of street networks on movement patterns of users of a cycling monitoring app, employing crowdsourced information from OpenStreetMap and Strava Metro. Choice and Integration measures from Space Syntax were used to analyse the street network's configuration for different radiuses. Multiple linear regression models were fitted to explore the influence of these measures on cycling activity at the street segment level after controlling other variables such as land use, household density, socio-economic status, and cycling infrastructure. The variation of such influence for different time periods (weekday vs. weekend) and trip purposes (commuting vs. sports) was also analysed. The results show a positive significant association between normalised angular choice ( NACH ) and cycling activity. Although the final regression model explained 5.5% of the log-likelihood of the intercept model, it represents an important improvement compared with the base (control-only) model (3.8%). The incidence rate ratio of NACH 's Z scores was 1.63, implying that for an increase of one standard deviation of NACH, there is an expected increment of about 63% in the total cyclist counts while keeping all other variables the same. These results are of interest for researchers, practitioners, and urban planners, since the inclusion of Space Syntax measures derived from available public data can improve movement behaviour modelling and cycling infrastructure planning and design.