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Exploring the influence of road network structure on the spatial behaviour of cyclists using crowdsourced data

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  • Daniel Orellana
  • Maria L Guerrero

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.

Suggested Citation

  • Daniel Orellana & Maria L Guerrero, 2019. "Exploring the influence of road network structure on the spatial behaviour of cyclists using crowdsourced data," Environment and Planning B, , vol. 46(7), pages 1314-1330, September.
    • Handle: RePEc:sae:envirb:v:46:y:2019:i:7:p:1314-1330
    DOI: 10.1177/2399808319863810
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    1. John Parkin & Mark Wardman & Matthew Page, 2008. "Estimation of the determinants of bicycle mode share for the journey to work using census data," Transportation, Springer, vol. 35(1), pages 93-109, January.
    2. Birch, Colin P.D. & Oom, Sander P. & Beecham, Jonathan A., 2007. "Rectangular and hexagonal grids used for observation, experiment and simulation in ecology," Ecological Modelling, Elsevier, vol. 206(3), pages 347-359.
    3. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    4. Guerra, Erick & Caudillo, Camilo & Monkkonen, Paavo & Montejano, Jorge, 2018. "Urban form, transit supply, and travel behavior in Latin America: Evidence from Mexico's 100 largest urban areas," Transport Policy, Elsevier, vol. 69(C), pages 98-105.
    5. Jestico, Ben & Nelson, Trisalyn & Winters, Meghan, 2016. "Mapping ridership using crowdsourced cycling data," Journal of Transport Geography, Elsevier, vol. 52(C), pages 90-97.
    6. Raford, Noah & Chiaradia, Alain & Gil, Jorge, 2007. "Space Syntax: The Role of Urban Form in Cyclist Route Choice in Central London," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8qz8m4fz, Institute of Transportation Studies, UC Berkeley.
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