Using spatial network analysis to model pedal cycle flows, risk and mode choice.

Cooper, Crispin H. V. ORCID: https://orcid.org/0000-0002-6371-3388 2017. Using spatial network analysis to model pedal cycle flows, risk and mode choice. Journal of Transport Geography 58 , pp. 157-165. 10.1016/j.jtrangeo.2016.12.003

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Abstract

Spatial network analysis (SpNA) provides a promising alternative to traditional transport models for the modelling of active travel, because walking and cycling behaviour is influenced by features smaller than the scale of zones in a traditional model. There is currently a need for link-level, city wide modelling of cycling, both to ensure the needs of existing cyclists are catered for in planning, and to model the effects of changing infrastructure in shaping cyclist behaviour. Existing SpNA models treat cyclists and car drivers as if they make navigational decisions in a similar way, which in reality is not the case. This paper presents an SpNA model using hybrid betweenness, which fits cyclist flows in Cardiff, Wales using distance, angular distance, motor vehicle traffic and slope as predictors of route choice. SpNA betweenness is also shown to implicitly capture the effect of urban density on mode choice. As it handles route finding decisions of drivers and cyclists separately, the model presented is also applicable to road safety models examining the interaction between the two classes of road user. The model has low cost of data collection and is reproducible using publicly available network analysis software and open mapping data. Further avenues for modelling the effect of infrastructure on cycling are discussed.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Geography and Planning (GEOPL)
Subjects:	G Geography. Anthropology. Recreation > G Geography (General)
Uncontrolled Keywords:	Spatial network analysis; Cycling; Modelling; Gis
Additional Information:	Released with a Creative Commons Attribution Non-Commercial No Derivatives License (CC BY-NC-ND)
Publisher:	Elsevier
ISSN:	0966-6923
Date of First Compliant Deposit:	8 December 2016
Date of Acceptance:	4 December 2016
Last Modified:	06 May 2023 05:04
URI:	https://orca.cardiff.ac.uk/id/eprint/96731

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