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Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling

Cooper, Crispin H. V. ORCID: and Yin Cheung Chang, Eric 2019. Using road class as a replacement for predicted motorized traffic flow in spatial network models of cycling. Scientific Reports 9 , 19724. 10.1038/s41598-019-55669-8

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Recent years have seen renewed policy interest in urban cycling due to the negative impacts of motorized traffic, obesity and emissions. Simulating bicycle mode share and flows can help decide where to build new infrastructure for maximum impact, though modelling budgets are limited. The four step model used for vehicles is not typically used for this task as, aside from the expense of use, it is designed around too-large zone sizes and a simplified network. Alternative approaches are based on aggregate statistics or spatial network analysis, the latter being necessary to create a model sufficiently sensitive to infrastructure location, although still requiring considerable modelling effort due to the need to simulate motor vehicle flows in order to account for the effect of motorized traffic in disincentivising cycling. The model presented uses an existing spatial network analysis methodology on an unsimplified network, but simplifies the analysis by substituting explicit prediction of motorized traffic flow with an alternative based on road classification. The method offers a large reduction in modelling effort, but nonetheless gives model correlation with actual cycling flows (R2 = 0.85) broadly comparable to a previous model with motorized traffic fully simulated (R2 = 0.78).

Item Type: Article
Date Type: Publication
Status: Published
Schools: Geography and Planning (GEOPL)
Sustainable Places Research Institute (PLACES)
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License
Publisher: Nature Publishing Group
ISSN: 2045-2322
Date of First Compliant Deposit: 2 December 2019
Date of Acceptance: 2 December 2019
Last Modified: 26 Oct 2022 08:22

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