Understanding bicycling in cities using system dynamics modelling.

ABSTRACT:

Background

Increasing urban bicycling has established net benefits for human and environmental health. Questions remain about which policies are needed and in what order, to achieve an increase in cycling while avoiding negative consequences. Novel ways of considering cycling policy are needed, bringing together expertise across policy, community and research to develop a shared understanding of the dynamically complex cycling system. In this paper we use a collaborative learning process to develop a dynamic causal model of urban cycling to develop consensus about the nature and order of policies needed in different cycling contexts to optimise outcomes.

Methods

We used participatory system dynamics modelling to develop causal loop diagrams (CLDs) of cycling in three contras

SUBMITTER: Macmillan A 

PROVIDER: S-EPMC5736169 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature