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Interventions to increase active commuting have been recommended as a methodto increase population physical activity, but evidence is mixed. Social normsrelated to travel behaviour may influence the uptake of active commutinginterventions but are rarely considered in their design and evaluation. In thisstudy we develop an agent-based model that incorporates social norms related totravel behaviour and demonstrate the utility of this through implementingcar-free Wednesdays. A synthetic population of Waltham Forest, London, UK wasgenerated using a microsimulation approach with data from the UK Census 2011and UK HLS datasets. An agent-based model was created using this syntheticpopulation which modelled how the actions of peers and neighbours, subculture,habit, weather, bicycle ownership, car ownership, environmental supportiveness,and congestion affect the decision to trave. The developed model (MOTIVATE) isa configurable agent-based model where social norms related to travel behaviourare used to provide a more realistic representation of the socio-ecologicalsystems in which active commuting interventions may be deployed. The utility ofthis model is demonstrated using car-free days as a hypothetical intervention.In the control scenario, the odds of active travel were plausible at 0.091 (89%HPDI: [0.091, 0.091]). Compared to the control scenario, the odds of activetravel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the interventionscenario, on non-car-free days; the effect is sustained to non-car-free days.The model is a useful tool for investigating the effect of how social networksand social norms influence the effectiveness of various interventions. Ifconfigured using real-world built environment data, it may be useful forinvestigating how social norms interact with the built environment to cause theemergence of commuting conventions.

Incorporating social norms into a configurable agent-based model of the decision to perform commuting behaviour