Software and results for: Incorporating social norms into a configurable agent-based model of commuting behaviour

In this study we develop an agent-based model that incorporates social norms related to travel behaviour and demonstrate the utility of this through implementing car-free Wednesdays. A synthetic population of Waltham Forest, London, UK was generated using a microsimulation approach with data from the UK Census 2011 and UK HLS datasets. An agent-based model was created using this synthetic population which modelled how the actions of peers and neighbours, subculture, habit, weather, bicycle ownership, car ownership, environmental supportiveness, and congestion (all configurable model parameters) affect the decision to travel between four modes: walking, cycling, driving, and taking public transport.

Results: In the control scenario, the odds of active travel were plausible at 0.091 (89% HPDI: [0.091 to 0.091]). Compared to the control scenario, the odds of active travel were increased by 70.3% (89% HPDI: [70.3% to 70.3%]), in the intervention scenario, on non-car-free days; the effect of the intervention is sustained to non-car-free days. Discussion While these results demonstrate the utility of our agent-based model, rather than aim to make accurate predictions, they do suggest that by there being a ‘nudge’ of car-free days, there may be a sustained change in active commuting behaviour.

The model is a useful tool for investigating the effect of how social networks and social norms influence the effectiveness of various interventions. If configured using real-world built environment data, it may be useful for investigating how social norms interact with the built environment to cause the emergence of commuting conventions.