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

TY - JOUR

T1 - Incorporating social norms into a configurable agent-based model of the decision to perform commuting behaviour

AU - Greener, Robert

AU - Lewis, Daniel

AU - Reades, Jon

AU - Miles, Simon

AU - Cummins, Steven

N1 - Funding Information: Funding: This research was funded by KCL%�LSHTM seed funding. Robert Greener is supported by a Medical Research Council Studentship [grant number: MR/N0136638/1]. Steven Cummins is funded by Health Data Research UK ?HDR-UK). HDR-UK is an initiative funded by the UK Research and Innovation, Department of Health and Social Care ?England) and the devolved administrations, and leading medical research charities. Publisher Copyright: © 2022 Copyright for this paper by its authors.

PY - 2022

Y1 - 2022

N2 - Interventions to increase active commuting have been recommended as a method to increase population physical activity, but evidence is mixed. Social norms related to travel behaviour may influence the uptake of active commuting interventions but are rarely considered in their design and evaluation. 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 parameters) affect the decision to travel between four modes: walking, cycling, driving, and public transport. The developed model (MOTIVATE) is a configurable agent-based model where social norms related to travel behaviour are used to provide a more realistic representation of the socio-ecological systems in which active commuting interventions may be deployed. The utility of this 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 active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the intervention scenario, on non-car-free days; the effect is sustained to non-car-free days. 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.

AB - Interventions to increase active commuting have been recommended as a method to increase population physical activity, but evidence is mixed. Social norms related to travel behaviour may influence the uptake of active commuting interventions but are rarely considered in their design and evaluation. 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 parameters) affect the decision to travel between four modes: walking, cycling, driving, and public transport. The developed model (MOTIVATE) is a configurable agent-based model where social norms related to travel behaviour are used to provide a more realistic representation of the socio-ecological systems in which active commuting interventions may be deployed. The utility of this 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 active travel were increased by 70.3% (89% HPDI: [70.3%, 70.3%]), in the intervention scenario, on non-car-free days; the effect is sustained to non-car-free days. 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.

KW - active travel

KW - agent-based modelling

KW - car-free days

KW - physical activity

UR - http://www.scopus.com/inward/record.url?scp=85136151675&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:85136151675

SN - 1613-0073

VL - 3173

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 12th International Workshop on Agents in Traffic and Transportation, ATT 2022

Y2 - 25 July 2022

ER -