Using agent-based modelling for investigating the impact of travel behaviour interventions on mode shift: A travel behaviour simulation in Dublin

Abstract

The present study examines the impact of various strategic interventions on behavioural changes from private car use to sustainable travel modes in Dublin City, using an agent-based modelling (ABM) approach with a particular focus on soft policy (i.e., cycling promotion campaigns and travel feedback programs) and hard policy (i.e., motor tax and fuel price increments) measures. Utilising the Irish National Household Travel Survey (NHTS), we analysed changes in travel mode share, behavioural stage progression, and CO2 emissions under different policy scenarios. The findings suggest that soft interventions, such as cycling promotion campaigns and travel feedback programs, have consistent impacts on reducing car use, progressing behavioural stages (a shift from lower stages like precontemplation to higher stages like action), and lowering CO₂ emissions, with effects amplified when combined with motor tax and fuel price measures. The findings further highlight that using multiple outcome measures provides a more comprehensive evaluation of the impact, making it valuable for policymakers and decision-makers in assessing different transport policy interventions.