The dependence on motorbikes has contributed to severe traffic problems in Hanoi, Vietnam. Policymakers have considered a controversial ban on non-electric motorbikes in parts of the city in an effort to reduce congestion and pollution. However, understanding of individual perceptions on critical transport policies, such as this potential ban, is lacking. This paper applies a machine learning algorithm (XGBoost) to a bespoke travel survey to better understand how residents perceive a potential motorbike ban and how their perceptions might change under different policy scenarios. Our results suggest that prior awareness of the ban and shorter distances to public transport both increase peoples’ favour.
