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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.

Hanoi is a rapidly expanding city with sprawling infrastructure dominated by private vehicles— predominantly motorbikes—that often follow an informal set of traffic rules. Therefore, a bespoke household survey is currently being conducted that collects information about demographics, trip data, vehicle ownership, attitudes towards a proposed motorbike ban and others. This project develops a data dashboard using R Shiny to serve as a communication tool of the survey findings. The interim development of the dashboard is discussed with the breakdown of both the functionality (what the user sees) and the code (the development process and the structure of the app).

The city of Hanoi, Vietnam, like many rapidly expanding cities, is experiencing problems such as congestion, and the emergence of informal transport infrastructures. This paper presents preliminary results of a Spatial Interaction Model (SIM) which aims at predicting the impact of a potential motorbike ban, by simulating the number of journeys made from an origin to a destination using a particular transport mode. This analysis lays the foundation of how SIMs will be used to derive new insights and answer policy development questions such as where motorbikes should be banned and the impacts on existing modes of transport.

The city of Hanoi, Vietnam, suffers high levels of congestion and air pollution as transport infrastructure has failed to keep pace with a rapidly growing population. This paper presents the use of propensity score matching as a means of up-sampling a bespoke travel survey through linkage to census microdata. Among other uses, it is hoped that these new data will begin to shed light on peoples’ perceptions of potential transport-related policies, such as a ban on motorbikes from parts of the city centre.