Urban Data Science for Sustainable Transport Policies in Emerging Economies
Published Web Locationhttps://doi.org/10.25436/E28G6D
In the city of Hanoi, Vietnam, as with other rapidly-developing cities, transport infrastructure is failing to keep pace with the burgeoning population. This has lead to high levels of congestion, air pollution, and a broad inequity in the accessibility of large parts of the city to residents. The emerging discipline of Urban Data Science has a valuable role in providing policy makers with robust evidence on which to base policy, but the discipline faces problems with the application of techniques that are based on assumptions that do not hold when applied to emerging economies.
This paper presents the preliminary outputs of a new programme of urban data science work that is being developed specifically for Hanoi. It leverages a spatial microsimulation approach to up-sample a bespoke travel survey and create a synthetic representation of the transport preferences of all residents in the city. These new data are used to assess the impacts that changes in the broader socio-economic context, such as increasing prosperity amongst residents, could have on rates of car ownership and hence on the problems of congestion and pollution. The results begin to highlight parts of the city where the impacts of improved economic conditions coupled with changes to wider transport policies might lead to greater use of personal cars in the future.