An open dataset on individual perceptions of transport policies.
Le-Minh KieuAlexis ComberHang Nguyen Thi ThuyThanh Bui QuangPhe Hoang HuuNick MallesonPublished in: Scientific data (2024)
Many cities are facing challenges caused by the increasing use of motorised transport and Hanoi, Vietnam, is no exception. The proliferation of petrol powered motorbikes has caused serious problems of congestion, pollution, and road safety. This paper reports on a new survey dataset that was created as part of the Urban Transport Modelling for Sustainable Well-Being in Hanoi (UTM-Hanoi) project. The survey of nearly 30,000 respondents gathers data on households' demographics, perceptions, opinions and stated behaviours. The data are informative in their own right and have also been used to experiment with multi-scale spatial statistics, synthetic population generation and machine learning approaches to predicting an individual's perceptions of potential government policies. The paper reports on the key findings from the survey and conducts a technical validation to contrast the outcomes to similar datasets that are available.
Keyphrases
- healthcare
- machine learning
- primary care
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- cross sectional
- electronic health record
- big data
- magnetic resonance
- mental health
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- heavy metals
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- type diabetes
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- drug induced