Abstract
New towns have been extensively developed in many worldwide metropolises to overcome a series of urban crisis caused by over-intensive population and economic activity, such as environmental deterioration, traffic congestion and space shortage. The successful development of new towns has been regarded as a common, international urban challenge aimed at creating vital and lively urban life. Quantifying the complex relationship between new town vibrancy and the built environment provides a theoretical basis for formulating adequate planning regulations and policies to solve the development predicaments of new towns. Taking the Wuhan metropolitan area as the case study, this study employs the gradient boosting decision tree (GBDT) machine learning method to delve into the intricate non-linear relationship between urban vibrancy and built environment indicators both in both peripheric new towns (NT) and central mother cities (MC), shedding light on the differences in the characteristics and complexities of vibrancy between NT and MC. The findings reveal stark disparities in vibrancy levels between NT and MC, emphasizing the critical need for tailored revitalization strategies. It is found that the indicators with the highest contribution to the impact of vibrancy in NT and MC are respectively the point of interest (POI) density and Transit-Oriented Development (TOD) development intensity. Model comparison between NT and MC concludes that enhancing POI density to meet the basic needs of citizens is a prerequisite for fostering urban vibrancy. To successfully create similarly vital and lively urban life as that in the MC, NT should focus on the impacts of TOD development and policy factors such as government relocation on vibrancy. Finally, deriving from these findings, we propose some policy insights and practical strategies for urban practitioners and policymakers to enhance the vibrancy and sustainability of the new towns.
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