Architecture, Building and Planning - Research Publications
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ItemRoad traffic injury in urban areas: understanding the complex city.(ICoRSI, 2019)Over the past 4 decades considerable efforts have been taken to mitigate the growing burden of road injury. With increasing urbanisation along with global mobility that demands not only safety but equitable, efficient and clean (reduced carbon footprint) transport, the responses to dealing with the burgeoning road traffic injury in low- and middle-income countries has become increasingly complex. In this paper, we apply unique methods to identify important strategies that could be implemented to reduce road traffic injury in the Asia and Pacific region; a region comprising large middle-income countries (China and India) that are currently in the throes of rapid motorization. Using a convolutional neural network approach, we classified cities around the world based on urban characteristics related to private motor vehicles and public transport networks. We then identified 689 cities situated within the Asia-Pacific region and assessed the global burden of disease attributed to road traffic injury for urban design clusters. The modelling identified 9 urban cluster types. The majority (64%) of cities in the Asia-Pacific region fall within Clusters 1 and 2 namely, urban form that is sparse with low capacity road infrastructure and limited public transport. Clusters 1 and 2 comprises cities predominantly from China and South Asia with many low- to middle-income cities that are in the throes of considerable urban development. Urban cluster types with both dense road networks (e.g., Clusters Intense and Cul de sac) and public transport (e.g., Clusters High Transit and Motor City) demonstrated lower rates of DALYs lost per 100,000 population for road traffic injury. This study demonstrates the utility of employing image recognition methods to discover new insights to better understand the complex city and how it relates to road traffic injury.
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ItemNo Preview AvailableThe Paris end of town? Urban typology through machine learning.(American Association of Geographers, 2018)