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Over the last two years, we have witnessed the ever-fast growth of micro-mobility services (e.g., e-bikes and e-scooters), which brings both challenges and innovations to the traditional . For example, they provide an opportunity to better address the 鈥渓ast mile鈥� problem due to their convenience, flexibility and zero emission. As such, it is essential to understand why and how urban dwellers use these micro-mobility services across space and time. In this paper, we aim to understand spatiotemporal trip purposes of urban micro-mobility through the lens of dockless e-scooter user behavior. We first develop a spatiotemporal topic modeling method to infer the underlying trip purpose of dockless e-scooter usage. Then, using Washington, D.C. as a case study, we apply the model to a dataset including 83,002 valid user trips together with 19,370 POI venues and land use land cover data to systematically explore the trip purposes of micro-mobility across space and time in the city. The results confirm a set of uncovered 100 Trips Topics as an informative and effective proxy of the spatiotemporal trip purposes of micro-mobility users. The findings in this paper provide important insights for city authorities and dockless e-scooter companies into more sustainable urban transportation planning and more efficient vehicle fleet reallocation in .

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Walking has numerous benefits for the mental and physical health. It is a sustainable mode of mobility that modern cities should incentivise. Walkability, a notion of how friendly a street is for walking, entails different aspects like the perception of safety, beauty and social vibrancy. The perception of walkability is also influenced by the physical structure and spatial configuration of streets and their features.

Most studies on walkability are conducted based on interviews collecting valuable and detailed data. However, this data collection procedure is time- and resource-intensive and difficult to upscale to large areas. This project will leverage on street view imagery, deep learning image interpretation, crowdsourcing technologies, and geospatial datasets to develop a data-driven account of how the perception of walkability relates to physical, social and visual attributes of streets across different social groups, thus providing city administrators and planners a concrete and transferable methodology that helps them evaluate and enhance the liveability of their cities. Besides a transferable methodology to estimate city-wide walkability, we will propose data visualisations that surface the diversified perception of the urban space across different groups and that may support data-based theoretical developments on this multi-dimensional concept.