Liveable Street: Exploring the Impact Path for Built Environment on Fine-Grained Pedestrian Activity Under Video-Based Deep Learning

Walking is essential for daily physical activity, yet most existing studies focus on pedestrian counts while neglecting varied on-street activities, largely owing to data shortages. Targeting this gap, the research defines walking activity quality as the occurrence likelihood of walking-related activities and explores built environment influences. It adopts deep-learning video recognition to capture fine-grained pedestrian behaviours and quantifies walkability via activity quality. Structural equation modelling (SEM) is applied to decode causal links between urban design quality, pedestrian volume and activity quality. According to the results, urban design qualities exhibit a more pronounced influence on activity quality compared to pedestrian volume. Among a broad array of 20 physical features examined, interface density, the quantity of street furniture, walkway width, and shading rate all demonstrated significant positive effects on stationary activities. Interestingly, higher interface density and shorter crossing distances facilitated the occurrence of social activities, whereas the proportion of ground-floor windows had a notable negative impact on social activities. The findings of this study can directly inform the development of sustainable and liveable streets.