Active Mobility

This study used historical street view imagery and cyclist data from London to establish causal relationships between urban features (like vegetation, slope, and bike lanes) and cycling patterns, finding that their impacts vary by population density and providing actionable insights for urban planning.

This study introduces a novel AI-based framework to mitigate perspective biases in vehicle-captured street view imagery, successfully translating car-centric views to pedestrian and cyclist perspectives for more accurate bikeability and walkability assessments in urban planning.

I attended IATBR 2024 in Vienna, Austria this week and presented my work on the causal impacts of visual elements on cyclist activities!

I was invited to talk about my research on street view imagery to a group of audience from government agencies and industries

The study develops a new method using deep learning to correct perspective biases in street view imagery, improving the assessment of active transportation infrastructure from the viewpoint of cyclists and pedestrians.