AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning
  and Spatial Post-processing
Bo Lin Shoshanna Saxe Timothy C. Y. Chan
Cycling stress assessment, which quantifies cyclists' perceived stress
imposed by the built environment and motor traffics, increasingly informs
cycling infrastructure planning and cycling route recommendation. However,
currently calculating cycling stress is slow and data-intensive, which hinders
its broader application. In this paper, We propose a deep learning framework to
support accurate, fast, and large-scale cycling stress assessments for urban
road networks based on street-view images. Our framework features i) a
contrastive learning approach that leverages the ordinal relationship among
cycling stress labels, and ii) a post-processing technique that enforces
spatial smoothness into our predictions. On a dataset of 39,153 road segments
collected in Toronto, Canada, our results demonstrate the effectiveness of our
deep learning framework and the value of using image data for cycling stress
assessment in the absence of high-quality road geometry and motor traffic data.