Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories

Songtao He, Mohammad Amin Sadeghi, Sanjay Chawla, Mohammad Alizadeh, Hari Balakrishnan, Samuel Madden; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11977-11985

Traffic accidents cost about 3% of the world's GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. We present a technique to generate high-resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse and risk estimation is limited by bias-variance trade-off. Prior accident risk maps either estimate low-resolution maps that are of low utility (high bias), or they use frequency-based estimation techniques that inaccurately predict where accidents actually happen (high variance). To improve this trade-off, we use an end-to-end deep architecture that can input satellite imagery, GPS trajectories, road maps and the history of accidents. Our evaluation on four metropolitan areas in the US with a total area of 7,488 km2 shows that our technique outperforms prior work in terms of resolution and accuracy.