Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories
Abstract
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.
Cite
Text
He et al. "Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.01176Markdown
[He et al. "Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/he2021iccv-inferring/) doi:10.1109/ICCV48922.2021.01176BibTeX
@inproceedings{he2021iccv-inferring,
title = {{Inferring High-Resolution Traffic Accident Risk Maps Based on Satellite Imagery and GPS Trajectories}},
author = {He, Songtao and Sadeghi, Mohammad Amin and Chawla, Sanjay and Alizadeh, Mohammad and Balakrishnan, Hari and Madden, Samuel},
booktitle = {International Conference on Computer Vision},
year = {2021},
pages = {11977-11985},
doi = {10.1109/ICCV48922.2021.01176},
url = {https://mlanthology.org/iccv/2021/he2021iccv-inferring/}
}