TY - GEN
T1 - Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories
AU - He, Songtao
AU - Sadeghi, Mohammad Amin
AU - Chawla, Sanjay
AU - Alizadeh, Mohammad
AU - Balakrishnan, Hari
AU - Madden, Samuel
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - 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 outperform prior work in terms of resolution and accuracy.
AB - 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 outperform prior work in terms of resolution and accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85125688495&partnerID=8YFLogxK
U2 - 10.1109/ICCV48922.2021.01176
DO - 10.1109/ICCV48922.2021.01176
M3 - Conference contribution
AN - SCOPUS:85125688495
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 11957
EP - 11965
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -