CrashFormer: A Multimodal Architecture to Predict the Risk of Crash-Reference-Cited by-同舟云学术

CrashFormer: A Multimodal Architecture to Predict the Risk of Crash

Published:2023-11-13 Issue: Volume: Page:
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Container-title:Proceedings of the 1st ACM SIGSPATIAL International Workshop on Advances in Urban-AI
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Author:

Karimi Monsefi Amin¹^ORCID,Shiri Pouya²^ORCID,Mohammadshirazi Ahmad¹^ORCID,Karimi Monsefi Nastaran³^ORCID,Davies Ron¹^ORCID,Moosavi Sobhan⁴^ORCID,Ramnath Rajiv¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The Ohio State University, Columbus, Ohio, USA

2. Circle Cardiovascular Imaging, Calgary, Alberta, Canada

3. Hamedan University of Technology, Tehran, Iran

4. Zoox Inc., San Francisco, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3615900.3628769

Reference42 articles.

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2. A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data

3. Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http://osc.edu/ark:/19495/f5s1ph73 Ohio Supercomputer Center. 1987. Ohio Supercomputer Center. http://osc.edu/ark:/19495/f5s1ph73

4. A New Spatio-Temporal Neural Network Approach for Traffic Accident Forecasting

5. Daniel Eisenberg . 2004. The mixed effects of precipitation on traffic crashes. Accident analysis & prevention 36, 4 ( 2004 ), 637--647. Daniel Eisenberg. 2004. The mixed effects of precipitation on traffic crashes. Accident analysis & prevention 36, 4 (2004), 637--647.