Deep Hybrid Attention Framework for Road Crash Emergency Response Management

Road traffic crash is a global tragedy that leads to economic loss, injury, and fatalities. Understanding the severity of a road crash at the early stages is vital to timely providing emergency medical services to crash victims. This study developed a crash emergency response management framework that requires basic crash information for emergency response decision-making. A Deep Hybrid Attention Network (DHAN) was proposed that captures temporal variations and spatial correlations for dynamic severity prediction. Further, two alternative model architectures that initially required only the approximate location or time of the crash were proposed and compared with the DHAN. The experiment was conducted on seven years French road crash dataset (2011-2017). The DHAN achieved an AUC of 0.820, an accuracy of 0.761, a recall of 0.803, and a false alarm rate of 0.258, outperforming baseline models.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3376653
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Kashifi, Mohammad Tamim
  • 0000-0002-4003-637X
• Publication Date: 2024-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 8807-8818
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Crash data; Crashes; Emergency medical services; Machine learning; Predictive models
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Security and Emergencies;

Filing Info

• Accession Number: 01936406
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 11 2024 9:39AM