Automatic Accident Detection System Using IoT Compared to the Systems that a Traffic Centre Uses for Accident Detection

In recent years, vehicle traffic has become a major issue with a significant increase in the number of vehicles, leading to congestion on urban roads and motorways. This has resulted in an increase in accidents, causing more injuries and fatalities. There are many steps in managing an accident, it can be said assuring that one of the most important steps is the rapid detection of the incident and its exact location. Quick and accurate information enables emergency services to act quickly at the incident location and reduce their response times. This paper tries to compare the automatic accident detection system with traditional traffic center systems and analyze the importance of implementing this system to reduce accident detection times and accurately detect their location. The detection of the accident time and its location are crucial links in the accident management chain.

• Record URL:
  • https://doi.org/10.1016/j.procs.2023.12.152
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S1877050923021646
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
• Supplemental Notes:
  • © 2023 The Author(s). Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zavantis, Dimitrios
  • Mandalozis, Dimitrios
  • Yasar, Ansar
  • Hasimi, Lumbarda
• Conference:
  • 14th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 13th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare (EUSPN/ICTH 2023)
  • Location: Almaty , Kazakhstan
  • Date: 2023-11-7 to 2023-11-9
• Publication Date: 2024

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 16-23
• Serial:
  • Procedia Computer Science
  • Volume: 231
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 1877-0509
  • Serial URL: http://www.sciencedirect.com/science/journal/18770509
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Alternatives analysis; Automatic incident detection; Incident management; Internet of things
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01906363
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 29 2024 5:11PM