• DocumentCode
    680759
  • Title

    Using Evolution Strategies to Reduce Emergency Services Arrival Time in Case of Accident

  • Author

    Barrachina, Javier ; Garrido, Pablo ; Fogue, Manuel ; Martinez, Francisco J. ; Cano, Juan-Carlos ; Calafate, Carlos T. ; Manzoni, Pietro

  • Author_Institution
    Univ. of Zaragoza, Zaragoza, Spain
  • fYear
    2013
  • fDate
    4-6 Nov. 2013
  • Firstpage
    833
  • Lastpage
    840
  • Abstract
    A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times.
  • Keywords
    emergency services; evolutionary computation; road safety; road traffic; V2I communications; density-based evolution strategy system; emergency services arrival time reduction; traffic accidents; traffic congestion problem; traffic density; traffic redirection; vehicle-to-infrastructure communications; Accidents; Emergency services; Equations; Mathematical model; Sociology; Statistics; Vehicles; Evolution Strategies; Traffic Accidents Assistance; Vehicular Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
  • Conference_Location
    Herndon, VA
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4799-2971-9
  • Type

    conf

  • DOI
    10.1109/ICTAI.2013.127
  • Filename
    6735338