In the last decade, over two billion people have become victims of mass casualty incidents (MCIs). The success of the emergency response to such events depends heavily on efficient emergency vehicle transportation. During a MCI, the status of transportation pathways is constantly fluctuating, making it difficult to evaluate real-time traffic delays. Standard traffic delay software is not reliable in routing emergency resources to incident sites. Thus, the question of how can image recognition and deep-learning be used in real-time to aid emergency vehicles in MCI efforts emerges. The “You-Only-Look-Once” method is applied to provide accurate vehicle detection; a convex hull is implemented to conduct road detection; and simple supporting methods are used to describe traffic states. This combination yields a classification of traffic congestion based on defined parameter thresholds. The resulting output will ultimately guide a decision-maker or supplemental model to optimize emergency vehicle deployment.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Object Detection Using Artificial Intelligence: Predicting Traffic Congestion to Improve Emergency Response to Mass Casualty Incidents


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Ahram, Tareq Z. (Herausgeber:in) / Karwowski, Waldemar (Herausgeber:in) / Kalra, Jay (Herausgeber:in) / Julson, Rye (Autor:in) / Ahlers, Miranda (Autor:in) / Hamilton, Alexander (Autor:in) / Kolesar, Michael (Autor:in) / Barbeito, Gonzalo (Autor:in) / Ehrlich, Jacob (Autor:in) / Dulin, Johnathon (Autor:in)

    Kongress:

    International Conference on Applied Human Factors and Ergonomics ; 2021 July 25, 2021 - July 29, 2021



    Erscheinungsdatum :

    08.07.2021


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Urban Traffic Emergency Management Response to Abnormal Incidents

    Yang, Zhaosheng / Liu, Chunrong | ASCE | 2011


    Rotorcraft Use in Disaster Relief and Mass Casualty Incidents - Case Studies

    S. Henninger / J. Thompson / R. Newman | NTIS | 1990



    Satellite Communications for Management of Mass Casualty Incidents: The e-Triage Project

    Via, A. / Werner, M. / Donner, A. et al. | British Library Conference Proceedings | 2010


    Satellite Communications for Management of Mass Casualty Incidents: The e-Triage Project

    Via, Angels / Fernandez, J. / Werner, Markus et al. | AIAA | 2010