The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to EMS decision makers is to identify those crashes with serious injuries and deploy the appropriate rescue and treatment capabilities. The objective of this paper is to determine the crash characteristics that increase the risk of serious injury. Within this paper, regression models are presented which relate occupant, vehicle and impact characteristics to the probability of serious injury using the Maximum Abbreviated Injury Scale Level (MAIS). The accuracy of proposed models were evaluated using National Automotive Sampling System/ Crashworthiness Data System (NASS/CDS) and Crash Injury Research and Engineering Network (CIREN) case data. Cumulatively, the positive prediction rate of models identifying the likelihood of MAIS3 and higher injuries was 74.2%. Crash mode has a significant influence of injury risk. For crashes with 30 mph deltaV, the risk of MAIS3+ injury for each mode is 38.9%, 83.8%, 47.8% and 19.9% for frontal, near side, far side and rear impact crashes, respectively. In addition to deltaV, a number of crash variables were identified that assist in the accurate prediction of the probability of MAIS 3+ injury. These variables include occupant age, partial ejection, safety belt usage, intrusion near the occupant, and crashes with a narrow object. For frontal crashes, added crash variables include air bag deployment, steering wheel deformation, and multiple impact crashes. The quantitative relationship between each of these crash variables and injury risk has been determined and validated by regression analysis based on NASS/CDS and CIREN data.


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    Titel :

    The life-saving opportunity for automatic crash notification systems


    Weitere Titelangaben:

    Das Lebensrettungspotenzial automatischer Unfallmeldesysteme


    Beteiligte:
    Augenstein, J. (Autor:in) / Perdeck, E. (Autor:in) / Stratton, J. (Autor:in) / Chimento, S. (Autor:in) / Lehmann, W. (Autor:in) / Digges, K. (Autor:in) / Bahouth, G. (Autor:in)


    Erscheinungsdatum :

    2003


    Format / Umfang :

    19 Seiten, 6 Bilder, 6 Tabellen, 8 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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    Augenstein, J. / Perdeck, E. / Stratton, J. et al. | British Library Conference Proceedings | 2003


    The life saving opportunity for automatic crash notification systems

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    Trigger algorithm of vehicle automatic crash notification system

    Xiao, W. / Li, Z. / Chen, W. et al. | Springer Verlag | 2016


    Benefits of Automatic Crash Notification for Traffic Management

    Walker, R. / Stevens, A. / Anjum, O. et al. | British Library Conference Proceedings | 2010


    Trigger algorithm of vehicle automatic crash notification system

    Xiao, W. / Li, Z. / Chen, W. et al. | British Library Online Contents | 2016