Highlights The Crash Momentum Index (CMI) is employed to represent impact eccentricity. ReliefF algorithm provides a ranking of the most in influential variables on injury risk. CMI affects injury risk, regardless of the used statistical or machine learning model. Impact-related variables are more in influential than occupant/vehicle-related features. By CMI and closing velocity, injury risk can be predicted before collision occurrence.

    Abstract Thorough evaluations on injury risk (IR) are fundamental for guiding interventions toward the enhancement of both the road infrastructure and the active/passive safety of vehicles. Well-established estimates are currently based on IR functions modeled on post-crash variables, such as velocity change sustained by the vehicle ( Δ V ); thence, these analyses do not directly suggest how pre-crash conditions can be modified to allow for IR reduction. Nevertheless, Δ V can be disaggregated into two contributions which enable its a priori calculation, based only on the information available at the impact instant: the Crash Momentum Index (CMI), representing impact eccentricity at collision, and the closing velocity at collision ( V r ). By extensively employing the CMI indicator, this work assesses the overall influence of impact eccentricity and closing velocity on the risk for occupants to sustain a serious injury. As CMI synthesizes indications regarding Δ V , its use can be disjointed from the Δ V itself for the derivation of high-quality IR models. This feature distinguishes CMI from the other eccentricity indicators available at the state-of-the-art, allowing for the contribution of eccentricity on IR to be completely isolated. Because of this element of originality, special attention is given to the CMI variable throughout the present work. Based on data extracted from the NASS/CDS database, the influence of the CMI and V r variables on IR is specifically highlighted and analyzed from several perspectives. The feature ranking algorithm ReliefF, whose use is unprecedented in the accident analysis field, is first employed to assess importance of such impact-related variables in determining the injury outcome: if compared to vehicle-related and occupant-related variables (as category and age, respectively), the higher influence of CMI and V r is initially highlighted. Secondly, the relevance of CMI and V r is confirmed by fitting different predictive models: the fitted models which include the CMI predictor perform better than models which neglect the CMI, in terms of classical evaluation metrics. As a whole, considering the high predictive power of the proposed CMI-based models, this work provides valuable tools for the a priori assessment of IR.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Injury risk assessment based on pre-crash variables: The role of closing velocity and impact eccentricity


    Beteiligte:


    Erscheinungsdatum :

    2020-10-22




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Crash momentum index and closing velocity as crash severity index

    Vangi, Dario / Gulino, Michelangelo-Santo / Fiorentino, Anita et al. | SAGE Publications | 2019


    Injury risk assessment of several crash data sets

    Wang,L. / Banglmaier,R. / Prasad,P. et al. | Kraftfahrwesen | 2003


    Injury Risk Assessment of Several Crash Data Sets

    Wang, Lan / Prasad, Priya / Banglmaier, Richard | SAE Technical Papers | 2003


    Speed related variables for crash injury risk analysis: what has been used?

    Torrão, Guilhermina | Taylor & Francis Verlag | 2022

    Freier Zugriff

    Brain Injury Risk Assessment of Frontal Crash Test Results

    Mertz, H. J. / Irwin, A. L. / Society of Automotive Engineers | British Library Conference Proceedings | 1994