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In Accident; analysis and prevention

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 (ΔV); thence, these analyses do not directly suggest how pre-crash conditions can be modified to allow for IR reduction. Nevertheless, ΔV can be disaggregated into two contributions which enable its apriori 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 (Vr). 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 ΔV, its use can be disjointed from the ΔV 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 Vr 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 Vr is initially highlighted. Secondly, the relevance of CMI and Vr 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 apriori assessment of IR.

Gulino Michelangelo-Santo, Gangi Leonardo Di, Sortino Alessio, Vangi Dario

2020-Dec-29

A priori analysis, Crash Momentum Index (CMI), Feature ranking, Machine learning, Predictive models, Velocity change ()