The advent of Automatic Crash Notification Systems offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to emergency medical service 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 Score (MAIS). The accuracy of proposed models was 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 MAIS 3 and higher injuries was 74.2%. Crash mode has a significant influence on injury risk. For crashes with 30 mph delta-V, the risk of MAIS 3+ injury for each mode is 38.9%, 83.8%, 47.8% and 19.9% for frontal, nearside, farside, and rear impact crashes, respectively. In addition to delta-V, 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.
Abstract