Understanding the new trends in pedestrian injury distribution and mechanism through data linkage and modeling.

The objectives of this study were to 1) collect and analyze recent pedestrian crash cases for better understanding of the pedestrian injury distribution and mechanism, 2) use computational simulations to reconstruct pedestrian cases and estimate potential benefit of pedestrian automatic emergency braking (PedAEB) in reducing pedestrian injury risks, and 3) estimate how future pedestrian crash distribution might influence priorities for pedestrian protection. Analyses of national crash-injury dataset showed that the overall number of pedestrians in crashes as well as the serious and fatal pedestrian injuries in the U.S. have been increasing in recent years. Striking vehicle type has changed (i.e., decreased proportion of passenger cars and increase of SUVs and pickup trucks) from 20 years ago mirroring changes in the fleet distribution of vehicle sales. A total of 432 pedestrian injury cases were generated by linking the Michigan trauma data and police-reported crash data from 2013 to 2018. Among the linked cases, pickup trucks and SUVs were involved in crashes with more injuries across body regions. Notably, AIS 3+ chest injuries occur at almost the same rate as lower extremity injuries. A method, combining MADYMO simulations (n = 3,500), response surface model, and data mining, was developed to reconstruct 25 linked pedestrian crash cases to estimate the effectiveness of PedAEB. Based on national field data and MADYMO simulations, PedAEB was estimated to be effective in reducing the risk of head and lower extremity injuries but is relatively less effective in reducing the risk of chest injuries. The increased proportions of SUVs and pickup trucks in the vehicle fleet and the higher penetration of PedAEB may highlight the importance of future research into chest injury risk for pedestrian protection.