Traffic accidents are rare events with inconsistent spatial and temporal dimensions; thus, accident injury severity (INJ-S) analysis faces a significant challenge in its classification and data stability. While classical statistical models have limitations in accurately modeling INJ-S, advanced machine learning methods have no apparent equations to prioritize/analyze different contributing factors to predict INJ-S levels. Also, the intercorrelations among the input factors could make the results of a typical sensitivity analysis misleading. Rear-end accidents constitute the most frequent type of traffic accidents; and therefore, their associated INJ-S need more insight investigations. To resolve all these issues, this study presents a sophisticated approach based on a deep learning paradigm combined with a Variance-Based Globa1 Sensitivity Analysis (VB/GSA). The methodology proposes a deep residual neural networks structure that utilizes residual shortcuts (i.e., connections), unlike other neural network architectures. The connections allow the DRNNs to bypass a few layers in the deep network architecture, circumventing the regular training with high accuracy problems. The Monte Carlo simulation with the aid of the trained DRNNs model was conducted to investigate the impact of each explanatory factor on the INJ-S levels based on the VB/GSA. The developed methodology was used to analyze all rear-end accidents in North Carolina from 2010 to 2017. The performance of the developed methodology was evaluated utilizing some selected representative indicators and then compared with the well-known ordered logistic regression (OLR) model. The developed methodology was found to achieve an overall accuracy of 83% and attained a superior performance, as compared with the OLR model. Furthermore, the VB/GSA analysis could identify the most significant attributes to rear-end crashes INJ-S level.