Civil Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt; Civil Engineering Department, Faculty of Engineering, Sphinx University, Egypt.
Civil Engineering Department, Faculty of Engineering, Assiut University, Assiut, Egypt.
Accid Anal Prev. 2022 Feb;165:106514. doi: 10.1016/j.aap.2021.106514. Epub 2021 Dec 8.
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.
交通事故是具有不一致时空维度的罕见事件；因此，事故伤害严重程度（INJ-S）分析在分类和数据稳定性方面面临重大挑战。虽然经典的统计模型在准确建模 INJ-S 方面存在局限性，但先进的机器学习方法没有明显的方程来优先分析/分析不同的影响因素，以预测 INJ-S 水平。此外，输入因素之间的相互关系可能会使典型敏感性分析的结果产生误导。追尾事故构成最常见的交通事故类型；因此，需要对其相关的 INJ-S 进行更深入的调查。为了解决所有这些问题，本研究提出了一种基于深度学习范例的复杂方法，结合基于方差的全局敏感性分析（VB/GSA）。该方法提出了一种深度残差神经网络结构，该结构使用残差捷径（即连接），与其他神经网络架构不同。这些连接允许 DRNN 绕过深度网络架构中的几个层，避免了常规训练中的高精度问题。利用训练后的 DRNN 模型进行蒙特卡罗模拟，根据 VB/GSA 调查每个解释因素对 INJ-S 水平的影响。该方法用于分析 2010 年至 2017 年北卡罗来纳州的所有追尾事故。利用一些选定的代表性指标评估所开发方法的性能，然后将其与知名的有序逻辑回归（OLR）模型进行比较。结果表明，所开发的方法的整体准确率为 83%，性能优于 OLR 模型。此外，VB/GSA 分析可以确定对追尾事故 INJ-S 水平最有影响的属性。
