Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.

中文翻译：

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基于改进神经网络和因子分析的碰撞严重度预测方法

事故严重性预测已成为交通事故研究中的关键问题。因此，为在该领域取得进展，本研究开发了一个完整的人工神经网络并结合了改进的元启发式算法，并对其结构，训练功能，因子分析和比较结果进行了测试。使用I5（华盛顿州州际公路在2011–2015年期间）的数据进行拟合和预测，并在设置了理论上的三层神经网络（NN）之后，使用了改进的粒子群优化（PSO）方法提供了自适应惯性权重来优化神经网络，最后，对不同的自适应策略进行了比较。结果表明，尽管算法在预测中产生了几乎相同的准确性，反向传播方法与PSO中的非线性惯性权重设置相结合，产生了快速的全局和精确的局部最优搜索，从而证明了对整个模型解释的更好理解，这最适合模型，最后，因子分析表明，与道路有关的因素，尤其是与车辆有关的因素，比与道路有关的变量更为重要。这项研究中开发的方法可以应用于交通事故的大数据分析，并可以用作决策者和交通安全研究人员的快速实用工具。因子分析表明，非道路相关因素，特别是车辆相关因素比道路相关变量更为重要。这项研究中开发的方法可以应用于交通事故的大数据分析，并可以用作决策者和交通安全研究人员的快速实用工具。因子分析表明，非道路相关因素，特别是车辆相关因素比道路相关变量更为重要。这项研究中开发的方法可以应用于交通事故的大数据分析，并可以用作决策者和交通安全研究人员的快速实用工具。