Traffic safety has always been an important issue in sustainable transportation development, and the prediction of traffic accident severity remains a crucial challenging issue in the domain of traffic safety. A huge variety of forecasting models have been proposed to meet this challenge. These models gradually evolved from linear to nonlinear forms and from traditional statistical regression models to current popular machine learning models. Recently, a machine learning algorithm called Deep Forests based on the decision tree ensemble has aroused widespread concern, which was proposed for the first time by a research team of Nanjing University. This algorithm was proved to be more accurate and robust in comparison with other machine learning algorithms. Motivated by this benefit, this study employs the UK road safety dataset to propose a novel method for predicting the severity of traffic accidents based on the Deep Forests algorithm. To verify the superiority of our proposed method, several other machine learning algorithm-based perdition models were implemented to predict traffic accident severity with the same dataset, and the prediction results show that the Deep Forests algorithm present good stability, fewer hyper-parameters, and the highest accuracy under different level of training data volume. It is expected that the findings from this study would be helpful for the establishment or improvement of effective traffic safety system within a sustainable transportation system, which is of great significance for helping government managers to establish timely proactive strategies in traffic accident prevention and effectively improve road traffic safety.

中文翻译：

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交通事故严重度预测的另一种方法：使用深林算法

交通安全一直是可持续交通发展中的重要问题，交通事故严重程度的预测仍然是交通安全领域中一个至关重要的挑战性问题。已经提出了各种各样的预测模型来应对这一挑战。这些模型逐渐从线性形式发展到非线性形式，从传统的统计回归模型发展到当前流行的机器学习模型。最近，基于决策树集合的机器学习算法“深林”引起了广泛的关注，这是南京大学的一个研究团队首次提出的。与其他机器学习算法相比，该算法被证明更加准确和健壮。受惠于此，这项研究利用英国道路安全数据集，提出了一种基于“深林”算法的交通事故严重程度预测方法。为了验证我们提出的方法的优越性，在基于相同数据集的情况下，还实施了其他几种基于机器学习算法的灭绝模型来预测交通事故的严重程度，并且预测结果表明，深林算法具有良好的稳定性，较少的超参数，并且在不同级别的训练数据量下具有最高的准确性。预期这项研究的结果将有助于在可持续交通系统中建立或改善有效的交通安全系统，