School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China; Shaanxi Key Lab Network Computer and Security Technology, Xi'an, Shaanxi 710048, China.
School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
Accid Anal Prev. 2022 Oct;176:106817. doi: 10.1016/j.aap.2022.106817. Epub 2022 Aug 31.
Railway accident prediction is of great significance for establishing an early warning mechanism and preventing the occurrences of accidents. Safety agencies rely on prediction models to design railroad risk management strategies. Based on historical railway accident data, an ensemble learning strategy for accident prediction is proposed. Firstly, an improved K-nearest neighbors (KNN) data imputation algorithm is proposed to solve the problem of missing data in the dataset. Then, to reduce the impact of imbalanced data on prediction performance, an AdaBoost-Bagging method is presented. Finally, according to the feature importance in the prediction model, accident features are ranked to identify new insights into the cause of the accident. The AdaBoost-Bagging prediction method is applied to the Federal Railroad Administration (FRA) dataset. The application results show that, compared with Artificial Neural Network (ANN), XGBoost, GBDT, Stacking and AdaBoost methods, AdaBoost-Bagging method has a smaller prediction error and faster inference time in predicting railway accidents. Accuracy, Precision, Recall and F1-score are 0.879, 0.879, 0.883 and 0.881 respectively, and the inference time is reduced by 23.38%, 12.15%, 6.66%, 3.17% and 11.41% respectively. The prediction method can well mine important features of railway accidents without knowing the accident mechanism or the relationship between various railway accidents and factors, e.g., the critic risk factors related to derailment and collision accidents are investigated in the prediction. The findings will be helpful to the prevention and management of railway accidents.
铁路事故预测对于建立预警机制和防止事故发生具有重要意义。安全机构依靠预测模型来设计铁路风险管理策略。基于历史铁路事故数据，提出了一种用于事故预测的集成学习策略。首先，提出了一种改进的 K-最近邻（KNN）数据插补算法，以解决数据集内缺失数据的问题。然后，为了减少不平衡数据对预测性能的影响，提出了一种 AdaBoost-Bagging 方法。最后，根据预测模型中的特征重要性，对事故特征进行排名，以识别导致事故的新原因。AdaBoost-Bagging 预测方法应用于联邦铁路管理局（FRA）数据集。应用结果表明，与人工神经网络（ANN）、XGBoost、GBDT、Stacking 和 AdaBoost 方法相比，AdaBoost-Bagging 方法在预测铁路事故时具有更小的预测误差和更快的推理时间。准确率、精确率、召回率和 F1 得分分别为 0.879、0.879、0.883 和 0.881，推理时间分别减少了 23.38%、12.15%、6.66%、3.17%和 11.41%。该预测方法可以在不了解事故机制或各种铁路事故与因素之间关系的情况下，很好地挖掘铁路事故的重要特征，例如，在预测中调查与脱轨和碰撞事故相关的关键风险因素。研究结果将有助于铁路事故的预防和管理。
