Railway accident prediction strategy based on ensemble learning

阿达布思 集成学习 人工神经网络 计算机科学 机器学习 人工智能 推论 数据挖掘 预测建模 工程类 支持向量机
作者
Haining Meng,Xinyu Tong,Yi Zheng,Guo Xie,Wenjiang Ji,Xinhong Hei
出处
期刊:Accident Analysis & Prevention [Elsevier BV]
卷期号:176: 106817-106817 被引量:14
标识
DOI:10.1016/j.aap.2022.106817
摘要

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.
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