PMID- 36057162
OWN - NLM
STAT- MEDLINE
DCOM- 20220920
LR  - 20220920
IS  - 1879-2057 (Electronic)
IS  - 0001-4575 (Linking)
VI  - 176
DP  - 2022 Oct
TI  - Railway accident prediction strategy based on ensemble learning.
PG  - 106817
LID - S0001-4575(22)00252-4 [pii]
LID - 10.1016/j.aap.2022.106817 [doi]
AB  - 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.
CI  - Copyright (c) 2022 Elsevier Ltd. All rights reserved.
FAU - Meng, Haining
AU  - Meng H
AD  - 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. Electronic address: hnmeng@xaut.edu.cn.
FAU - Tong, Xinyu
AU  - Tong X
AD  - School of Computer Science and Engineering, Xi'an University of Technology, 
      Xi'an, Shaanxi 710048, China.
FAU - Zheng, Yi
AU  - Zheng Y
AD  - School of Computer Science and Engineering, Xi'an University of Technology, 
      Xi'an, Shaanxi 710048, China.
FAU - Xie, Guo
AU  - Xie G
AD  - School of Automation and Information Engineering, Xi'an University of Technology, 
      Xi'an, Shaanxi 710048, China.
FAU - Ji, Wenjiang
AU  - Ji W
AD  - School of Computer Science and Engineering, Xi'an University of Technology, 
      Xi'an, Shaanxi 710048, China.
FAU - Hei, Xinhong
AU  - Hei X
AD  - School of Computer Science and Engineering, Xi'an University of Technology, 
      Xi'an, Shaanxi 710048, China. Electronic address: heixinhong@xaut.edu.cn.
LA  - eng
PT  - Journal Article
DEP - 20220831
PL  - England
TA  - Accid Anal Prev
JT  - Accident; analysis and prevention
JID - 1254476
SB  - IM
MH  - *Accidents, Traffic/prevention & control
MH  - Algorithms
MH  - Humans
MH  - Machine Learning
MH  - Neural Networks, Computer
MH  - *Railroads
OTO - NOTNLM
OT  - Accident prediction
OT  - Accident prevention
OT  - AdaBoost
OT  - Bagging
OT  - Data imputation
OT  - Ensemble learning
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/09/04 06:00
MHDA- 2022/09/21 06:00
CRDT- 2022/09/03 18:14
PHST- 2021/12/31 00:00 [received]
PHST- 2022/08/19 00:00 [revised]
PHST- 2022/08/20 00:00 [accepted]
PHST- 2022/09/04 06:00 [pubmed]
PHST- 2022/09/21 06:00 [medline]
PHST- 2022/09/03 18:14 [entrez]
AID - S0001-4575(22)00252-4 [pii]
AID - 10.1016/j.aap.2022.106817 [doi]
PST - ppublish
SO  - Accid Anal Prev. 2022 Oct;176:106817. doi: 10.1016/j.aap.2022.106817. Epub 2022 
      Aug 31.