Prediction of high-speed train delay propagation based on causal text information

The delay-causing text data contain valuable information such as the specific reasons for the delay,location and time of the disturbance,which can provide an efficient support for the prediction of train delays and improve the guidance of train control efficiency.Based on the train operation data and delay-causing data of the Wuhan-Guangzhou high-speed railway,the relevant algorithms in the natural language processing field are used to process the delay-causing text data.It also integrates the train operatingenvironment information and delay-causing text information so as to develop a cause-based train delay propagation prediction model.The Word2vec model is first used to vectorize the delay-causing text description after word segmentation.The mean model or the term frequency-inverse document frequency-weighted model is then used to generate the delay-causing sentence vector based on the original word vector.Afterward,the train operating-environment features and delay-causing sentence vector are input into the extreme gradient boosting(XGBoost)regression algorithm to develop a delay propagation prediction model.In this work,4 text feature processing methods and 8 regression algorithms are considered.The results demonstrate that the XGBoost regression algorithm has the highest prediction accuracy using the test features processed by the continuous bag of words and the mean models.Compared with the prediction model that only considers the train-operating-environment features,the results show that the prediction accuracy of the model is significantly improved with multi-ple regression algorithms after integrating the delay-causing feature.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.