Abstract 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 operating-environment 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 multiple regression algorithms after integrating the delay-causing feature. 

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

Train station delay prediction is always one of the core research issues in high-speed railway dispatching. Reliable prediction of station delay can help dispatchers to accurately estimate the train operation status and make reasonable dispatching decisions to improve the operation and service quality of rail transit. The delay of one station is affected by many factors, such as spatiotemporal factor, speed limitation or suspension caused by strong wind or bad weather, and high passenger flow caused by major holiday. But previous studies have not fully combined the spatiotemporal characteristics of station delay and the impact of external factors. This paper makes good use of the train operation data, proposes the multiattention mechanism to capture the spatiotemporal characteristics of train operation data and process the external factors, and establishes a Multiattention Train Station Delay Graph Convolution Network (MATGCN) model to predict the train delay at high-speed railway stations, so as to provide references for train dispatching and emergency plan. This paper uses real train operation data coming from China high-speed railway network to prove that our model is superior to ANN, SVR, LSTM, RF, and TSTGCN models in the prediction effect of MAE, RMSE, and MAPE.

Prediction of Train Station Delay Based on Multiattention Graph Convolution Network

Modeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

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Yafei Hou

Papers

Delay recovery model for high-speed trains with compressed train dwell time and running time