Study On Train Delay Propagation Mechanism And Models In High-speed Railways With Data-driven Approaches

Analyzing delay propagation patterns plays an important role in reducing the impact of delays and improving the level of intelligent dispatching and the quality of transport services in high-speed railway(HSR).Based on the operation records of high-speed trains in China,this thesis examined the delay distribution characteristics,delay propagation at time direction,delay propagation at space direction,and the macro and micro effects of disruptions on train operation considering delay propagation at time and space directions by using big data and artificial intelligence technologies.The expected results can enrich the theories of train delay propagation and train dispatching and commanding,and provide theoretical support for the realization of intelligent dispatching in high-speed railway.The main jobs and achievements in the thesis include:1)The influencing factors of train delays and the train punctuality in other countries were first examined from existing literatures and public data.In addition,based on the real-world data of high-speed trains in China,the operational features of high-speed trains,the spatiotemporal and duration distribution of delays,and the running time and dwelling time distribution of trains were investigated,which could provide the railway managers,passengers,and train dispatchers a macro understanding of the basic information of high-speed train delays.2)Patterns and predictive model for train delay propagation at time direction(the influence of the primary delay train on its succeeding trains)were studied with statistical methods and machine learning models to reveal the delay propagation mechanism at time dimension from the statistical results and the primary delay influencing models.First,the delay propagation mechanism at time direction was clarified.Then,the primary delays were clustered according to their own characteristics and timetable parameters.After clustering,probabilistic distribution models were established to examine the influence of primary delays of each category,including the number of affected trains(NAT)and the total delay times(TDT).Finally,the model validation results based on the future data showed that the model could well fit the data about NAT and TDT,and had strong application ability in the future prediction task.3)Patterns and predictive model for train delay propagation at space direction were studied with statistical methods and machine learning models to reveal the delay propagation mechanism at space dimension from the statistical results,recovery model and delay increase model.Firstly,the delay increasing and delay decreasing(recovery)characteristics in(at)section(station)were analyzed.Then,a predictive random forest model that considered the supplement times in section and at station and primary delay severity was established to predict the delay recovery of high-speed trains once they were delayed;the predictive results of the training and testing dataset showed that random forest model could well fit the delay recovery data,and had high predictive accuracy compared against other machine learning models.Finally,in view of the shortcomings of machine learning models by offline learning and online learning in real-time dispatching,a hybrid prediction model for delay increase was established combining support vector machine and filtering technology.The verification results of the hybrid model showed that filtering technology could effectively solve the shortcomings of machine learning model,including much time-consuming by online training and incapability of delay increase situation by offline training,and the proposed model could significantly improve the accuracy of a traditional machine learning model in delay increase prediction.Accurate prediction of train delay propagation at space direction was realized by combing the recovery model and delay increase model.4)Considering the spatiotemporal propagation of train delays,a real-time prediction model for the macro-effect of disruptions was established to reveal the spatialtemporal propagation mechanism of train delays with Bayesian networks(BN).First,the indexes for measuring the influence on disruptions on train operations were determined,including the primary delay time(L),the number of affected trains(N),and the total delay time(T).Then,the BN structure was determined by considering the real-time dispatching requirements,the domain(expert)knowledge,and the structure learned from data using a heuristic algorithm.Next,the BN model was tested using data from two high-speed railway lines,which showed that the proposed BN model,with superior performance compared against other prediction models,had high prediction accuracy about the factor L,N,and T,and it could be applied to high-speed railway lines with different operational features.5)Considering the spatiotemporal propagation of train delays,real-time prediction models for the micro effect of disruptions,i.e.,the train delay,were established to reveal the spatialtemporal propagation mechanism of train delays with deep learning methods.Firstly,considering the interaction between trains,a deep learning train delay prediction model,named FCL-Net,was proposed,which combines two specific neural network structures,i.e.,fully-connected neural networks(FCNN)and long short-term memory(LSTM),to separately consider the operational and non-operational factors of delay influencing factors.The prediction accuracy,expansibility and efficiency of the model were tested using the operation records of two HSR lines in China,and some common train delay prediction models were selected as the benchmark of the proposed model.The results showed that,compared against the benchmark model,the proposed FCL-Net model,which took train interactions into account,outperformed other widely used train delay prediction models on two HSR lines with different operational features,in terms of both regression and classification model evaluation metrics.However,the FCL-Net model only considered the correlation between trains,as the train operation depends not only on its preceding trains,but also on its own past states.To address this drawback,a deep learning model based on convolutional neural network(CNN),named,FCC-Net,was proposed to predict train delays.The model also combines two kinds of neural networks,i.e.,the FCNN and CNN,to deal with train delay influencing factors with different attributes separately.The FCC-Net model took timetables as images,and used the image processing ability of CNN model to capture the self-correlation and cross-correlation of train operations,for further improvement of train delay prediction.The model testing results showed that the FCC-Net,because of considering the self-correlation and cross-correlation of train operation,had higher prediction accuracy than FCL-Net model on data from two HSR lines.