A Novel Model for Train Operation Adjustment in High-Speed Railway based on Max-plus Algebra

The running time of high-speed train is generally not late, and it can run normally in most cases. However, when severe weather conditions, train components and equipment, accidents or emergencies occur, it will lead to train operation delay and traffic congestion. Therefore, when an accident occurs, we need to adjust the train time or route timely and accurately. As an important algebraic system, max-plus algebra is widely used in the field of industrial production control. In industrial production, the most production mode is the discrete system , but the characteristics and the ability of discrete systems depends on the periodic of system and the number of workpiece produced by the system in unit time, and the characteristics of the system are closely related to the properties of the matrix, especially, the eigenvalues and eigenvectors of the matrix in the sense of max-plus algebra. Therefore, this paper studies the max-plus algebra theory and the solution of eigenvalues and eigenvectors of matrices in the sense of max-plus algebra, establish the operation time matrix to optimize the train operation adjustment model of high-speed railway, and analyze the failure propagation model.

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

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.

An Integration of Train Timetabling, Platforming and Routing-Based Cooperative Adjustment Methodology for Dealing with Train Delay

Train delay is a serious issue that can spread rapidly in the railway network leading to further delay of other trains and detention of passengers in stations. However, the current practice in the event of the trail delay usually depends on train dispatcher’s experience, which cannot manage train operation effectively and may have safety risks. The application of intelligent railway monitor and control system can improve train operation management while increasing railway safety. This paper presents a methodology in which train timetabling, platforming and routing models are combined by studying the real-time adjustment and optimization of high-speed railway in the case of the train delay in order to produce a cooperative adjustment algorithm so that the train operation adjustment plan can be obtained. MATLAB computer programs have been developed based on the proposed methodology and adjustment criteria have been established from knowledge data bases in order to calculate optimized solutions. A case study is used to demonstrate the proposed methodology. The results show that the proposed method can quickly adjust the train operation plan in the case of the train delay, restore the normal train operation order, and reduce the impact of train delay on railway network effectively and efficiently.