Accurate train delay prediction is vital for effective railway traffic planning and management
as well as for providing satisfactory passenger service quality. Despite significant advances …

This paper presents a carefully designed train delay prediction model, called FCLL-Net,
which combines a fully-connected neural network (FCNN) and two long short-term memory …

Due to the increase in railway traffic, trains increasingly need to adhere to their scheduled
path to avoid extensive propagation of delays. This paper presents the findings of a …

Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the
three main consequences of disruptions and disturbances during train operations, namely …

Multi-line stations (MLSs) are the intersections of different railway lines; they are crucial for
delay propagation in railway networks. Therefore, the precise prediction of train arrival …

As a vital component of train operational control, train delay propagation pattern discovery is
critically important for both railway controllers and passengers. In this study, we present a …

Train delay evolutions exhibit different patterns (ie, increasing delays, decreasing delays, or
unchanged delays), because of the effects of stochastic disturbances and pre-scheduled …

Intermodal freight rail operations represent a complex stochastic system that is impacted by
disruptions and disturbances from diverse causes like extreme weather events, unplanned …

Advanced decision support for rail traffic control is significant for enhancing the safety and
quality of railway transport service. Data-driven methods have shown powerful learning …

Understanding the impact of various factors on train arrival delays is a prerequisite for
effective railway traffic operating control and management. Existing studies analyze the train …