Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a
large number of domains, including railways. In this paper, we present a systematic literature …

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 …

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway
transport is no exception. However, due to the plethora of different new terms and meanings …

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 …

The aim of this paper is to review existing publicly available and open artificial intelligence
(AI) oriented datasets in different domains and subdomains of the railway sector. The …

External and internal factors can cause disturbances or disruptions in daily train operations,
leading to deviations from official timetables and passenger delays. As a result, efficient train …

Railway operations are subject to deviations from the planned schedule, ie, delays. In those
situations, high-quality traffic control actions are needed to reduce the delays. Existing …

Railways speedily transport many people and goods nationwide, so railway accidents can
pose immense damage. However, the infrastructure of railways is so complex that its …

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 …

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 …