In this paper we present a stochastic model for predicting the propagation of train delays
based on Bayesian networks. This method can efficiently represent and compute the …

We propose machine learning models that capture the relation between passenger train
arrival delays and various characteristics of a railway system. Such models can be used at …

Current train delay prediction systems do not take advantage of state-of-the-art tools and
techniques for handling and extracting useful and actionable information from the large …

We present a Bayesian network-(BN) based train delay prediction model to tackle the
complexity and dependency nature of train operations. Three different BN schemes, namely …

Current train delay (TD) prediction systems do not take advantage of state-of-the-art tools
and techniques for handling and extracting useful and actionable information from the large …

In this paper we present a method for modelling uncertainty of train delays based on a
Markov stochastic process. The dynamics of a train delay over time and space is presented …

Secondly, I would like to offer a special thanks to my colleague, Yong Cui. He gave me the
framework of the simulation model developed in his own dissertation; hence I could quickly …

This PhD thesis is the outcome of 5 years of work, the contribution of many people and a lot
of choices. Doing this PhD was only possible after I chose to move from Brazil to the …

Trajectory elements of train movement, such as departure and running times, are subject to
random influences, which can lead to disruption of the arrivals. Analysis of the …

Delay prediction is significant for the well‐organised operation of trains. Meanwhile, the
accurate prediction is interfered by comprehensive factors in practice, hence there are …