Research Information System
IEEE Transactions On Intelligent Transportation Systems, pp.1-10, 2021 (Journal Indexed in SCI)
Uncertainty affects
the overall performance of railway systems. It can be captured tracking the successive
departure and arrival events of trains at stations in the scheduled railways.
Markov chains has shown to be an effective means to model variabilities in the
departure and arrival delays of trains. The empirical data of actual operations
are used to develop the Markov matrices. The transitions between delay states
comprise the internal dynamics of running and dwelling processes in
probabilistic terms, presenting particular patterns of deterioration, recovery
and state keeping in the prevailing timetable setting. All these features
conform to the practice of railway operations as well as train dispatchers and
drivers. Hence, delay-based Markov chains becomes a simple and effective tool to
predict delays in the following events of trains and distribution of delays over
a longer period of time using simple performance measures. We have developed
homogeneous and non-homogeneous Markov models and test their performances using
the actual data collected in the Netherlands railway network. The analysis and
evaluation show that the railway corridor considered in this study performs
well and the corresponding Markov chain models can be used effectively to predict
train delays and to evaluate timetable robustness. The outcomes of the homogeneous
and non-homogeneous model are evaluated with respect to the observed data
resulting that the non-homogeneous model performs better than the homogeneous
model.