Akademik Veri Yönetim Sistemi
16th World Conference on Transport Research (WCTR), Montreal, Kanada, 17 - 21 Temmuz 2023
Delay is a service quality measure in urban and intercity rail systems,
reducing their operational efficiency and reliability. Accurate
estimation of train delays is one of the main challenges in railway
operation due to complex interrelations among the operating elements and
inherent uncertainty associated with their behavior. Markov chains have
recently been used, in this respect, to model variability in departure
and arrival delays of trains in running and dwelling processes. In this
study, we measured the performance of delay-based homogeneous and
non-homogeneous Markov models and benchmarked them with artificial
neural networks, support vector machine, and random forest models. The
results show that Markov models can make comparable predictions.
Moreover, due to their interpretability and transparency, the Markov
chains allows us to gain insights into delay transitions in various
processes and to make statistical inferences, such moment estimations
and occurrence probabilities for delays. Model comparisons also imply
that the process of train running and dwelling is memoryless.