A Novel Markov Model for Near-Term Railway Delay Prediction

Abstract

Predicting the near-future delay with accuracy for trains is momentous for railway operations and passengers' traveling experience. This work aims to design prediction models for train delays based on Netherlands Railway data. We first develop a chi-square test to show that the delay evolution over stations follows a first-order Markov chain. We then propose a delay prediction model based on non-homogeneous Markov chains. To deal with the sparsity of the transition matrices of the Markov chains, we propose a novel matrix recovery approach that relies on Gaussian kernel density estimation. Our numerical tests show that this recovery

Analyzing the Impacts of Inbound Flight Delay Trends on Departure Delays Due to Connection Passengers Using a Hybrid RNN Model

“…An accurate prediction of delay trends in the near future (1-2 weeks ahead) allows passengers to adjust their travel plans so as to improve their overall travel experiences. Furthermore, an estimate of delay trends enables operations managers to make timely dispatch adjustments and reschedules, especially if dramatic delay increases or decreases are likely [9]. It should be noted that a delay trend analysis is not an estimated time of arrival (ETA).…”