Stochastic prediction of train delays in real-time using Bayesian networks

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 complex stochastic inference between random variables. Moreover, it allows updating the probability distributions and reducing the uncertainty of future train delays in real time under the assumption that more information continuously becomes available from the monitoring system. The dynamics of a train delay over time and space is presented as a stochastic process that describes the evolution of the time-dependent random variable. This approach is further extended by modelling the interdependence between trains that share the same infrastructure or have a scheduled passenger transfer. The model is applied on a set of historical traffic realisation data from the part of a busy corridor in Sweden. We present the results and analyse the accuracy of predictions as well as the evolution of probability distributions of event delays over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the continuously changing delays

Evaluating and ranking infrastructure manager strategies using the combined AHP/DEA method

Restructuring of European Railway companies has resulted in creating new subjects within railway systems - infrastructure managers (IM) on the one hand and railway operators on the other. Each of these subjects strives for efficient operation within the boundaries dictated by regulatory bodies or government directly. In this paper we examine the efficiency and rank different business strategies of railway infrastructure managers using combined AHP/DEA method. Different strategies include different number of paths allocated (diferent capacity utilization index), different ratio of paths allocated to passenger and freight trains in case of demand for paths being higher than the capacity of the line and infrastructure access charges based on different principles. Each strategy examined is subjected to constrains such as the maximum capacity of the line and public service obligation considering the obligatory number of passenger trains in the timetable defined by the public authorities. The method applied for evaluating and ranking different strategies is a combined AHP/DEA method. Each strategy of the IM is regarded as a decision making unit (DMU) and as such, in the first stage of the model, paired with each of the remaining DMU’s. The Data Envelopment Analysis (DEA) is run for each pair of units separately. In the second stage, the pair wise evaluation matrix generated in the first stage is utilized to rank scale the units via the Analytical Hierarchical Process (AHP). The results can be used for evaluation of efficient use of infrastructure capacity and financial efficiency of IM simultaneously

Train delay evolution as a stochastic process

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 as a stochastic process that describes the evolution of the time-dependent random variable. Probability distribution of an arrival delay in a station changes over time in discrete steps as more information becomes available. We consider and compare the results and computational requirements of two discrete state space formulations. Moreover, we test the applicability of modelling train delays as a non-stationary Markov chain, meaning that the probability of a state change depends on the moment of transition. The model is applied on a set of historical traffic realisation data from the part of the high-speed corridor between Beijing and Shanghai. We analyse the accuracy of predictions as well as the evolution of probability distributions of all events over time. The presented method is important for making better predictions for train traffic, that are not only based on static, offline collected data, but are able to positively include the dynamic characteristics of the ever-changing delays, thus increasing the reliability of prediction by 71%

Rescheduling models for railway traffic management in large-scale networks

In the last decades of railway operations research, microscopic models have been intensively studied to support traffic operators in managing their dispatching areas. However, those models result in long computation times for large and highly utilized networks. The problem of controlling country-wide traffic is still open since the coordination of local areas is hard to tackle in short time and there are multiple interdependencies between trains across the whole network. This work is dedicated to the development of new macroscopic models that are able to incorporate traffic management decisions. Objective of this paper is to investigate how different levels of detail and number of operational constraints may affect the applicability of models for network-wide rescheduling in terms of quality of solutions and computation time. We present four different macroscopic models and test them on the Dutch national timetable. The macroscopic models are compared with a state-of-theart microscopic model. Trade-off between computation time and solution quality is discussed on various disturbed traffic conditions