مقاله انگلیسی رایگان در مورد پیش بینی تاخیر قطار با استفاده از شبکه های بیزی – الزویر 2018

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.

Introduction

Accurate prediction of train delays (deviations from timetable) is an important requirement for proactive and anticipative realtime control of railway traffic. Traffic controllers need to predict the arrival times of the trains within (or heading towards) their area in order to control the feasibility of timetable realisation. Similarly, the transport controllers on behalf of train operating companies may use the predictions to estimate the feasibility of planned passenger transfers, as well as rolling-stock and crew circulation plans. Valid estimates of arrival and departure times are therefore important for preventing or reducing delay propagation, managing connections, and providing reliable passenger information. The difficulty for predicting the train event times comes from the uncertainty and unpredictability of process times in railway traffic. The models for real-time traffic control have so far mostly focused on overcoming the great combinatorial complexity of train rescheduling (Corman et al., 2014b; Meng and Zhou, 2014; Törnquist and Persson, 2007), delay management (Dollevoet et al., 2014) and rolling-stock and crew rescheduling (Nielsen et al., 2012; Potthoff et al., 2010). The developed approaches are able to solve complex instances in real-time, however they typically assume perfect deterministic knowledge of the input traffic state and subsequent traffic evolution. In recent years, the uncertainty of train event times has been recognised as one of the major obstacles for computing feasible and implementable solutions for rescheduling problems in railway traffic (Corman and Meng, 2014; Quaglietta et al., 2013). The uncertainty of an event is usually represented by the probability distribution of its realisation. However, most of the existing approaches assume fixed probability distributions for train delays and do not consider the effect that real-time information on train positions and delays may have on (the parameters of) the corresponding distributions. In order to create realistic online tools for real-time traffic management, the dynamics of uncertainty of delays needs to be considered. When new information about train positions and delays becomes available, the uncertainty for predicting subsequent events is typically reduced. The main objective of this paper is to examine the effect that the prediction horizon and incoming information about a running train may have on the predictability of subsequent arrival and departure times of all trains. In other words, we try to give an answer to the question: how does the probability distribution of delay of an event change over time?