Delay Prediction System for Large-Scale Railway Networks Based on Big Data Analytics

Abstract

State-of-the-art train delay prediction systems do not exploit historical train movements data collected by the railway information systems, but they rely on static rules built by expert of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven train delay prediction system for large-scale railway networks which exploits the most recent Big Data technologies and learning algorithms. In particular, we propose a fast learning algorithm for predicting train delays based on the Extreme Learning Machine that fully exploits the recent in-memory large-scale data processing technologies. Our system is able to rapidly extract nontrivial information from the large amount of data available in order to make accurate predictions about different future states of the railway network. Results on real world data coming from the Italian railway network show that our proposal is able to improve the current state-of-the-art train delay prediction systems.

Machine learning methods for short-term probability of default: A comparison of classification, regression and ranking methods

“…An application field that shares this goal of overdue estimation is train delay estimation. In this setting, recent research efforts include the use of Support Vector Regression (SVR) (Markovi c et al, 2015), Extreme Learning Machines (MLS) (Oneto et al, 2017), neural network models (Yaghini et al, 2013), a Fuzzy Petri Net (FPN) (Milinkovi c et al, 2013) and graph-based approaches (Berger et al, 2011;Kecman & Goverde, 2015).…”

Strategies for the Use of Data and Algorithm Approaches in Railway Traffic Management

“…[12] use support vector regression to identify the relationship between various system characteristics and train delay; [14]use artificial neural networks to predict delay, achieving high accuracy in an application to Iranian railways. A major flaw in these approaches for our application can be the computational time required for the analysis of very large data sets; [13] propose a fast learning algorithm based on the 'Extreme Learning Machine', which can extract relevant information quickly to make accurate predictions about future network states, they show the method can improve the current prediction systems implemented in Italian railway networks.…”

Towards a General Prediction System for the Primary Delay in Urban Railways

“…According to [2] and [10], a railway network is considered as a graph where nodes indicate a series of checkpoints C = {C1, C2, … , Cn} successively connected. For any checkpoint , a train arrives at the time and departs at a time in the scheduled timetable, where t denotes a timestamp.…”