No abstract

No abstract

No 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.

Current Train Delay Prediction Systems (TDPSs) do not take advantage of state-of-the-art tools and techniques for extracting useful insights from large amounts of historical data collected by the railway information systems. Instead, these systems rely on static rules, based on classical univariate statistic, built by experts of the railway infrastructure. The purpose of this book chapter is to build a data-driven TDPS for large-scale railway networks, which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.