Delay prediction system for large-scale railway networks based on big data analytics

Description

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 largescale 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.

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