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Train Delay Prediction Systems: A Big Data Analytics Perspective
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DOI：10.1016/j.bdr.2017.05.002.png)
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Abstract
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Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (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. (c) 2017 Elsevier Inc. All rights reserved.
Keywords:
Railway network
Train Delay Prediction systems
Big data analytics
Extreme learning machines
Shallow architecture
Deep architecture
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