Train Delay Prediction Systems: A Big Data Analytics Perspective
| Autor: | Nadia Mazzino, Giorgio Clerico, Federico Papa, Carlo Dambra, Luca Oneto, Davide Anguita, Emanuele Fumeo, Renzo Canepa |
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| Rok vydání: | 2018 |
| Předmět: |
Information Systems and Management
Exploit Computer science Big data Extreme learning machines 02 engineering and technology computer.software_genre Big data analytics Management Information Systems Railway network Deep architecture Shallow architecture Train Delay Prediction systems Information Systems Computer Science Applications1707 Computer Vision and Pattern Recognition 0502 economics and business 0202 electrical engineering electronic engineering information engineering Information system Statistic 050210 logistics & transportation Data processing business.industry 05 social sciences Perspective (graphical) Univariate Computer Science Applications Management information systems 020201 artificial intelligence & image processing Data mining business computer |
| Zdroj: | Big Data Research |
| ISSN: | 2214-5796 |
| DOI: | 10.1016/j.bdr.2017.05.002 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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