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1 |
Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر قطار: چشم انداز تحلیل داده های بزرگ-2018 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.
Keywords: Railway network ، Train Delay Prediction systems ، Big data analytics ، Extreme learning machines ، Shallow architecture ، Deep architecture |
مقاله انگلیسی |
2 |
Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر در قطار: چشم انداز تجزیه و تحلیل داده بزرگ-2017 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.
Keywords: Railway network | Train Delay Prediction systems | Big data analytics | Extreme learning machines | Shallow architecture | Deep architecture |
مقاله انگلیسی |