Advanced analytics for train delay prediction systems by including exogenous weather data
| Autor: | Carlo Dambra, Luca Oneto, Renzo Canepa, Nadia Mazzino, Emanuele Fumeo, Federico Papa, Davide Anguita, Giorgio Clerico |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Information Systems and Management
Computer science Railway Weather forecasting 02 engineering and technology computer.software_genre Machine learning Data modeling Intelligent Transportation Systems Data-Driven Algorithms Delay Prediction Exogenous Weather Data Information Systems Artificial Intelligence 0502 economics and business 0202 electrical engineering electronic engineering information engineering Information system Intelligent transportation system 050210 logistics & transportation Artificial neural network business.industry 05 social sciences Ensemble learning Kernel method Analytics 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | DSAA 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) |
| Popis: | State-of-the-art train delay prediction systems neither exploit historical data about train movements, nor exogenous data about phenomena that can affect railway operations. They rely, instead, on static rules built by experts of the railway infrastructure based on classical univariate statistics. The purpose of this paper is to build a data-driven train delay prediction system that exploits the most recent analytics tools. The train delay prediction problem has been mapped into a multivariate regression problem and the performance of kernel methods, ensemble methods and feed-forward neural networks have been compared. Firstly, it is shown that it is possible to build a reliable and robust data-driven model based only on the historical data about the train movements. Additionally, the model can be further improved by including data coming from exogenous sources, in particular the weather information provided by national weather services. Results on real world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the current state-of-the-art train delay prediction systems. Moreover, the performed simulations show that the inclusion of weather data into the model has a significant positive impact on its performance. |
| Databáze: | OpenAIRE |
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