Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability
| Autor: | Simone Petralli, Renzo Canepa, Luca Oneto, Paolo Sanetti, Irene Buselli, Davide Anguita |
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| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Computer science business.industry 020209 energy Scale (chemistry) 05 social sciences Big data 02 engineering and technology Prediction system Asset (computer security) Industrial engineering Work (electrical) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Train business Real world data Interpretability |
| Zdroj: | Recent Advances in Big Data and Deep Learning-Proceedings of the INNS Big Data and Deep Learning Conference INNSBDDL2019, held at Sestri Levante, Genova, Italy 16-18 April 2019 Proceedings of the International Neural Networks Society Proceedings of the International Neural Networks Society-Recent Advances in Big Data and Deep Learning Proceedings of the International Neural Networks Society ISBN: 9783030168407 INNSBDDL |
| ISSN: | 2661-8141 2661-815X |
| DOI: | 10.1007/978-3-030-16841-4_14 |
| Popis: | Every time an asset of a large scale railway network is affected by a failure or maintained, it will impact not only the single asset functional behaviour but also the normal execution of the railway operations and trains circulation. In this framework, the restoration time, namely the time needed to restore the asset functionality, is a crucial information for handling and reducing this impact. In this work we deal with the problem of building an interpretable and reliable restoration time prediction system which leverages on the large amount of data generated by the network, on other freely available exogenous data such as the weather information, and the experience of the operators. Results on real world data coming from the Italian railway network will show the effectiveness and potentiality of our proposal. |
| Databáze: | OpenAIRE |
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