Publications

BOOK CHAPTERS

  • Oneto, L. and Fumeo, E. and Clerico, C. and Canepa, R. and Papa, F. and Dambra, C. and Mazzino, N. and Anguita. D., Innovative Applications of Big Data in the Railway Industry, Kohli, S. and Senthil, A. V. and Easton, J. M. and Roberts, C., IGI Global, Big Data Analytics for Train Delay Prediction: A case study in the Italian Railway Network, 2017.

JOURNALS

  • Oneto, L. and Buselli, I. and Lulli, A. and Canepa, R. and Petralli, S. and Anguita, D., International Journal of Data Science and Analytics, Num:-, Pag:-, A Dynamic, Interpretable, and Robust Hybrid Data Analytics System for Train Movements in Large-Scale Railway Networks, Vol:-, 2019.

  • Oneto, L. and Fumeo, E. and Clerico, C. and Canepa, R. and Papa, F. and Dambra, C. and Mazzino, N. and Anguita. D., Big Data Research, Pag:54-64 - Train Delay Prediction Systems: a Big Data Analytics Perspective, Vol:11 - 2018.

CONFERENCES

  • Minisi, S. and Garrone, A. and Oneto, L. and Canepa, R. and Dambra, C. and Anguita, D., European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Simple Non Regressive Informed Machine Learning Model for Predictive Maintenance of Railway Critical Assets, 2022.

  • Garrone, A. and Minisi, S. and Oneto, L. and Dambra, C. and Borinato, M. and Sanetti, P. and Vignola, G. and Papa, F. and Mazzino, N. and Anguita, D., International Conference on System-Integrated Intelligence. Intelligent, flexible and connected systems in products and production (SysInt), Simple Non Regressive Informed Machine Learning Model for Prescriptive Maintenance of Track Circuits in a Subway Environment, 2022.

  • Cardellini, M. and Maratea, M. and Vallati, M. and Boleto, G. and Oneto, L., International Symposium on Combinatorial Search (SoCS), A Planning-based Approach for In-Station Train Dispatching, 2021.

  • Cardellini, M. and Maratea, M. and Vallati, M. and Boleto, G. and Oneto, L., International Conference on Computational Science (ICCS), An Efficient Hybrid Planning Framework for In-Station Train Dispatching, 2021.

  • Cardellini, M. and Maratea, M. and Vallati, M. and Boleto, G. and Oneto, L., International Conference on Automated Planning and Scheduling (ICAPS), In-Station Train Dispatching: a PDDL+ Planning Approach, 2021.

  • Consilvio, A. and Sanetti, P. and Anguita, D. and Crovetto, C. and Dambra, C. and Oneto, L. and Papa, F. and Sacco, N., International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Prescriptive Maintenance of Railway Infrastructure: From Data Analytics to Decision Support, 2019.

  • Ducuing, C. and Oneto, L. and Canepa, R., European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Fairness and Accountability of Machine Learning Models in Railway Market: are Applicable Railway Laws Up to Regulate Them?, 2019.

  • Spigolon, R. and Oneto, L. and Anastasovski, D. and Fabrizio, N. and Swiatek, M. and Canepa, R. and Anguita, D., INNS Big Data and Deep Learning (INNSBDDL), Improving Railway Maintenance Actions with Big Data and Distributed Ledger Technologies., 2019.

  • Oneto, L. and Buselli, I. and Sanetti, P. and Canepa, R. and Petralli, S. and Anguita, D., INNS Big Data and Deep Learning (INNSBDDL), Restoration Time Prediction in Large Scale Railway Networks: Big Data and Interpretability., 2019.

  • Oneto, L. and Buselli, I. and Luli, A. and Canepa, R. and Petralli, S. and Anguita, D., INNS Big Data and Deep Learning (INNSBDDL), Train Overtaking Prediction in Railway Networks: a Big Data Perspective., 2019.

  • Schlegel, U. and Jentner, W. and Buchmueller, J. and Cakmak, E. and Castiglia, G. and Canepa, R. and Petralli, S. and Oneto, L. and Keim, D. A. and Anguita, D., INNS Big Data and Deep Learning (INNSBDDL), Visual Analytics for Supporting Conflict Resolution in Large Railway Networks, 2019.

  • Lulli, A. and Oneto, L. and Canepa, R. and Petralli, S. and Anguita, D., IEEE International Conference on Data Science and Advanced Analytics (DSAA), Large-Scale Railway Networks Train Movements: a Dynamic, Interpretable, and Robust Hybrid Data Analytics System, 2018.