In-Station Train Movements Prediction: from Shallow to Deep Multi Scale Models

[1]  Luca Oneto,et al.  In-Station Train Dispatching: A PDDL+ Planning Approach , 2021, ICAPS.

[2]  Luca Oneto,et al.  Model Selection and Error Estimation in a Nutshell , 2020, Modeling and Optimization in Science and Technologies.

[3]  Liping Fu,et al.  Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal , 2019, IEEE Access.

[4]  Davide Anguita,et al.  A dynamic, interpretable, and robust hybrid data analytics system for train movements in large-scale railway networks , 2019, International Journal of Data Science and Analytics.

[5]  Francesco Corman,et al.  Stochastic prediction of train delays in real-time using Bayesian networks , 2018, Transportation Research Part C: Emerging Technologies.

[6]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[7]  Leslie N. Smith,et al.  Cyclical Learning Rates for Training Neural Networks , 2015, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[8]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[9]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[10]  Shai Ben-David,et al.  Understanding Machine Learning: From Theory to Algorithms , 2014 .

[11]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[12]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[13]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.