A stochastic multiple area approach for public transport network design. - Bernhard Alt; Ulrich Weidmann 001 (Public Transp., 2011)

Predicting time series of railway speed restrictions with time-dependent machine learning techniques. - Olga Fink; Enrico Zio; Ulrich Weidmann 001 (Expert Syst. Appl., 2013)

Quantifying the reliability of fault classifiers. - Olga Fink; Enrico Zio; Ulrich Weidmann 001 (Inf. Sci., 2014)

Predicting component reliability and level of degradation with complex-valued neural networks. - Olga Fink; Enrico Zio; Ulrich Weidmann 001 (Reliab. Eng. Syst. Saf., 2014)

Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures. - Olga Fink; Enrico Zio; Ulrich Weidmann 001 (IEEE Trans. Reliab., 2015)

A Classification Framework for Predicting Components' Remaining Useful Life Based on Discrete-Event Diagnostic Data. - Olga Fink; Enrico Zio; Ulrich Weidmann 001 (IEEE Trans. Reliab., 2015)

Energy savings in mixed rail traffic rescheduling: an RCG approach. - Ambra Toletti; Valerio De Martinis; Ulrich Weidmann 001 (ITSC, 2016)

Periodic railway timetabling with sequential decomposition in the PESP model. - Sabrina Herrigel; Marco Laumanns; Jácint Szabó; Ulrich Weidmann 001 (J. Rail Transp. Plan. Manag., 2018)

Coordinated railway traffic rescheduling with the Resource Conflict Graph model. - Ambra Toletti; Marco Laumanns; Ulrich Weidmann 001 (J. Rail Transp. Plan. Manag., 2020)

A hybrid stochastic approach for offline train trajectory reconstruction. - Pier Giuseppe Sessa; Valerio De Martinis; Axel Bomhauer-Beins; Ulrich Alois Weidmann; Francesco Corman (Public Transp., 2021)