Global sensitivity analysis applied to traffic rescheduling in case of power shortage

• Published in: International journal of transport development and integration Vol. 2; no. 3; pp. 271 - 283
• Main Authors: Saad, Soha, Ossart, Florence, Bigeon, Jean, Sourdille, Etienne, Gance, Harold
• Format: Journal Article
• Language: English
• Published: WIT Press 2018
• Subjects: Electric power; Engineering Sciences; Monte Carlo filtering; rescheduling; sensitivity analysis; regional sensitivity analysis; Sobol indexes; global sensitivity analysis; railway simulation
• ISSN: 2058-8305; 2058-8313
• DOI: 10.2495/tdi-v2-n3-271-283

The present work addresses traffic rescheduling in case of electric infrastructure failure. The power available for train traction is restricted and the traffic must be reorganized according to this constraint. The system behaviour is computed using a dynamic multi-physics railway simulator which gives physi- cal quantities such as the train speed profiles, voltage along the catenary lines and temperatures. The rescheduling problem relies on this non-linear model, with a large number of continuous and discrete variables, constraints on dynamic outputs (typically voltage limits) and a high computation cost. We propose a rescheduling process based on sensitivity analysis in order to analyse the behaviour of this complex system and obtain information about the adjustment operations needed in order to reschedule the traffic in an optimal way. Our approach is based on statistics, with predefined variation ranges of the input parameters. In a first stage, variance decomposition-based sensitivity analysis (generalized Sobol indexes) is used for prioritization and fixing factors; then regional sensitivity analysis is used for factor mapping. The proposed approach has been tested on a simple case, with a nominal traffic running on a single-track line. The considered incident is the loss of a feeding power substation. The variables to be adjusted are the time interval between departure times and speed reduction in the vicinity of the faulty substation. The results show that increasing the time interval between trains is the most influential vari- able. Pareto-optimal fronts are also built in order to perform multi-criteria analysis according to travel- ling time, train delays and traction energy.