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Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study

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  • Soha Saad

    (Laboratoire de Génie Electrique et Electronique de Paris, Centrale Supélec, CNRS, Université Paris-Saclay, 91192 Gif-sur-Yvette, France
    CNRS, LS2N, 44200 Nantes, France
    SNCF Réseau, Direction de L’Ingénierie, 6 Avenue Francois Mitterand, 95574 La Plaine St-Denis, France)

  • Florence Ossart

    (Laboratoire de Génie Electrique et Electronique de Paris, Centrale Supélec, CNRS, Université Paris-Saclay, 91192 Gif-sur-Yvette, France)

  • Jean Bigeon

    (CNRS, LS2N, 44200 Nantes, France)

  • Etienne Sourdille

    (SNCF Réseau, Direction de L’Ingénierie, 6 Avenue Francois Mitterand, 95574 La Plaine St-Denis, France)

  • Harold Gance

    (SNCF Réseau, Direction de L’Ingénierie, 6 Avenue Francois Mitterand, 95574 La Plaine St-Denis, France)

Abstract

The adjustment of rail traffic in the event of an electrical infrastructure disruption presents an important decision-making process for the smooth operation of the network. Railway systems are complex, and their analysis relies on expensive simulations, which makes incident management difficult. This paper proposes the use of sensitivity analysis in order to evaluate the influence of different traffic adjustment actions (e.g., spacing between trains and speed reduction) on the train supply voltage, which must never drop below the critical value prescribed by technical standards. Three global sensitivity analysis methods dedicated to black box, multivariate, nonlinear models are considered: generalized Sobol indices, energy distance-based indices, and regional sensitivity analysis. The three methods are applied to a simple traffic rescheduling test case and give similar results, but at different costs. Regional sensitivity analysis appears to be the most suitable method for the present application: it is easy to implement, rather fast, and accounts for constraints on the system output (a key feature for electrical incident management). The application of this method to a test case representative of a real rescheduling problem shows that it provides the information needed by the traffic manager to reschedule traffic in an efficient way. The same type of approach can be used for any power system optimization problem with the same characteristics.

Suggested Citation

  • Soha Saad & Florence Ossart & Jean Bigeon & Etienne Sourdille & Harold Gance, 2021. "Global Sensitivity Analysis Applied to Train Traffic Rescheduling: A Comparative Study," Energies, MDPI, vol. 14(19), pages 1-29, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6420-:d:651653
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    References listed on IDEAS

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    1. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    2. Garcia-Cabrejo, Oscar & Valocchi, Albert, 2014. "Global Sensitivity Analysis for multivariate output using Polynomial Chaos Expansion," Reliability Engineering and System Safety, Elsevier, vol. 126(C), pages 25-36.
    3. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
    4. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    5. Campbell, Katherine & McKay, Michael D. & Williams, Brian J., 2006. "Sensitivity analysis when model outputs are functions," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1468-1472.
    6. Tarantola, S. & Kopustinskas, V. & Bolado-Lavin, R. & Kaliatka, A. & Ušpuras, E. & Vaišnoras, M., 2012. "Sensitivity analysis using contribution to sample variance plot: Application to a water hammer model," Reliability Engineering and System Safety, Elsevier, vol. 99(C), pages 62-73.
    7. Cacchiani, Valentina & Toth, Paolo, 2012. "Nominal and robust train timetabling problems," European Journal of Operational Research, Elsevier, vol. 219(3), pages 727-737.
    8. Li, Luyi & Lu, Zhenzhou & Wu, Danqing, 2016. "A new kind of sensitivity index for multivariate output," Reliability Engineering and System Safety, Elsevier, vol. 147(C), pages 123-131.
    9. Bolado-Lavin, R. & Castaings, W. & Tarantola, S., 2009. "Contribution to the sample mean plot for graphical and numerical sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1041-1049.
    10. Zhan, Shuguang & Kroon, Leo G. & Veelenturf, Lucas P. & Wagenaar, Joris C., 2015. "Real-time high-speed train rescheduling in case of a complete blockage," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 182-201.
    11. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    12. Michael Schachtebeck & Anita Schöbel, 2010. "To Wait or Not to Wait---And Who Goes First? Delay Management with Priority Decisions," Transportation Science, INFORMS, vol. 44(3), pages 307-321, August.
    13. Lamboni, Matieyendou & Monod, Hervé & Makowski, David, 2011. "Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 450-459.
    14. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    15. Szekely, Gábor J. & Rizzo, Maria L., 2005. "A new test for multivariate normality," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 58-80, March.
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    1. Chuan Qin & Yuqing Jin & Meng Tian & Ping Ju & Shun Zhou, 2023. "Comparative Study of Global Sensitivity Analysis and Local Sensitivity Analysis in Power System Parameter Identification," Energies, MDPI, vol. 16(16), pages 1-21, August.

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