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Effects of Rolling Stock Unavailability on the Implementation of Energy-Saving Policies: A Metro System Application

  • Marilisa BotteEmail author
  • Luca D’Acierno
  • Mariano Gallo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11620)

Abstract

The recent world policies have shown the necessity of implementing suitable strategies, especially in urban contexts, in order to promote more sustainable transportation systems. In this context, the rail-based systems allow to achieve sustainable goals according to a threefold effect: reduction in externalities (such as congestion, accidents, air and noise pollution), increase in efficiency (in terms of operational cost per real/potential carried passenger), and delocalization of energy production centres (large industrial plants out of population centres producing with optimal yields). Positive environmental aspects of the rail and metro systems may be further amplified by implementing Energy-Saving Strategies (ESSs) based on the adoption of suitable driving profiles and/or the installation of onboard/wayside recovery devices. In this context, we investigate the effects of rolling-stock unavailability (for breakdowns, maintenance or under-sized fleet) on the effectiveness of ESSs within a multi-objective framework which combines the reduction in energy consumption with a passenger-oriented perspective. A real metro line in the south of Italy has been analysed as case-study in order to show the feasibility of the proposed approach.

Keywords

Rail-based public transport Energy-Saving Strategies Passenger-oriented approach 

References

  1. 1.
    Caprara, A., Kroon, L., Monaci, M., Peeters, M., Toth, P.: Passenger railway optimization. Handbooks Oper. Res. Manag. Sci. 14, 129–187 (2007).  https://doi.org/10.1016/S0927-0507(06)14003-7CrossRefGoogle Scholar
  2. 2.
    D’Acierno, L., Botte, M., Montella, B.: Assumptions and simulation of passenger behaviour on rail platforms. Int. J. Transp. Dev. Integr. 2(2), 123–135 (2018).  https://doi.org/10.2495/TDI-V2-N2-123-135CrossRefGoogle Scholar
  3. 3.
    D’Acierno, L., Botte, M., Placido, A., Caropreso, C., Montella, B.: Methodologyfor determining dwell times consistent with passenger flows in the case ofmetro services. Urban Rail Transit 3(2), 73–89 (2017).  https://doi.org/10.1007/s40864-017-0062-4CrossRefGoogle Scholar
  4. 4.
    Botte, M., D’Acierno, L.: Dispatching and rescheduling tasks and theirinteractions with travel demand and the energy domain: models and algorithms. Urban Rail Transit 4(4), 163–197 (2018).  https://doi.org/10.1007/s40864-018-0090-8CrossRefGoogle Scholar
  5. 5.
    D’Acierno, L., Botte, M.: An analytical approach for determining reserve timeson metro systems. In: Proceedings of the 17th IEEE International Conferenceon Environment and Electrical Engineering (IEEE EEEIC 2017) and 1ndIndustrial and Commercial Power Systems Europe (I&CPS 2017). Milan, Italy (2017). https://doi.org/10.0.4.85/EEEIC.2017.7977519
  6. 6.
    D’Acierno, L., Botte, M., Gallo, M., Montella, B.: Defining reserve times for metro systems: an analytical approach. J. Adv. Transp. 2018, 1–15 (2018).  https://doi.org/10.1155/2018/5983250CrossRefGoogle Scholar
  7. 7.
    D’Acierno, L., Botte, M.: Passengers’ satisfaction in the case of energy-saving strategies: a rail system application. In: Proceedings of the 18th IEEE International Conference on Environment and Electrical Engineering (IEEE EEEIC 2018) and 2nd Industrial and Commercial Power Systems Europe (I&CPS 2018). Palermo, Italy (2018).  https://doi.org/10.1109/EEEIC.2018.8494575
  8. 8.
    D’Acierno, L., Botte, M.: A passenger-oriented optimization model for implementing energy-saving strategies in railway contexts. Energies 11(11), 1–25 (2018).  https://doi.org/10.3390/en11112946CrossRefGoogle Scholar
  9. 9.
    Gonzalez-Gil, A., Palacin, R., Batty, P.: Sustainable urban rail systems: strategies and technologies for optimal management of regenerative braking energy. Energy Convers. Manage. 75, 374–388 (2013).  https://doi.org/10.1016/j.enconman.2013.06.039CrossRefGoogle Scholar
  10. 10.
    Ghavihaa, N., Campilloa, J., Bohlinb, M., Dahlquista, E.: Review of application of energy storage devices in railway transportation. Energy Procedia 105, 4561–4568 (2017).  https://doi.org/10.1016/j.egypro.2017.03.980CrossRefGoogle Scholar
  11. 11.
    Song, R., Yuan, T., Yang, J., He, H.: Simulation of braking energy recovery for the metro vehicles based on the traction experiment system. Simulation 93, 1099–1112 (2017).  https://doi.org/10.1177/0037549717726146CrossRefGoogle Scholar
  12. 12.
    Yan, X., Cai, B., Ning, B., ShangGuan, W.: Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements. Transp. Res. Part C 69, 60–78 (2016).  https://doi.org/10.1016/j.trc.2016.05.019CrossRefGoogle Scholar
  13. 13.
    Huang, Y., Ma, X., Su, S., Tang, T.: Optimization of train operation in multiple interstations with multi-population genetic algorithm. Energies 8, 14311–14329 (2015).  https://doi.org/10.3390/en81212433CrossRefGoogle Scholar
  14. 14.
    De Martinis, V., Weidmann, U.: Definition of energy-efficient speed profiles within rail traffic by means of supply design models. Res. Transp. Econ. 54, 41–50 (2015).  https://doi.org/10.1016/j.retrec.2015.10.024CrossRefGoogle Scholar
  15. 15.
    Sicre, C., Cucala, A., Fernandez, A., Lukaszewicz, P.: Modeling and optimizing energy-efficient manual driving on high-speed lines. IEEJ Trans. Electr. Electron. Eng. 7, 633–640 (2012).  https://doi.org/10.1002/tee.21782CrossRefGoogle Scholar
  16. 16.
    Zhao, N., Roberts, C., Hillmansen, S., Tian, Z., Weston, P., Chen, L.: An integrated metro operation optimization to minimize energy consumption. Transp. Res. Part C 75, 168–182 (2017).  https://doi.org/10.1016/j.trc.2016.12.013CrossRefGoogle Scholar
  17. 17.
    De Martinis, V., Corman, F.: Data-driven perspectives for energy efficient operations in railway systems: current practices and future opportunities. Transp. Res. Part C 95, 679–697 (2018).  https://doi.org/10.1016/j.trc.2018.08.008CrossRefGoogle Scholar
  18. 18.
    Corman, F., Meng, L.: A review of online dynamic models and algorithms for railway traffic management. IEEE Trans. Intell. Transp. Syst. 16(3), 1274–1284 (2015).  https://doi.org/10.1109/TITS.2014.2358392CrossRefGoogle Scholar
  19. 19.
    Goverde, R.: Punctuality of Railway Operations and Timetable Stability Analysis. Delft University of Technology, Delft, The Netherlands (2005)Google Scholar
  20. 20.
    Caimi, G., Fuchsberger, M., Laumanns, M., Lüthi, M.: A model predictive control approach for discrete-time rescheduling in complex, central railway station areas. Comput. Oper. Res. 39(11), 2578–2593 (2012).  https://doi.org/10.1016/j.cor.2012.01.003CrossRefzbMATHGoogle Scholar
  21. 21.
    Mazzarello, M., Ottaviani, E.: A traffic management system for real-time traffic optimization in railways. Transp. Res. Part B 41(2), 246–274 (2007).  https://doi.org/10.1016/j.trb.2006.02.005CrossRefGoogle Scholar
  22. 22.
    Quaglietta, E., Corman, F., Goverde, R.: Stability analysis of railway dispatching plans in a stochastic and dynamic environment. J. Rail Transp. Plann. Manag. 3(4), 137–149 (2013).  https://doi.org/10.1016/j.jrtpm.2013.10.009CrossRefGoogle Scholar
  23. 23.
    Törnquist, J.: Railway traffic disturbance management-an experimental analysis of disturbance complexity, management objectives and limitations in planning horizon. Transp. Res. Part A 41(3), 249–266 (2007).  https://doi.org/10.1016/j.tra.2006.05.003CrossRefGoogle Scholar
  24. 24.
    Corman, F., D’Ariano, A., Pacciarelli, D., Pranzo, M.: Bi-objective conflict detection and resolution in railway traffic management. Transp. Res. Part C 20(1), 79–94 (2012).  https://doi.org/10.1016/j.trc.2010.09.009CrossRefGoogle Scholar
  25. 25.
    D’Ariano, A., Pacciarelli, D., Samà, M., Corman, F.: Microscopic delay management: Minimizing train delays and passenger travel times during real-time railway traffic control. In: Proceedings of the 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (IEEE MT-ITS 2017). Naples, Italy (2017).  https://doi.org/10.1109/MTITS.2017.8005686
  26. 26.
    Xu, P., Corman, F., Peng, Q., Luan, X.: A timetable rescheduling approach and transition phases for high speed railway traffic during disruptions. Transp. Res. Rec. 2607(1), 82–92 (2017).  https://doi.org/10.3141/2607-11CrossRefGoogle Scholar
  27. 27.
    Botte, M., D’Acierno, L., Montella, B., Placido, A.: A stochastic approach for assessing intervention strategies in the case of metro system failures. In: Proceedings of 2015 AEIT Annual Conference (AEIT 2015). Naples, Italy (2015).  https://doi.org/10.1109/AEIT.2015.7415258
  28. 28.
    D’Acierno, L., Placido, A., Botte, M., Gallo, M., Montella, B.: Defining robust recovery solutions for preserving service quality during rail/metro systems failure. Int. J. Supply Oper. Manag. 3(3), 1351–1372 (2016).  https://doi.org/10.22034/2016.3.01CrossRefGoogle Scholar
  29. 29.
    Larsen, R., Pranzo, M., D’Ariano, A., Corman, F., Pacciarelli, D.: Susceptibility of optimal train schedules to stochastic disturbances of process times. Flex. Serv. Manuf. J. 26, 466–489 (2014).  https://doi.org/10.1007/s10696-013-9172-9CrossRefGoogle Scholar
  30. 30.
    Davydov, B., Chebotarev, V., Kablukova, K.: Stochastic model for the real-time train rescheduling. Int. J. Transp. Dev. Integr. 1(3), 307–317 (2017).  https://doi.org/10.2495/TDI-V1-N3-307-317CrossRefGoogle Scholar
  31. 31.
    Li, X., Shou, B., Ralescu, D.: Train rescheduling with stochastic recovery time: a new track-backup approach. IEEE Trans. Syst. Man Cybern. Syst. 44(9), 1216–1233 (2014).  https://doi.org/10.1109/TSMC.2014.2301140CrossRefGoogle Scholar
  32. 32.
    Kecman, P., Corman, F., Meng, L.: Train delay evolution as a stochastic process. In: Proceedings of the 6th International Conference on Railway Operations Modelling and Analysis (RailTokyo 2015). Narashino, Japan (2015)Google Scholar
  33. 33.
    Kecman, P., Corman, F., Peterson, A., Joborn, M.: Stochastic prediction of train delays in real-time using bayesian networks. In: Proceedings of Conference on Advanced Systems in Public Transport (CASPT 2015). Rotterdam, The Netherlands (2015).  https://doi.org/10.3929/ethz-b-000175478
  34. 34.
    Meng, L., Zhou, X.: Robust single-track train dispatching model under a dynamic and stochastic environment: a scenario-based rolling horizon approach. Transp. Res. Part B 45(7), 1080–1102 (2011).  https://doi.org/10.1016/j.trb.2011.05.001CrossRefGoogle Scholar
  35. 35.
    Yin, J., Tang, T., Yang, L., Gao, Z., Ran, B.: Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: an approximate dynamic programming approach. Transp. Res. Part B 91, 178–210 (2016).  https://doi.org/10.1016/j.trb.2016.05.009CrossRefGoogle Scholar
  36. 36.
    Louwerse, I., Huisman, D.: Adjusting a railway timetable in case of partial or complete blockades. Eur. J. Oper. Res. 235, 583–593 (2014).  https://doi.org/10.1016/j.ejor.2013.12.020MathSciNetCrossRefzbMATHGoogle Scholar
  37. 37.
    Zhan, S., Kroon, L., Veelenturf, L., Wagenaar, J.: Real-time high-speed train rescheduling in case of a complete blockage. Transp. Res. Part B 78, 182–201 (2015).  https://doi.org/10.1016/j.trb.2015.04.001CrossRefGoogle Scholar
  38. 38.
    Durmus, M., Takai, S., Söylemez, M.: Fault diagnosis in fixed-block railway signaling systems: a discrete event systems approach. IEEJ Trans. Electr. Electron. Eng. 9(5), 523–531 (2014).  https://doi.org/10.1002/tee.22001CrossRefGoogle Scholar
  39. 39.
    D’Acierno, L., Placido, A., Botte, M., Gallo, M., Montella, B.: A methodological approach for managing rail disruptions with different perspectives. Int. J. Math. Models Meth. Appl. Sci. 10, 80–86 (2016). http://naun.org/cms.action?id=12152Google Scholar
  40. 40.
    Hao, W., Meng, L., Veelenturf, L., Long, S., Corman, F., Niu, X.: Optimal reassignment of passengers to trains following a broken train. In: Proceedings of the 2018 IEEE International Conference on Intelligent Rail Transport (IEEE ICIRT 2018). Marina Bay Sands, Singapore (2018).  https://doi.org/10.1109/ICIRT.2018.8641524
  41. 41.
    Botte, M., Puca, D., Montella, B., D’Acierno, L.: An innovative methodology for managing service disruptions on regional rail lines. In: Proceedings of the 10th International Conference Environmental Engineering (ICEE 2017). Vilnius, Lithuania (2017).  https://doi.org/10.3846/enviro.2017.134
  42. 42.
    Nash, A., Huerlimann, D.: Railroad simulation using opentrack. Comput. Railways 9, 45–54 (2004).  https://doi.org/10.2495/CR040051CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Civil, Architectural and Environmental EngineeringFederico II University of NaplesNaplesItaly
  2. 2.Department of EngineeringUniversity of SannioBeneventoItaly

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