A Tutorial on Blocking Time Theory and Strategic Extension with Virtual Coupling Principles

Jiamin Zhang

doi:10.6919/ICJE.202408_10(8).0015

Authors

Jiamin Zhang

DOI:

https://doi.org/10.6919/ICJE.202408_10(8).0015

Keywords:

Blocking Time Theory; Virtual Coupling; Headway Policy; Conflict Detection and Resolution; Timetabling; Capacity Allocation.

Abstract

Blocking time can be employed to precisely represent the infrastructure usage. The well-known blocking time theory forms the foundations for capacity allocation, conflict detection, and timetabling. By focusing on the microscopic models for calculating the precise track blocking times, the stability and feasibility of railway schedules/timetables can be guaranteed from the operation point of view. In order to be used as a strategic guidance to scheduling train path with blocking time theory over the given infrastructure under virtual coupling, this study focuses on the blocking time and its elementary components, the formal basic definition of virtual coupling, the headway policy, and how to include the extended blocking time theory in conflict detection and resolution during the coupling process. The motivation for extending the blocking time theory with virtual coupling principles is to adapt all methods based on blocking time theory/model that can be pote ntially applied also for virtual coupling.

Downloads

Download data is not yet available.

References

UIC (2004) Code 406: capacity, 1st edn. International Union of Railways, Paris

UIC (2013) Code 406: capacity, 2nd edn. International Union of Railways, Paris.

Pachl, J.( 2018). Railway operation and control, 4th Edition, VTD Rail Publishing, Mountlake Terrace.

Quaglietta, E., Pellegrini, P., Goverde, R.M.P., Albrecht, T., Jaekel, B., Marlière, G., Rodriguez, J., Dollevoet, T., Ambrogio, B., Carcasole, D., Giaroli, M., Nicholson, G. (2016). The ON-TIME real-time railway traffic management framework: A proof-of-concept using a scalable standardised data communication architecture. Transportation Research Part C: Emerging Technologies, 63, 23-50.

Goverde, R.M.P., Bešinović, N., Binder, A., Cacchiani, V., Quaglietta, E., Roberti, R., Toth, P. (2016). A three-level framework for performance-based railway timetabling. Transportation Research Part C: Emerging Technologies, 67, 62-83.

Medeossi, G., Longo, G., & de Fabris, S. (2011). A method for using stochastic blocking times to improve timetable planning. Journal of Rail Transport Planning & Management, 1(1), 1-13.

Uyttendaele, J., Van Hoeck, I., Besinovic, N., & Vansteenwegen, P. (2023). Timetable compression using max-plus automata applied to large railway networks. Top, 31(2), 414-439.

Van Thielen, S., Corman, F., & Vansteenwegen, P. (2019). Towards a conflict prevention strategy applicable for real-time railway traffic management. Journal of Rail Transport Planning & Management, 11, 100139.

Quaglietta, E. (2019). Analysis of platooning train operations under v2v communication-based signalling: fundamental modelling and capacity impacts of virtual coupling. In Proceedings of the 98th Transportation Research Board Annual Meeting: Washington DC, 13th-17th January 2019. Transportation Research Board (TRB).

Knutsen, D., Olsson, N. O., & Fu, J. (2024). Capacity evaluation of ERTMS/ETCS hybrid level 3 using simulation methods. Journal of Rail Transport Planning & Management, 30, 100444.

Wang, R., Nie, L., & Tan, Y. (2020). Evaluating line capacity with an analytical UIC code 406 compression method and blocking time stairway. Energies, 13(7), 1853.

Weik, N. (2022). Macroscopic traffic flow in railway systems-A discussion of the applicability of fundamental diagrams. Journal of Rail Transport Planning & Management, 23, 100330.

Aoun, J. (2023). Impact Assessment of Train-Centric Rail Signalling Technologies, PhD Dissertation, Technical University of Delft.

Shift2Rail (2020). Application Roadmap for the Introduction of Virtual Coupling; MOVINGRAIL, European: Maastricht, The Netherlands, pp. 1–74.

Hansen, I. A. & Pachl, J. (2014), Railway Timetabling and Operations, Eurailpress, Hamburg.

J. Pachl (2002), Railway Operation and Control. Mountlake Terrace, USA: VTD Rail Publishing.

Pachl J (2014) Railway operation and control, 3rd edn. VTD Rail Publishing, Mountlake Terrace. ISBN: 978-0-9719915-6-9.

Hansen, I., Pachl, J., (2008). Railway Timetable & Trafﬁc, Eurail Press, Hamburg.

Lindner, T. (2011). Applicability of the analytical UIC Code 406 compression method for evaluating line and station capacity. Journal of Rail Transport Planning & Management, 1(1), 49-57.

Bešinović N, Goverde RMP (2018) Capacity Assessment in Railway Networks. In: Borndörfer R, Klug T, Lamorgese L, Mannino C, Reuther M, Schlechte T (Eds) Handbook of Optimization in the railway Industry. International Series in Operations Research & Management Science, vol. 26. Springer, Cham, pp 25–45 https:// doi. org/ 10. 1007/ 978-3- 319- 72153-8_2.

Versluis, N. D., Quaglietta, E., Goverde, R. M., Pellegrini, P., & Rodriguez, J. (2024). Real-time railway traffic management under moving-block signalling: A literature review and research agenda. Transportation research part C: emerging technologies, 158, 104438.

Theeg G., Vlasenko S.( 2009), Railway Signalling and Interlocking International Compendium, Eurail Press.

Büker, T., Graffagnino, T., Hennig, E., Kuckelberg, A., (2019). Enhancement of blocking-time theory to represent future interlocking architectures. In: RailNorrköping 2019, 8th International Conference on Railway Operations Modelling and Analysis, No. 069. ICROMA, Linköping University Electronic Press, pp. 219–240.

Schlechte, T., Borndörfer, R., Denißen, J., Heller, S., Klug, T., Küpper, M., ... & Steadman, W. (2022). Timetable optimization for a moving block system. Journal of Rail Transport Planning & Management, 22, 100315.

Gao, H., Zhang, Y., Guo, J., (2020). Calculation and optimization of minimum headway in moving block system. In: 2020 IEEE 5th International Conference on Intelligent Transportation Engineering. ICITE, IEEE, pp. 482–486.

Choobchian, P., Roscoe, G., Dick, T., Zou, B., Work, D., Zhang, K., ... & Hung, Y. C. (2023). Leveraging connected vehicle platooning technology to improve the efficiency and effectiveness of train fleeting under moving blocks. Transportation Research Part C: Emerging Technologies, 148, 104026.

Su, S., She, J., Wang, D., Gong, S., & Zhou, Y. (2023). A stabilized virtual coupling scheme for a train set with heterogeneous braking dynamics capability. Transportation Research Part C: Emerging Technologies, 146, 103947.

Zhang, J. (2023). Simulation-based schedule optimization for virtual coupling-enabled rail transit services with multiagent technique. Journal of Advanced Transportation, 2023.

Ketphat, N., & Pathomsiri, S. (2024). The Conditions and Suitable Coupling Speed to Create a Train Convoy: A Train Movement under the Virtual Coupling Control. Engineering Journal, 28(3), 1-13.

Bešinović, N., Goverde, R. M., & Quaglietta, E. (2017). Microscopic models and network transformations for automated railway traffic planning. Computer‐Aided Civil and Infrastructure Engineering, 32(2), 89-106.

D’Ariano, A., Pacciarelli, D., Pranzo, M., (2007). A branch and bound algorithm for scheduling trains in a railway network. European J. Oper. Res. 183 (2),643–657.

Janssens, M.L., (2022). Multi Machine Approaches for Conflict Resolution Under Moving Block Signalling, master’s thesis. Delft University of Technology, The Netherlands.

Corman, F., Meng, L. (2015). A review of online dynamic models and algorithms for railway traffic management. IEEE Trans. Intell. Transp. Syst. 16 (3), 1274–1284.

Weik, N., Warg, J., Johansson, I., Bohlin, M., & Nießen, N. (2020). Extending UIC 406-based capacity analysis–New approaches for railway nodes and network effects. Journal of Rail Transport Planning & Management, 15, 100199.

Ketphat, N., Whiteing, A., & Liu, R. (2021). Train movement under the virtual coupling system. In Urban Rail Transit: Proceedings of the 6th Thailand Rail Academic Symposium (pp. 257-276). Springer Singapore.