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https://doi.org/10.21256/zhaw-27277| Publikationstyp: | Beitrag in wissenschaftlicher Zeitschrift |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | An experimental analysis of hierarchical rail traffic and train control in a stochastic environment |
| Autor/-in: | Wang, Pengling Jeiziner, Annik Luan, Xiaojie De Martinis, Valerio Corman, Francesco |
| et. al: | No |
| DOI: | 10.1155/2022/8674538 10.21256/zhaw-27277 |
| Erschienen in: | Journal of Advanced Transportation |
| Band(Heft): | 2022 |
| Heft: | 8674538 |
| Erscheinungsdatum: | 2022 |
| Verlag / Hrsg. Institution: | Hindawi |
| ISSN: | 2042-3195 |
| Sprache: | Englisch |
| Schlagwörter: | Railway operation; Automatic train operation; Traffic management system |
| Fachgebiet (DDC): | 380: Verkehr |
| Zusammenfassung: | The hierarchical connection of Rail Traffic Management System (TMS) and Automatic Train Operation (ATO) for mainline railways has been proposed for a while; however, few have investigated this hierarchical connection with the real field. This paper studies in detail the benefits and limitations of an integrated framework of TMS and ATO in stochastic and dynamic conditions in terms of punctuality, energy efficiency, and conflict-resolving. A simulation is built by interfacing a rescheduling tool and a stand-alone ATO tool with the realistic traffic simulation environment OpenTrack. The investigation refers to different disturbed traffic scenarios obtained by sampling train entrance delays and dwell times within a typical Monte Carlo scheme. Results obtained for the Dutch railway corridor Utrecht–Den Bosch prove the value of the approach. In case of no disruptions, the implementation of ATO systems is beneficial for maintaining timetables and saving energy costs. In case of delay disruptions, the TMS rescheduling has its full effect only if trains are able to follow TMS rescheduled timetables, while the energy-saving by using ATO can only be achieved with conflict-free schedules. A bi-directional communication between ATO and TMS is therefore beneficial for conflict-resolving and energy saving. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/27277 |
| Volltext Version: | Publizierte Version |
| Lizenz (gemäss Verlagsvertrag): | CC BY 4.0: Namensnennung 4.0 International |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Datenanalyse und Prozessdesign (IDP) |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_Wang-etal_Hierarchical-rail-traffic-train-control-analysis.pdf | 1.71 MB | Adobe PDF |  Öffnen/Anzeigen |
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Wang, P., Jeiziner, A., Luan, X., De Martinis, V., & Corman, F. (2022). An experimental analysis of hierarchical rail traffic and train control in a stochastic environment. Journal of Advanced Transportation, 2022(8674538). https://doi.org/10.1155/2022/8674538
Wang, P. et al. (2022) ‘An experimental analysis of hierarchical rail traffic and train control in a stochastic environment’, Journal of Advanced Transportation, 2022(8674538). Available at: https://doi.org/10.1155/2022/8674538.
P. Wang, A. Jeiziner, X. Luan, V. De Martinis, and F. Corman, “An experimental analysis of hierarchical rail traffic and train control in a stochastic environment,” Journal of Advanced Transportation, vol. 2022, no. 8674538, 2022, doi: 10.1155/2022/8674538.
WANG, Pengling, Annik JEIZINER, Xiaojie LUAN, Valerio DE MARTINIS und Francesco CORMAN, 2022. An experimental analysis of hierarchical rail traffic and train control in a stochastic environment. Journal of Advanced Transportation. 2022. Bd. 2022, Nr. 8674538. DOI 10.1155/2022/8674538
Wang, Pengling, Annik Jeiziner, Xiaojie Luan, Valerio De Martinis, and Francesco Corman. 2022. “An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment.” Journal of Advanced Transportation 2022 (8674538). https://doi.org/10.1155/2022/8674538.
Wang, Pengling, et al. “An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment.” Journal of Advanced Transportation, vol. 2022, no. 8674538, 2022, https://doi.org/10.1155/2022/8674538.
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