An Experimental Analysis of Hierarchical Rail Traffic and Train Control in a Stochastic Environment. (29th May 2022)

• Record Type: Journal Article
• Authors: Wang, Pengling; Jeiziner, Annik; Luan, Xiaojie; De Martinis, Valerio; Corman, Francesco
• Other Names: van der Hurk Evelien Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-05-29
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/8674538
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : 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 bidirectional communication between ATO and TMS is therefore beneficial for conflict-resolving and energy-saving.