Conflict detection and resolution for moving-block signalling

N.D. Versluis, Paola Pellegrini, E. Quaglietta, R.M.P. Goverde, Joaquin Rodriguez

Research output: Contribution to journalArticleScientificpeer-review

Abstract

To support railway traffic management in taking optimised rescheduling decisions in case of disturbances, conflict detection and resolution models are being developed. The existing models mostly refer to conventional fixed-block signalling systems. In these fixed-block systems, minimum train separation distances are determined based on a preset number of blocks considering worst-case braking distances. In fixed-block distance-to-go signalling systems, such as the European Train Control System (ETCS), minimum train separation is based on absolute braking distances. Hence, a different dependency on train speed is required in conflict detection and resolution models for distance-to-go signalling than in models for conventional fixed-block signalling. In this paper, we propose enhancements for existing conflict detection and resolution models to describe distance-to-go operations. The enhancements include the introduction of speed profile options and the redefinition of train blocking times. As a proof of concept, we apply these enhancements to the state-of-the-art rescheduling model RECIFE-MILP, originally developed for conventional fixed-block signalling. The enhanced model is verified and compared with the original model in terms of delay and rescheduling decisions in two case studies of a complex junction and a corridor. The results indicate that the distance-to-go model can propose different rescheduling decisions than the conventional fixed-block one, exploiting distance-to-go operations for a better delay recovery.
Original languageEnglish
JournalTransportation Research. Part C: Emerging Technologies
Publication statusSubmitted - 2024

Keywords

  • Real-time railway traffic management
  • Conflict detection and resolution
  • Train rescheduling
  • ETCS Level 2
  • Distance-to-go signalling
  • Mixed integer linear programming

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