Description
Conflict detection and resolution models are being developed to support railway traffic management in taking optimised rescheduling decisions in case of disturbances. Existing models mostly concern conventional fixed-block signalling systems, featuring trackside multi-aspect signals. In these fixed-blocksystems, 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 ERTMS/ETCS Level 2 and Level 3 Fixed Virtual Block, minimum train separation is based on absolute braking distances. Hence, conflict detection and resolution models require a different dependency on train speed for distance-to-go signalling than for conventional fixed-block signalling. In this paper, we propose enhancements for existing fixed-block conflict detection and resolution models to describe distance-to-go operations. The enhancements include the generalisation of the infrastructure discretisation, the introduction of speed profile options and the redefinition of train blocking times. We apply the enhancements to the state-of-the-art rescheduling model RECIFE-MILP, developed for fixed-block trackside signalling. We verify the enhanced model by comparing the solutions of the distance-to-go
model and the original model in terms of delay and rescheduling decisions in two control areas. The results indicate that the distance-to-go model can propose different rescheduling decisions than the fixed-block trackside model, exploiting distance-to-go operations for a better delay recovery.
| Period | 31 Oct 2023 |
|---|---|
| Event title | TRAIL PhD Congress 2023 |
| Event type | Conference |
| Degree of Recognition | National |