- Titel
- Railway nodes in an integrated timetable-based network capacity assessment model
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-1052344
- Konferenz
- RailDresden 2025 – 11th International Conference on Railway Operations Modelling and Analysis. Dresden, 01.04.2025 - 04.04.2025
- Erstveröffentlichung
- 2025
- Abstract (EN)
- In this paper, we show an approach to integrate nodes into a timetable-based network capacity model, where the residual freight capacity is computed for a given passenger timetable. Since timetable-based capacity assessment suffers from fluctuating freight traffic demand, pre-constructed slots on net segments and nodes are used which will be combined to freight train paths for a certain demand. An evaluation, using a (max,+)-approach, of resulting slots and timetables ensures that they are robust against delays and provide an acceptable level of service. As capacity assessment relies on an accurate modelling of the infrastructure of railway nodes, a sufficient level of accuracy must be derived. In this paper, it is shown that publicly available open data sources contains the necessary information. Based on the provided layout of railway nodes with all tracks, switches and signals, further relevant information can be deduced, as turn restrictions or the number of siding tracks for freight trains. With it, we are able to construct feasible slots and train paths in and through railway nodes. This allows for a network-wide capacity assessment. Moreover, the approach allows for consideration and modelling of different signalling and train control equipment and is flexible to allow data enrichment from other sources. The integrated model is applied in a case study on a railway node and its adjacent lines in Northern Germany to evaluate its effects.
- Freie Schlagwörter (EN)
- railway networks, railway nodes, timetable-based capacity assessment, open data
- Klassifikation (DDC)
- 380
- Klassifikation (RVK)
- ZO 5000
- Herausgeber (Institution)
- Technische Universität Dresden, Dresden
- Technische Universität München, München
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
ID: 512633826 - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-1052344
- Veröffentlichungsdatum Qucosa
- 11.06.2026
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0