|Long Term Strategic Timetabling|
Subschema: Timetable and Rostering
Related subschemas: IS IL RS
|For general information on use cases see UC:Use cases|
- 1 Use case / Anwendungsfall / Scénario d’utilisation
- 2 Description / Beschreibung / Description
- 3 Data Flows and Interfaces / Datenflüsse und Schnittstellen / Flux de données et interfaces
- 4 Interference with other railML® schemas / Interferenz mit anderen railML®-Schemen / Interaction avec autres schemas railML®
- 5 Characterizing Data / Charakterisierung der Daten / Caractérisation des données
Use case / Anwendungsfall / Scénario d’utilisation
Long Term Strategic Timetabling; Langfristfahrplanerstellung
Description / Beschreibung / Description
The timetable data prepared in this use case is not formally linked to the production process of the LTP yearly timetable. It is typically produced more than two years in advance of operation (VLTP), and may be used for purposes including: infrastructure design and evaluation, socio-economic analyses and service planning (where it forms the basis of the LTP yearly timetable).
The data from this use case does not need to form a full production timetable, but contain sufficient information about trains to identify them, route them through the infrastructure and state their validity.
The infrastructure used in this use case is not based on the current infrastructure but an agreed future infrastructure state.
It is important that any structure (such as repeating train patterns or direct connections between trains) in the timetable is maintained in the data.
Data Flows and Interfaces / Datenflüsse und Schnittstellen / Flux de données et interfaces
Typically, the data produced in this use case is transferred to LTP systems using a file based interface or to another VLTP system for special studies.
- VLTP system-> VLTP system
- VLTP system-> LTP production system
- VLTP system-> consumers of study information (i.e. authorities)
- VLTP system-> VLTP vehicle rostering
The frequency of this data flow is not defined by any process, but as required. Typically this is low frequency: once per study, several times per year, etc.
Interference with other railML® schemas / Interferenz mit anderen railML®-Schemen / Interaction avec autres schemas railML®
Characterizing Data / Charakterisierung der Daten / Caractérisation des données
How often do the data change (update)?
- Production by the user on demand (ad-hoc), infrequent.
How big are the data fragments to be exchanged (complexity)?
- Whole data set: provide the full timetable
- Selected trains: provide the timetable entries for a subset of trains, for further special analysis
Which views are represented by the data (focus)?
- Macroscopic infrastructure
- Interval trains / train groups
- Regular validity (i.e. daily or fixed weekday patterns)
- Operational and commercial trains (e.g. direct connections, train kilometres, connections, …)
- Rolling stock requirements (e.g. seat capacity, maximum speed, braking capability, …)
The data contains information about the validity and trains paths in the proposed timetable. While it is not a prerequisite, it is likely that the timetable contains repeating patterns of trains within a train family sharing common characteristics allowing the user to efficiently create and manage timetable data using the <trainGroup> element.
Validity data should be optional allowing the user to plan generic days which may later be enriched to include dated validities.
Commercial (published) and operational (scheduled) <times> may be relevant.
Additional information for filtering purposes
- train types <category>
- railway undertaking <railwayUndertaking>
- stop types <stopDescription>
- train status, related to processStatus in <train>
Terms and Expressions
- LTP = Long Term Planning: the yearly timetable(s)
- VLTP = Very Long Term Planning (>2 years)