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D3.1 – Final pre-study for an improved methodology for timetable planning including state-of-the-art and future work plan
Swedish Transport Administration (Trafikverket), Borlänge, Sweden.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering. (Trafiksystem, Traffic Systems)ORCID iD: 0000-0001-6880-8549
Ansaldo STS, Genova, Italy.
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering. (Kvantitativ logistik, Quantitative Logistics)
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2018 (English)Report (Other academic)Alternative title
D3.1 Improved timetable planning (English)
Abstract [en]

In ARCC project work package 3, research and innovation activities have been done to identify areas with a need for improved timetable planning methods and outline how new methods can be developed and implemented.

Improved timetable planning scope were described and there was an activity to connect to other relevant Shift2Rail projects. An workshop was organised in Stockholm 2018-05-29.

State of the art in practice was described for timetable planning in Sweden, UIC 406 method and Ansaldo STS Traffic management systems. Also state of the art in algorithms was described.

Future work plan research needs areas are:

1. Understanding of various goals for timetabling and how they co-variate

2. Residual capacity

3. Connection and coordination of the planning processes

4. Connection and coordination of the yard/terminal planning and network planning

5. Integration of freight trains into the timetable, focusing on short-term and ad-hoc

6. Integration of maintenance scheduling and timetabling, at all planning stages

7. Improved decision support for handeling of deviations from timetable in operations

8. Features of planning tools, and implementation of automatized timetabling

Place, publisher, year, edition, pages
2018. , p. 66
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-160801OAI: oai:DiVA.org:liu-160801DiVA, id: diva2:1359213
Projects
Automated Rail Cargo Consortium: Rail freight automation research activities to boost levels of quality, efficiency and cost effectiveness in all areas of rail freight operations
Funder
EU, Horizon 2020, H2020 – 730813
Note

Peterson är "main editor".

Övriga författare är "Medarbetare/bidragsgivare".

Available from: 2019-10-08 Created: 2019-10-08 Last updated: 2019-10-08Bibliographically approved

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CiteExportLink to record
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