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1317. Timetable-based network capacity assessment
Invited abstract in session WD-54: Railway Capacity Management, stream Public Transport Optimization.
Wednesday, 14:30-16:00Room: S01 (building: 101)
Authors (first author is the speaker)
| 1. | Franziska Theurich
|
| Chair of Traffic Flow Science, TU Dresden | |
| 2. | Sara Comelli
|
| Professorship for Design and Operation of Public Rail Transport Systems (RTS), Technical University of Munich | |
| 3. | Cédric Kekes
|
| Chair of Traffic Flow Science, TU Dresden | |
| 4. | Karl Nachtigall
|
| Faculty of Transport and Traffic Sciences, Institut for Logistics and Aviation, Technical University of Dresden | |
| 5. | Norman Weik
|
| Professorship for Design and Operation of Public Rail Transport Systems (RTS), Technical University of Munich |
Abstract
In project “Network Capacity” we develop an approach allowing to compute the residual freight capacity for a given passenger timetable, considering freight traffic demand while maintaining punctuality and reliability in operations. Since timetable-based capacity assessment suffers from fluctuating freight traffic demand, we use pre-constructed slots on net segments which will be combined to train paths as the basis of our model.
The approach consists of two parts, timetabling and delay prognosis, which are combined to an integrated planning framework. In the timetabling part, we solve a Multi Commodity Flow Problem to predict the required number of slots on network segments. Subsequently, we construct (possible overlapping) slots for freight traffic in the passenger timetable using mathematical optimization techniques such as Branch-and-Price. These slots are the basis for routing freight traffic demand based on a MIP-based slot allocation. In detail, slots are selected and connected to build up conflict-free trains paths. In the delay prognosis part, a stochastic (max,+)-approach is used to assess the reliability of the timetable on the network level. It allows to calculate the delay distribution for each train and every infrastructure element and to assess timetable stability by suitable network performance metrics. With this approach, network capacity can be assessed by scaling the demand up to the maximum that can be reliably satisfied.
Keywords
- Capacity Planning
- Railway Applications
- Decision Support Systems
Status: accepted
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