1. Capacity for Rail KAJT Dagarna, Dala-Storsund 2015-05-07 Pavle Kecman - LiU Anders Peterson - LiU Martin Joborn – LiU, SICS Magnus Wahlborg - Trafikverket

2. Outline Capacity4Rail short introduction Framework for modelling and simulation Operational traffic control Improving simulation models to support operational traffic control

3. 3 Project at a glance

4. Sub-project 3 at a glance WP3.1 Capability trade-offs WP3.2 Simulation and models to evaluate enhanced capacity WP3.3 Optimal strategies to manage major disturbances WP3.4 Ubiquitous data for railway operations

5. SP3 at a glance WP3.1 Capability trade-offs WP3.2 Simulation and models to evaluate enhanced capacity WP3.3 Optimal strategies to manage major disturbances WP3.4 Ubiquitous data for railway operations

6. 6 STRATEGIC LEVEL PLANNING TACTICAL LEVEL PLANNING OPERATIONAL LEVEL PLANNING Economic growth Urbanization Socio-economic forecasting Trip generation Trip distribution Modal split Capacity demand Economic cycle Operating RUs No. of cargo trains No. of passenger trains Need for train slots Ad-hoc changes Train cancellation Operational changes On-time performance Driving Railway network Junctions Stations Capacity supply Signalling systems Planned Maintenance work Train slots Rolling stock Major traffic disturbances Crew scheduling Immediate maintenance work Disruptions Real time operations Modelling railway capacity

7. WP3.2 at a glance Railway traffic models exist and can be used at every planning level Our task: Develop a framework for modelling (simulation) that can be used to evaluate the impact of an innovation (on any planning level) on railway capacity Results and models developed within ON- TIME project are taken as input

8. Framework analysis Modelling framework should support analysis of impact of: – Infrastructure improvements – Enhancements of safety and signalling systems – Modifications of the timetabling principples – Improvement of operational traffic control – Inovations in train control (DAS, ATO, etc.)

9. Modelling framework

14. Research focus in WP3.2 14 Each planning level is supported by corresponding models Link is strong between strategic and tactical levels – operational level is typically excluded from capacity analysis The impact of disturbances, disruptions and reactions of operational control are thus excluded

15. Planned vs. Actual capacity utilisation 15

16. Strategic – operational 16 C UMULATIVE DISTRIBUTION OF STOCHASTIC CAPACITY CONSUMPTION ( SOURCE : J ENSEN ET AL., 2015)

17. Tactical – operational 17 Effect of enhancement of the signaling system on capacity consumption (source: Goverde et al., 2013)

18. Tactical – operational 18 Effectiveness of ETCS L2 including real time traffic control (source: Goverde et al., 2013)

19. Operational traffic control 19

20. Challeneges in operational traffic control 20 Rescheduling models have been in focus due to their complexity Current challanges include integration with monitoring and prediction models Traffic control models require continuos communication with the simulation model that represents ”reality”

21. Research focus in WP3.2 21 Operational planning level can be included in capacity analysis by closing the loop between traffic control and simulation (ON TIME) Problem: Existing simulation models are not adapted for operational level

22. Research direction 22 1.Existing simulation and prediction tools are based on fixed distributions callibrated offline 2.Information received in real time is therefore not used to update the estimates of process times and delays 3.Dynamic adaptive responsive tool is required in order to adequatly represent traffic for operational control

23. Current research 23 Availabiliy of historical traffic data motivated the developement of a data-driven model. Challenge is to analyse how real-time information can be used to reduce uncertainty of the coming events

24. Current research 24 A stochastic Bayesian model captures dependencies between events from historical data When an information about an event becomes available, distributions of all dependent events are updated

25. Current research 25 A stochastic Bayesian model captures dependencies between events from historical data When an information about an event becomes available, distributions of all dependent events are updated

26. Initial results 26

27. Expected results and application 27 Up-to-date estimates of probability distributions (separate and joint) for all considered events This enables accurate estimation of probability of delays – for proactive traffic and transport control Contribution for C4R– implementation of the concept of dynamics of uncertainty in railway simulation models Improved simulation models would enable closing the loop between oprational and tactical (strategic planning levels)

28. 28 Thank you for your attention