Combining the benefits of Traffic Management System and Automatic Train Operation

1. Combining the benefits of Traffic Management System and Automatic Train Operation Doctoral student: Xiaolu Rao systransis Ltd., Switzerland ETH Zurich, Switzerland x.rao@systransis.ch 18.01.2013  2013systransis

2. Outline • Introduction • Background of TMS and ATO • Integration of TMS and ATO • Conclusion and future work systransis Ltd., ETH Zürich, Switzerland

3. Outline • Introduction • Background of TMS and ATO • Integration of TMS and ATO • Conclusion and future work systransis Ltd., ETH Zürich, Switzerland

4. Introduction State of the art Methodology used ConclusionandFuturework Introduction Train Behavior Optimization Network Management Optimization Accurate Operation Efficient Rescheduling Automatic Train Operation Train Management System systransis Ltd., ETH Zürich, Switzerland

5. Outline • Introduction • Background of TMS and ATO • Integration method and Optimized ATO • Conclusion and future work systransis Ltd., ETH, Switzerland

6. Introduction State of the art Methodology used ConclusionandFuturework Train Management System (TMS) • TMS is a single centralized platform to maximize infrastructure operational efficiency. systransis Ltd., ETH Zürich, Switzerland

7. Introduction State of the art Methodology used ConclusionandFuturework TMS Framework • Single closed control loop in conventional TMS • Single closed control loops in advanced rescheduling. • CDR: Conflict Detection and Resolution • SPG: Speed Profile Generator systransis Ltd., ETH Zürich , Switzerland

8. Introduction State of the art Methodology used ConclusionandFuturework TMS Framework (Continued) • Two closed control loops in advanced rescheduling systransis Ltd., ETH Zürich , Switzerland

9. Introduction State of the art Methodology used ConclusionandFuturework TMS Example in Lötschberg Base Tunnel systransis Ltd., ETH, Switzerland

10. Introduction State of the art Methodology used ConclusionandFuturework Automatic Train Operation (ATO) • ATO is a complement to ATP. • ATO is a train level optimization. systransis Ltd., ETH Zürich , Switzerland

11. Introduction State of the art Methodology used ConclusionandFuturework ATO Approaches • Conventional ATO • Optimized ATO systransis Ltd., ETH Zürich, Switzerland

12. Introduction State of the art Methodology used ConclusionandFuturework Lessons Learnt systransis Ltd., ETH Zürich, Switzerland

13. Content • Introduction • Background of TMS and ATO • Integration of TMS and ATO • Conclusion and future work systransis Ltd., ETH, Switzerland

14. Introduction State of the art Methodology used ConclusionandFuturework Integration of TMS and ATO • TMS: Decision Support System for Dispatchers ? • ATO: Decision Support System for Drivers systransis Ltd., ETH Zürich, Switzerland

15. Introduction State of the art Methodology used ConclusionandFuturework Integration of TMS and ATO • Combination of optimization efforts from both the infrastructure side and the train side. systransis Ltd., ETH Zürich, Switzerland

16. Introduction State of the art Methodology used ConclusionandFuturework Step 1:Conflict Detection and Resolution systransis Ltd., ETH Zürich, Switzerland

17. Introduction State of the art Methodology used ConclusionandFuturework Step 2:Speed Profile Generator systransis Ltd., ETH Zürich, Switzerland

18. Introduction State of the art Methodology used ConclusionandFuturework Step 3:Automatic Train Operation systransis Ltd., ETH Zürich, Switzerland

19. Potential Benefits • Potential Increase of Capacity. • Potential Increase of Energy reservation. • Potential Increase of Operation Quality. systransis Ltd., ETH Zürich, Switzerland

20. Introduction State of the art Methodology used ConclusionandFuturework Potential capacity benefits by TMS systransis Ltd., ETH Zürich, Switzerland

21. Introduction State of the art Methodology used ConclusionandFuturework Potential capacity benefits by ATO systransis Ltd., ETH Zürich, Switzerland

22. Introduction State of the art Methodology used ConclusionandFuturework Potential Increase of Capacity by TMS +ATO • Potential increase of capacity in four aspects simultaneously based on the “balance of capacity” model. systransis Ltd., ETH Zürich, Switzerland

23. Outline • Introduction • Background of TMS and ATO • Integration method and Optimized ATO • Conclusion and future work systransis Ltd., ETH Zürich, Switzerland

24. Introduction IntegrationofTMSandATO OptimizedATO ConclusionandFuturework Conclusion • TMS + ATO. • DSS for Dispatchers + DSS for Drivers. • Infrastructure optimization + train optimization. • = Holistic optimization by combining the perspective of the infrastructure and the train. systransis Ltd., ETH, Switzerland

25. Introduction IntegrationofTMSandATO OptimizedATO ConclusionandFuturework Future work • The algorithm study and demonstrator design are carried out parallel to each other. • A demonstrator will be developed to support the integration idea. • More conflict scenario analysis will be carried out. • The innovative capacity concept requires more effort to clarify the details. • The potential capacity benefit of Lötschberg Base Tunnel will be analysed based on this new capacity model. systransis Ltd., ETH Zürich, Switzerland

26. Thanks systransis Ltd., ETH, Switzerland

27. Backups Slides systransis Ltd., ETH, Switzerland

28. Introduction State of the art Methodology used ConclusionandFuturework Rescheduling • “Conflict-based” approach: systransis Ltd., ETH Zürich, Switzerland

29. Introduction State of the art Methodology used ConclusionandFuturework Rescheduling (Continued) • “Conflict-based” approach: systransis Ltd., ETH Zürich, Switzerland

30. Introduction State of the art Methodology used ConclusionandFuturework Optimized ATO- Predictive Fuzzy Control Summarize Driver Experience Represent into Computer Language Build models for each optimization goals Question: Only two evaluation factors? Apply fuzzy relations with different goals Find out the best control command systransis Ltd., ETH, Switzerland

31. Introduction State of the art Methodology used ConclusionandFuturework Predictive fuzzy control& Analytical Hierarchy Process Decide the best control command Criterion 1 Criterion 2 Criterion n Predictive Fuzzy Control Optimization Goal 1 Optimization Goal 2 Optimization Goal n Command 1 Command 2 Command n systransis Ltd., ETH, Switzerland

32. Introduction State of the art Methodology used ConclusionandFuturework Lessons Learnt (Continued) • Why TMS and ATO are used in different railway lines? systransis Ltd., ETH Zürich, Switzerland