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Modelling Railway Capacity: Improving Simulation Models for Operational Traffic Control

This project aims to develop a framework for modelling and simulation to evaluate the impact of innovations on railway capacity. The focus is on enhancing safety systems, operational traffic control, and train control innovations. The research also aims to include the operational planning level in capacity analysis by closing the loop between traffic control and simulation.

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Modelling Railway Capacity: Improving Simulation Models for Operational Traffic Control

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

  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. Frameworkanalysis • 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. Modellingframework

  10. Modellingframework

  11. Modellingframework

  12. Modellingframework

  13. Modellingframework

  14. Research focus in WP3.2 • 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. Actualcapacityutilisation

  16. Strategic – operational Cumulative distribution of stochastic capacity consumption (source: Jensen et al., 2015)

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

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

  19. Operationaltrafficcontrol

  20. Challeneges in operationaltrafficcontrol • 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 • 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 • Existing simulation and predictiontoolsarebased on fixed distributions callibratedoffline • Information received in real time is therefore not used to update the estimatesof process times and delays • Dynamic adaptive responsivetool is required in order to adequatlyrepresenttraffic for operationalcontrol

  23. Current research • 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 • 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 • 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

  27. Expectedresults and application • 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. Thank you for your attention

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