Capacity for Rail

KAJT Dagarna, Dala-Storsund 2015-05-07

Pavle Kecman - LiUAnders Peterson - LiUMartin Joborn – LiU, SICSMagnus Wahlborg - Trafikverket

Outline

• Capacity4Rail short introduction• Framework for modelling and simulation• Operational traffic control• Improving simulation models to support

operational traffic control

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Project at a glance

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

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

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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 RUsNo. of cargo trains

No. of passenger trains Need for train slots

Ad-hoc changesTrain cancellation

Operational changes

On-time performance

Driving

Railway networkJunctions

Stations

Capacity supply

Signalling systems

Planned Maintenance work

Train slotsRolling stock

Major traffic disturbancesCrew scheduling

Immediate maintenance work

Disruptions

Real time operations

Modelling railway capacity

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

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.)

Modelling framework

Modelling framework

Modelling framework

Modelling framework

Modelling framework

Research focus in WP3.2

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• 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

Planned vs. Actual capacity utilisation

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Strategic – operational

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CUMULATIVE DISTRIBUTION OF STOCHASTIC CAPACITY CONSUMPTION (SOURCE: JENSEN ET AL., 2015)

Tactical – operational

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

Tactical – operational

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

Operational traffic control

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Challeneges in operational traffic control

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• 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”

Research focus in WP3.2

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• 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

Research direction

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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

Current research

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• 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

Current research

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• 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

Current research

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• 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

Initial results

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Expected results and application

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• 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)

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Thank you for your attention