Rail Rome 2011 Final

8/2/2019 Rail Rome 2011 Final

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A Model-Predictive Control Framework for

Railway Traffic Management

Pavle Kecman1, Nicolas Weiss

2, Rob M.P. Goverde

1, Ton J.J. van den

Boom2

1Delft University of Technology, Department of Transport and Planning, the Netherlands2Delft University of Technology, Delft Centre for Systems and Control, the Netherlands

{p.kecman, n.m.weiss, r.m.p.goverde, a.j.j.vandenboom}@tudelft.nl

Abstract

This paper proposes a model-predictive control framework for anticipative management

of railway traffic. The framework aims at closing the loop between timetabling and train

operations with a continuous feedback of train positions and field data to allow fastrescheduling of train paths in case of disturbances. The control algorithms use a priori

knowledge of the timetable structure and online observations of train positions and delays.

The predictive property of the railway traffic model enables a controller to anticipate on

the propagation of current delays and to estimate the effect of proposed dispatching

strategies. Based on feedback of the actual state of the railway system, the actual timetable

is monitored on a network scale and optimal adjustments are proposed to the traffic

controllers when the timetable no longer suffices or when logistic constraints (rolling

stock and train crews) are jeopardized.

Keywords

Dynamic railway traffic management, model-predictive control, railway operations, traffic

prediction

1 Introduction

Railway traffic is considered to be very inadaptable when subjected to disturbances

originating from external factors (weather, number of passengers and their behaviour, etc.)

as well as from internal entities from within a railway system (reliability of infrastructure

and vehicle equipment, behaviour of personnel, etc.). Disruptions in railway traffic are

considered to be inevitable and therefore, actions are made in order to minimize their

possible effect on the system both in the stage of timetable construction and in real time

during railway operation. In the process of timetabling it is crucial to have in mind the

importance of robustness and resilience of the timetable, i.e., its ability to resist and adapt

to minor disturbances. For that reason running time supplements and buffer times are

introduced in order to enable trains to make up for their delay and at least to some extent

avoid affecting other trains and creating secondary delays. However, both running timesupplements and buffer times are limited by capacity consumption constraints [15] and in

addition, neither of them is meant to compensate for major disruptions such as accidents,

infrastructure or vehicle equipment failures, etc. Therefore, good timetabling can only to

certain extent contribute to the punctuality of the railway traffic.



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Dynamic railway traffic management at all levels (from the level of an interlocking

area to the network wide level) is necessary to make further effort to maintain the

punctuality of railway operations in real time. The concept of dynamic traffic

management has so far been widely understood as a reactive set of actions with the

purpose of minimizing the consequences of previously noted delays. In this paper we

propose a framework for railway traffic management based on model-predictive control

(MPC). The essential characteristic of the proposed framework is that it suggests

proactive and anticipative (in contrast to reactive) traffic management aiming to predict

the occurrence of potential conflicts and prevent them by performing certain control

actions. Moreover, the proposed model-predictive approach enables choosing the most

effective dispatching action using its feedback loop to verify and estimate the set of

possible actions.

The proposed MPC approach assumes having continuous feedback of train positions

and field data which are used as an input to running time prediction models whose output

is a vector of delays of all trains over the rolling prediction horizon. The railway traffic

prediction model can also be used as an evaluation tool in order to determine the optimal

dispatching strategy from the set of possible strategies. In other words, the MPCframework for railway traffic management consists of 3 components:

1. Monitoring train positions, speeds, condition and availability of infrastructure and

based on this determining up-to-date running time estimates

2. A real-time railway traffic prediction model continuously updated with field data and

dispatching actions

3. A model-predictive controller that optimizes future control decision by using

predictions of the future state and the current condition of railway operations.

The next section describes the current practice in railway traffic control and gives

comparisons to the proposed framework. Section 3 and 4 contain formulations of MPC

and the proposed framework for railway traffic management, respectively. Subsequent

sections give detailed descriptions of each of the 3 components separately and embedded

in the proposed framework. In the final section, the main conclusions are presented.

2 Current Practice in Railway Traffic Control

2.1 The Concept Based on Hierarchical Levels

Current practice in railway traffic control on European networks is based on a multi level

hierarchy (Figure 1). The number of levels and the area under control at each level may

vary but essentially traffic control systems are split into a tactical and operational level

[21]. The tactical level (regional or network controllers) comprises the supervision of the

state of traffic on a network level, detection of deviations from the timetable, resolution of

conflicts affecting the overall network performance, handling failures and events that may

have big impact on performance indicators, etc. The operational level consists of local

traffic controllers (in major stations with a complex topology of interlocking areas) or

centres for remote control (for small stations with a simple topology and possible points of

conflict between major stations such as junctions, level crossings etc.) with the task to

perform all safety related actions, set routes for trains, predict and solve conflicts on alocal level and control processes that take place on the part of infrastructure under their

supervision.



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Figure 1: Hierarchical structure of traffic control

The lower the hierarchical level the smaller is the area under control and the more

detailed is the control of train movements with respect to the network topology and

possible route conflicts. On the other hand, the higher the control level the more

comprehensible is the actual state of traffic on the network level, i.e., future train

movements and thus also global conflicts (passenger or logistic connections) and delays

are easier to predict. The situational awareness of local controllers (signallers) is limited to

their own interlocking area: to handle trains coming their way and dispatch them to anadjacent interlocking area following standard rules and their own experience without

further knowledge of the network wide consequences of their decisions. This situation

requires close interaction of local and network controllers because none of them is able to

have the overall view of the traffic situation on a global level [39]: the network traffic

controller can not monitor and control the train movements and infrastructure on a

microscopic level (interlocking areas and track occupation in the stations) while the localcontroller does not have the information (apart from the neighbouring local controllers)

about the movements of trains heading towards his/her area.

2.2 Integrated Traffic Control

The main task of the integrated railway traffic control system with its interdependent

components is to monitor the state of traffic on the network, forecast the state in the near

future, identify and solve conflicts and if necessary reschedule events defined in the

timetable to minimize deviation from the original plan.

From the current railway practice [4, 11, 15, 21, 25, 31, 39] it can be seen that the

process of railway traffic control takes the form of a loop where the information data

flows from the infrastructure (occupation, availability) and the trains (position) upward

towards the operational and tactical levels of control, whilst the flow of commands has the

opposite direction. Based on the current state and the traffic situation on the network, the

expected running time for each train heading towards their interlocking area is nowestimated by signallers based on their experience (they typically take the expected arrival

delay equal to the current upstream delay at the preceding interlocking area as they have

no information about possible recovery times). Network traffic controllers (tactical level)



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are notified only after a train delay has become already significantly large and they then

have to reschedule trains reactively and try to prevent further accumulation of delays.

Control decisions on the level of network traffic controllers include reordering, rerouting,

revising services (cancelling transfers or trains, adding extra trains), rescheduling or using

spare resources, etc.). Those control actions need to be accommodated by local traffic

controllers who need to set train routes with regard to the microscopic network topology

and safety regulations.

Necessity for improvementsIn the forthcoming decade a further growth of both passenger and freight flows is

expected which will mostly have to be accommodated over the existing railway

infrastructure. This will lead to an increase of capacity utilization thus reducing reliability

and punctuality of railway services. Therefore, improvements in traffic management and

control have to be made in order to prevent a decrease of traffic reliability.

While the timetable is carefully planned a year in advance using sophisticated

mathematical models, the daily operational control of disruptions and delays still relies

predominantly on predetermined rules and the experience and skills of personnel withoutany significant support such as short-term traffic prognosis, conflict detection and

prediction or optimal dispatching. Working in a preventive manner is poorly supported

and train traffic controllers are usually restricted to just solving problems as they occur

[19]. Moreover, neither local nor network traffic controllers have a reliable supportingtool to predict the effect of their decisions and evaluate them.

Potential Impact of the MPC FrameworkThe MPC framework for railway traffic management [37] aims at providing the traffic

controllers with a decision support system based on algorithms that would be able to

collect and process data about the current state of traffic and infrastructure, predict and

detect possible conflicts, and propose the optimal way for their resolution, in real time. In

other words, the functional components of the MPC framework correspond to the process

of traffic control currently in practice on most railway networks. Moreover, each

component can separately be used as a support tool for railway traffic control. Both localand network traffic controllers would benefit from a tool for monitoring the current state

of traffic and infrastructure condition and making reliable short term traffic situation

estimates. Models for traffic prediction can be used to initiate and to evaluate the effect of

control actions on both levels (defining the new feasible timetable on the tactical level and

local conflict resolution and accommodation of the actual timetable on the operational

level). Finally, in the process of choosing the control actions to implement in case of

traffic disruptions, a model-predictive controller provides an adequate support for making

the most effective decision.

3 Model-Predictive Control

Model (based) predictive control represents a methodology with the purpose to optimize

forecasts of process behaviour over manipulable inputs [22]. It is a widely accepted and

used control scheme in the process industry. All MPC systems rely on the idea of generating values for process inputs as solutions of an on-line (real-time) optimization of

predicted values of process outputs. Figure 2 shows the general structure and explains the

components of the MPC approach.



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Process model

Optimizer

-

+

Real process

Reference

outputs

Predicted

outputs

Measurements

and disturbances

Objective

functionConstraints

Future

inputs

Figure 2: Basic structure of general MPC

On the basis of the internal process model, a prediction of system outputs over aspecified time horizon is made. The prediction can be either time driven or event driven,

i.e., triggered either at each predetermined time instant or by the occurrence of events. The

system outputs, predicted by the process model (based on the measurements from the

system) over a specified prediction horizon, are compared to the reference trajectory and

optimization is carried out with the purpose of computing control signals which would

direct the system towards the desired trajectory. The process model is again used to

estimate the effect of the proposed control signals. Thus the deviation from the reference

trajectory is minimized with respect to constraints which reflect the nature of the system,

safety or economic requirements, etc. After the optimal control sequence has been

computed, only the first control entry will be implemented and the complete cycle repeats

starting with the updated situation, the so called receding horizon principle [22].

Parameters that define the model predictive controller are [26]:• internal process model,

• disturbance prediction,

• objectives, reference trajectory and constraints,

• measurements,

• sampling period,

• prediction horizon.

4 Railway Traffic Management Based on MPC

4.1 General Framework Formulation

The use of the MPC approach for management of railway traffic relies on the fact that

MPC represents more of a methodology of controlling a variety of different processes

than a specific control technique [36]. Figure 2 shows the structure and relationshipsbetween the basic components of the MPC railway traffic management model embedded

in its environment. In order to fit into the integrated control framework presented in

section 2.2, the working timetable (Figure 3) can represent both the adjusted published

timetable on the network level, and the modified schedule of events on microscopic level



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depending on the level of control action.

Figure 3: Model predictive controller and its environment

Railway traffic management based on MPC represents an approach in which control

and optimization are integrated, coordinated and organized in a closed-loop form [21].

Moreover, from the systems and control theory point of view it corresponds to a cascade

control system with two loops on different hierarchical levels (Figure 4). Outer loop has

the purpose to control and optimize the behaviour of the system under major disturbances

(global level) and sets the target or the reference trajectory for the inner loop which

distributes control actions on the local level. In addition, the inner loop handles minor

disturbances which do not initiate activation of the outer loop. Note how this approach

resembles the relationship between local and network controllers described in section 2.

4.2 Cascade Loops Control Framework

Railway operations are monitored on the network level and accurate field data about the

train positions, speeds, infrastructure conditions and availability are used to create reliable

estimates of running times which are further processed by the predictive traffic model,

yielding the state of the system over the prediction horizon. All local or global conflicts

are detected by the predictive traffic model and based on the outcome of prediction, only

inner (local conflicts) or both outer and inner loops (disturbances with global effect such

as passenger or logistic connections) are activated. The model-predictive controller

creates control signals (on one or both levels) which direct the system towards the desired

trajectory and whose impact and effect is evaluated by the predictive model.

The inner loop has the task to optimize control signals which would accommodate

and minimize the deviation from the target trajectory. When the outer loop is not

activated, the target is the published timetable and otherwise it is the working (updated)

timetable that is the outcome of the outer loop (optimization on global level with

objectives depending on the type and scale of disturbances).



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Figure 4: MPC approach as a cascade control system

All three essential components of MPC framework for railway traffic management:

monitoring, the predictive traffic model and the model-predictive controller will be

analyzed in detail in the following sections.

5 Monitoring

5.1 Traffic Situation and Short-Term Prediction

One of the crucial requirements for traffic control and rescheduling systems is the early

and reliable detection of deviations and disturbances in railway operations. As presentedin section 2, traffic controllers use the information about the current state of traffic and

infrastructure, and their experience to make short-term estimates of the traffic situation.Depending on the traffic situation and infrastructure availability and condition, the

procedures leading to control actions on both local and global level can be initiated.

Precise information about the exact positions of trains, their speed and dynamics

together with reliable data about infrastructure occupation, up-to-date database of

temporary speed restrictions, blocked tracks and equipment failures are the essential

requirements for detection of deviations from the target trajectory, making reliable free

running time estimates with the purpose to predict the conflicting infrastructure claims

and project the traffic situation in the near future.

Detecting deviations from the target trajectory of a train before the actual occurrence

of conflicts with other trains is a crucial requirement which enables traffic controllers to

manage the traffic proactively and in an anticipative manner. In current practice

deviations (delays) are identified (existing delays are updated) only at stations withreference to scheduled arrivals, departures or passages. If the traffic controllers would

have accurate information about the running times and expected arrivals of trains heading

towards their area, many unscheduled stops or hindered train runs could be avoided by

resolving such conflicts in advance, by e.g. rerouting, changing the train order at the point



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of conflict or giving speed advice to drivers.

5.2 Traffic and Infrastructure Monitoring Systems

Current practice in monitoring of railway traffic on European networks relies on so-called

train describer systems. The railway infrastructure (both open tracks and station areas) is

divided by the track side devices (e.g. axle counters, track circuits, balises) to sections

whose length may vary. Train describers use those devices as detection points of train

transitions from one section to another. Each train has been assigned a unique number by

which it is recognized by the train describer system. The role of train describers in themonitoring of railway traffic is limited to determining the occupied infrastructure sections

and the positions of trains with precision that depends on the length of the sections. No

additional information necessary for deriving running time estimates can be transmitted

apart from the average train speeds which can be computed based on the successive time

instants of train steps over the network sections and the lengths of the sections [11, 21].

Another method for monitoring the railway traffic which would enable overcoming the

drawbacks of train describers is the application of periodic train positioning (onpredetermined time instants) based on satellite positioning systems (GPS, Galileo). A

comparison of both methods with respect to their applicability in traffic monitoring and

deviation detection is given by Lüthi [21]. Salmi and Torkkeli [28] give a state of the art

survey of various applications of GPS in the railway sector.

5.3 Short-term Traffic Prediction Models

To predict the train positions over a larger distance and time horizons two approaches can

be pursued: (i) microscopic running time calculations based on train dynamics and

detailed infrastructure characteristics [16] or (ii) statistical procedures based on filtering

historical data and ex-post data analysis to learn how previous trains moved in similar

conditions. The first approach gives accurate running time estimations and it has so far

been used in the process of timetable construction and insertion of additional trains in the

existing timetable rather than for on-line use.Historical data obtained by a train positioning system (train describer or satellite

based) can be used to determine the (time or position) intervals for punctual (conflict free)

train runs. Lüthi [21] used the tolerance band approach to detect the deviation in train

runs. Ex-post data analysis can be used to determine the bandwidth (time window for train

describers or position window for periodic positioning). Van der Meer et al. [38]

presented a running time prediction model in which they used historical data to determine

running time dependency on delays, time of the day, rolling stock characteristics, and the

weather. In future research, the model could be extended and refined by including train

describers measured signals or periodic positioning sequences as update points for

running time estimation. Furthermore, the applicability of knowledge-based systems,

machine learning, neural networks and statistical pattern recognition for accurate and

intelligent prediction of train running times can be examined.

Mining train describers records data has previously been used by Goverde and Hansen

to assign section (track circuit) occupation and release times to a specific train (number)[14], to identify route conflicts and distinguish data sets of hindered and unhindered trains[6], and to find and explain variations in process times in stations [10]. Flier et al. [9] used

data mining technique to determine delay dependencies on a network wide scale. Yuan

[40] performed the ex-post data analysis to investigate delay propagation in stations.



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6 Predictive Traffic Model

6.1 Importance and Main Tasks

Railway operations on heavily utilized networks are characterized by many simultaneous

processes and interdependent events. Deviation from the schedule in one or more

processes can, on such networks, have consequences that are very difficult to predict.

After a deviation from the schedule has been detected, both local and network traffic

controllers need to estimate the impact of the deviation on the future state of traffic in the

controlled area and react accordingly. Accurate prediction of conflicts is another essential

condition for proactive traffic management which implies resolving the conflicts or

diminishing their potential effect before they actually occur. Local controllers have to

predict the possible route conflicts and network controllers determine jeopardized

passenger and logistic connections and estimate the effect of the deviation on global level.

Traffic controllers on both levels need to determine the set of appropriate control actions

which would minimize the negative effect of traffic disturbance on performance

indicators.However, due to the strong interdependence between train runs and the large number

of possible control actions, the impact of detected deviations, as well as the effect of

feasible dispatching decisions is almost impossible to predict without the aid of an

appropriate decision support system.

A model for traffic prediction should, as a supporting tool for traffic controllers, give

accurate forecasts of conflicts (on both global and local level) resulting from the detected

deviations and disruptions in traffic and infrastructure. Moreover, the predictive model

has the task to estimate the effect and evaluate the quality of the potential control

decisions with regard to the objectives which depend on the level of control (tactical or

operational). An important requirement for such models is that the computation time

should be short enough to enable their implementation in real-time applications.

6.2 Mesoscopic Character of the Model

Advanced microscopic simulation tools are able to accurately simulate railway operations

based on a detailed modelling of infrastructure, signalling, rolling stock characteristics,

train dynamics and the timetable [16, 24], but using microscopic models to capture the

structure and processes on large, complex and heavily utilized railway networks can result

in long computation times, which makes such models inappropriate for real-time

applications. On the other hand, macroscopic models based on deterministic process

times between timetable reference points, provide computational performance applicable

even for large networks [13].

Prediction models, in order to be appropriate for implementation in real-world railway

operations, need to fit in the cascade loop control concept described in section 4.2. In

other words, its task depends on the loop in which it is active. In the outer control loop,

the model has to predict the effect of large disruptions and evaluate control actions on a

global level, for which the macroscopic level is appropriate. In the inner control loop, the

predictive model needs to take into account the precise network topology and projectdetailed processes and control decisions on a microscopic level. Thus a mesoscopic model

consisting of a macroscopic model with local microscopic network structures whenever

necessary (i.e. at disrupted areas) would be the appropriate tool for traffic prediction in the



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model-predictive control framework.

6.3 Models Based on Graph Theory

The macroscopic model of railway operations based on timed event graphs and max-plus

algebra [1, 18] allows fast computation of performance indicators and delay propagation

in short time even on large networks for both deterministic and stochastic process times

[2, 11, 12, 13, 17]. However, max-plus systems assume a fixed structure, i.e., fixed train

orders, sequences, and routes. That means that the modelling of dispatching actions which

may prevent delay propagation, such as changing the order of trains, cancelling a train ora connection, etc., can not be done using the conventional max-plus systems.

Van den Boom & De Schutter [9, 33, 34, 35] proposed a new approach called

switching max-plus linear systems that can be used to incorporate discrete dispatching

actions such as train order changes or connection cancellations into the max-plus

framework. This approach uses different max-plus linear models each of which

corresponds to a specific mode describing the railway traffic model with respect to the

specified order of events and synchronization constraints. The system is managed byswitching between different modes, thus allowing the inclusion of discrete decisions into

the model. Goverde [13] presented an efficient graph algorithm for computing delay

propagation on large networks which can be used to evaluate the effect of each set of

dispatching actions (mode, graph structure) on the global level.

In the context of MPC railway traffic management, a predictive traffic model based onswitching max-plus linear systems can be used to predict the values of performance

indicators after certain dispatching actions, where each dispatching action will result in

the switch of the system into an appropriate mode. Offline preconstruction of modes

reflecting all possible dispatching decisions that would be called when needed is not an

option due to the vast consumption of memory in case of modelling large networks withmany interconnected lines. Therefore, the modes should be constructed on the fly by an

algorithm given the current availability of infrastructure and resources.

Schöbel [30] used a graph model based interpretation of railway operations to

optimize the solution to delay management problem. The model was further extended byinclusion of headways and capacity constraints [29].

Another model for real-time railway traffic management that relies on graph

interpretation of railway operations is based on alternative graphs [5, 7, 8]. In this

microscopic graph representation of railway traffic each decision variable (order of trains

at a point of conflict) is modelled by a pair of alternative arcs. Only one arc in each pair

can be selected resulting in dismissing his companion arc. A complete selection

corresponds to the situation where one arc has been chosen from each pair of alternative

arcs defining train orders at each point of conflict. Every choice between alternative arcswithin one pair corresponds to a dispatching decision. In the context of the MPC

framework for railway traffic management, each complete selection of alternative arcs can

be regarded as one mode in switching max-plus algebra framework. The critical path

method can be used to calculate the time of all train events within the prediction horizon.

This representation has been successfully applied for real-time railway traffic application

in local dispatching areas.



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7 Model-Predictive Controller

7.1 Different Aspects of Optimization

Disturbances in railway operations can have different magnitudes. From minor deviations

in process times to major disruptions such as accidents, infrastructure failures, track

closures etc. Their impact depends on the traffic situation, and moreover, on the location

on the railway network and the time of occurrence. In the previous section, the importance

of accurate predictions of conflicts, their consequences, as well as the impact of resulting

control actions on performance indicators in general has been emphasized. However, it is

important to point out that the scale of disruptions, their effect, location, and time of their

occurrence are the actual factors that determine the set of performance indicators that need

to be optimized and the constraints for their optimization. The objectives can range from

minimizing the deviation from the published timetable (in case of minor disturbances) to

maximizing the throughput and ensuring the traffic flow in case of major disruptions. An

overview of the different optimization criteria and the corresponding time requirements

are given by Lüthi [21].In current railway practice, rescheduling on both tactical and operational level is

performed without adequate decision support systems and relies predominantly on the

experience of controllers and predetermined rules which do not guarantee suitability of the

control decisions and their quality [39].

7.2 Hierarchical Approach to Optimization

In the MPC framework for railway traffic management, the objective function and

constraints, in the process of computing the optimal control actions, depend on the loop in

which the model-predictive controller is active. The hierarchical relationship between the

two loops is maintained. That means that the objective for optimization in the inner loop is

minimizing the deviation from the target set by either the published timetable or the outer

loop. The control variables in the inner loop can be the order of trains on conflict points,

assignment of station tracks or routes over interlocking areas, etc. The constraints which

need to be taken into account on the operational level reflect the actual state of traffic and

infrastructure on the microscopic level, and safety requirements.

On the other hand, on the tactical level, where the outer loop is active, optimization

criteria reflect the global network performance indicators, such as total or average

weighted delay, passenger or logistic connections, timetable realization with the planned

resources (personnel crews and rolling stock), maintaining the traffic flow between

stations using predetermined or alternative routs, etc. The constraints which characterize

the optimization on the tactical level include the actual state of traffic and infrastructure

on the global level, availability of resources, market-based and user-oriented constraints,

etc. In other words, the tactical level of model-predictive controller has a task to

determine in real time the feasible values of controllable input variables (schedule of

events, train routes, maintained and cancelled connections, cancelled/added trains,

activation of rolling stock and personnel from “hot reserve”, etc.) that would ensure the

optimal values of performance indicators on the network wide level. The choice of performance indicators, objectives, and criteria for optimization should be user-oriented.

If for instance, performance indicators would rely solely on overall or average delays and

the objective function on their minimization that would surely result in cancelling all

passenger connections and transfers. If reliable data about passenger flows (O-D matrices



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in time windows) could be obtained then a specific penalty coefficient could be assigned

to each connection reflecting the number of passengers which would be affected by its

cancellation.

7.3 Existing Applicable Models

The operations research literature on railways has focused mainly towards optimization

models for offline tactical planning. For operational traffic management however, these

models can not be used due to extensive computation times [3]. Recently developed

railway traffic management algorithms are based on microscopic models and limited to

managing small disruptions without changing the actual timetable and use decomposition

of large networks to local areas [23, 27], corridors [7] or a number of connected and

coordinated small networks [5]. Real-time computation time requirements prevent direct

extensions of these models to large-scale networks of strongly interconnected lines.

Global, network scale optimization therefore requires an efficient higher-level controller

that optimizes the actual state over the overall network and controls the traffic from a

global perspective with adjustments to the timetable. Operational requirements for on-linetraffic management on a global scale are given by Hansen [15] and a survey of models

and algorithms by Törnquist [32] and D’Ariano [7].

As presented in the previous section, the application of the models based on graph

theory is shown to be a promising approach to model and optimize railway operations in

real-time [5, 7 ,13, 29, 30, 33, 34, 35].

8 Summary

This paper presented a framework for dynamic management of railway traffic on large

and heavily utilized networks. The framework follows the pattern and hierarchical

character of the current practice in traffic control and relies on already verified principles

and approaches, combining them into a concept which should result in a proactive tool for

anticipative traffic management. The approach to railway traffic management based on

model-predictive control aims at closing the loop between timetabling and train

operations with a continuous feedback of train positions and field data to allow fast

rescheduling of train paths in case of disturbances. The MPC approach has been divided

in three interconnected components: monitoring, a predictive traffic model, and a model

predictive controller. Each of them has been analyzed separately with regard to its

potential impact on improving the current traffic control practice and supported with the

review of relevant models and approaches from the respective fields. However, only

functional integration of all components would yield the complete decision support

systems for effective traffic control on all levels. Computational aspects have been taken

into account and the applicability in real-time on a network wide level has been analyzed

of each component individually and in the context of possible implementation in the MPC

framework. Results of all presented models reveal that there is a promising starting point

for future research and model development.

AcknowledgementThis paper is a result of the research project funded by the Dutch Technology Foundation,

STW: “Model-Predictive Railway Traffic Management” (project no. 11025).



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