NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society

Fuzzy Algorithm for Real-Time Train Dispatch and

Contrl*o

Alexandre TazonieroUnicamp - FEEC - DCA

State University of CampinasCampinas-SP-Brazil

aIextaz@dca.fee.unicamp.br

Rodrigo GonqalvesCflex Ltda

Rua Mogi Guapu, 547Campinas-SP-Brazilrodrigo@cflex.com.br

Fernando GomideUnicamp - FEEC - DCA

State University of CampinasCampinas-SP-Brazil

gomide@ dca.fee.unicamp.br

Abstract - This paper discusses a knowledge-based systemthat uses fuzzy set techniques to plan train circulation in freightrailroads. Its purpose is to aid train dispatchers in decision-making and traffic control in real-time. The system uses decisionprocedures based on fuzzy rules with the purpose of driving traincirculation as closed as possible to reference trajectories. It caneasily incorporate operational knowledge and dispatcherexperience as a part of the decision-making strategy. A majorcharacteristic of the system concerns its ability to providefeasible circulation plans in real-time. Incorporation of practicalknowledge and real-time response are key requirements forcontrol and traffic management in train control rooms.

I. INTRODUCTION

In railroad traffic control, train dispatchers control trainmovement over a line, plan the meeting and passing of trainson single-track sections, align switches to control each trainmovement, perform information gathering, reporting, andcommunication with train crew and station or yard workers.

Currently, either centralized or distributed computersystems can be used to support these critical tasks, dependingon the size and railroad network complexity. Computerizeddispatching systems have been evolving from off-linecomputer aided dispatch systems, through in-line automateddispatch and on-line integrated systems for dispatch operationoptimization, traffic management and train control.

Early solutions for the train dispatch problem date from thebeginning of the seventies, with the development of operationsresearch models [I]-[4], linear, mixed programming modelsbeing the ones found most often [5][6]. Heuristic schemessuch as tabu search, local search [7], knowledge-basedsystems [8], have also been developed as an attempt to solvethe problem. At the same time, discrete event dynamicsystems have been introduced as an alternative modelingapproach [9][10]. Currently, fuzzy systems, neural networks,genetic algorithms [i 1][12], distributed and multi-agent agentsystems [13] and hybrids [14] are being investigated.

Essentially a train dispatch system is an instance of aninformation system with local and remote data entryconcerning train characteristics, location and status, andrecord keeping to prepare train graph and train performance

reports. In addition to these information-processing tasks,computer-aided dispatch systems perform the clearing ofsignals ahead of trains, the clearing of trains out of sidings,and suggest meeting and passing decisions. The dispatchermay accept or override decisions and aligns switches. Inautomated dispatch systems the computer selects the meetingand passing, controls switches and signals and the dispatcheroverrides only in exceptional circumstances. Integrateddispatch and train control systems comprise the most complexdispatch systems and, in addition of the previous tasks,perform continuos train operation decisions based oncontinuos feedback of dispatch decisions. Reactive trainscheduling taking into account system state, constraints andcrew shifts also is an important task in integrated systems.

Nowadays most railways worldwide operate undercentralized traffic control systems [15] often with distributedcomputer architectures and local area networks withcommunication capabilities using radio, optic fiber andsatellite. Multi-agent systems solutions have been consideredas an alternative approach to construct future systems.

This paper describes a knowledge-based system that usesfuzzy set techniques [16,17] to plan train circulation in freightrailroads. Its purpose is to help dispatchers in decision-makingand real-time train control. The system uses rule-based fuzzydecision-making procedure to drive trains as closed aspossible to ideal trajectories. It can incorporate operationalknowledge and dispatcher experience to compose thedecision-making strategy. Incorporation of knowledge andreal-time response are key requirements for trafficmanagement railroad control.

The paper is organized as follows. The train dispatchplanning and control system structure is summarized in thenext section. The fuzzy decision procedure is addressed inSection III. Next, comparison with optimal decisions obtainedusing a mixed linear programming model and geneticalgorithms is discussed. A case study conducted for a hightraffic section of a major Brazilian railway is reported. Thepaper concludes summarizing the main results and issues to beaddressed in the future.

This work is partially supported CNPq, the Brazilian National Research Council Grant #475295/03-0 and 304299/03-0.

0-7803-9187-X1051$20.00 ©2005 IEEE. 332

II. SYSTEM STRUCTURE

A set of modules composes the train dispatch planningand control system. The main module consists of a detaileddiscrete event model and system simulator [10] to simulatetrains movement considering operational constraints, lineblockage [10] and production objectives as well. Examples ofoperational constraints include: trains can not be at the samesingle track section at the same time; trains must be fuelled atspecific fueling stations; train meetings must occur at siding;time windows for rail maintenance and crew work mustconsidered. The system structure and modules is shown inFig. 1. The Man Machine Interface allows dispatchers to editand custom the circulation plan. The Global Optimizationmodule uses a mixed linear programming model [6] andgenetic fuzzy algorithms [12] to generate optimal trajectoriesas references to the simulator. As simulation advances,conflicting situations in which trains compete for the sametrack at the same time often occur. In these situations thesystem must decide which train has the preference to occupythe track and which train must stop at a siding. At this pointthe simulator calls the fuzzy decision-making algorithm thatencodes the decision strategy.

Man Train

Machine ActivitiesDBJ Interface Line State

Planning

4 andresults

Operational objectives, Constraints, Priorities, Line topology.

Fig. 1. System structure.

The decision-making strategy is the heuristics encoded ina fuzzy rule base that classify trains states and movements totake the most appropriate decision. The rules are usuallyderived from the dispatcher experience and engineeringknowledge.A typical rule often used by dispatchers is: if a train with a

large delay meets another train that is ahead of its desiredtime reference, then the delayed train has higher preference tooccupy the track.

Fuzzy set techniques are used primarily to analyze and todecide train movement situations. Train delay and advanceare defined assuming a reference trajectory with lower andupper boundary lines as depicted in Fig. 2.

The reference trajectory may be seen as a reference for thetrain movement along the railroad section corresponding to atime horizon. The reference trajectory can either beconstructed by the dispatcher or obtained by an (global)optimization model.

We note that, as shown in Fig. 2, whenever a train iswithin Area 1 it is advanced and when within Area 2 it isdelayed. Delays and time advances are granulated by fuzzysets defined in normalized universes as depicted in Fig. 3where ND = no delay, SD = small delay, BD = big delay, andSA = small advance, BA = big advance.

0 Time

Fig. 2. Reference trajectory and bounds.

Whenever two trains compete for a track, their currentstates are classified using the fuzzy sets and rule bases areused to get a decision. During each decision-making cycle,trains delays or advances are evaluated using linguistic labelsshown in Fig. 3.A decision table mapping the fuzzy rule base, Fig. 4, is

used to find the relative priorities between trains. The shadedregion of Fig. 4 means situations in which competing trainsare at equivalent states or situations. In this case the finaldecision will depend on others factors such as trainsregularity, number of stops, default priorities, etc. In theapplication example and scenarios addressed in next section,the decision strategy aims at reducing the total delay, that is,the sum of the delays of all trains within the planning horizon.When trains are at similar states, the decision is to assign thetrack to the train that will traverse the conflicting yard first.

/ / ~~~~Current/traiecttorYard

*.Referencetraiecto)rv.

-1L+1

BA SA ND SD BD

Fig. 3. Characterization of delays and time advances.

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1.

One of the most important characteristics train duringtravels is the frequent and unexpected events that occur suchas train faults, line outages, crew problems, load and unloaddelays, etc. In general this means that, in actualcircumstances, the idea of optimal decisions should be viewedmore carefully. Often, whenever an optimal solution is found,the state of the railroad system is different from the oneoriginally assumed by the solution and, therefore, solutionmay be no longer valid. For this reason real-time, knowledgebased solutions are often preferred in practice because it canrespond faster and represent more realistically trade-offs traindispatchers have to do in control rooms.

HI SIMULATIONS RESULTS

In this section we address the results provided by theknowledge based system detailed above and compare thesolutions it develops with the ones obtained when using amixed linear programming model and a fuzzy geneticalgorithm (global optimisation module) described in [6] and[12], respectively. Due the considerable processing timeneeded to obtain a solution from the mathematicalprogramming model, only small size problems are used herefor comparison purposes. As it is well known, train dispatchproblems of the type addressed in this paper are np-hard [8].Therefore, we consider a rail line with five single-tracksegments separated by four double track segments forcrossing and passing, and six trains, three in each direction.

<Priorltv A

ND%>*-------------------.-.-............... ................... - -.-4...........................

g D g > ~~~~~~~~~~~~~~~~~~~~~~~~~............ ,,v..............

........ ......................../00:/ . . ~~~~~Priqrity: B.r ...............I..............

_

A

solution provided by the mathematical model (MPM) and thesolution obtained by the genetic fuzzy algorithm (GFA) wereadopted as the reference trajectory used by the fuzzy decision-making algorithm. Trains trajectories that include trainsactivities program (TAP) was adopted as the referencetrajectory, albeit in practice they can be hardly followed. In allsimulations we assume that the main objective is to reduce thetotal train circulation delay. Table I summarizes thesimulation results for each ofthe case.

TABLE I.TOTAL DELAY (M NUTES)

GFA |MPM FDMoFA FDMMpM FDMTApCase 1 248 | 248 258 258 369Case 2 539 349 617 448 653

Case 3 505 320 519 519 519

Case 4 720 350 720 720 720

FGA: Genetic Fuzzy AlgorithmMPM: Mathematic Progranmning ModelFDMOFA: Fuzzy Decision Making with GFA reference.FDMMpM: Fuzzy Decision Making withMPM reference.FDMTAP: Fuzzy Decision Making with TAP reference.

Fig. 5 shows the optimal solution provided by themathematical model for the second case, the one with amaintenance window. Fig. 6 shows the results when thisoptimal solution is used as a reference for the fuzzy decision-making algorithm. The same with the genetic fuzzy algorithmis depicted in Fig. 7 and the corresponding fuzzy decision-making algorithm in Fig. 8. In this case, the fuzzy decision-making algorithm fully follows the reference line provided bythe GFA. Fig. 9 shows the results when the TAP is used as areference.

B S N S BA A D D D

TRAIN B

Fig. 4. Decision table.

Using the railroad scenario as above, four different casesare addressed. The first considers the trains running assumingthat no line maintenance or mandatory stops are required.Next we consider the same case except that we require a timewindow for rail line maintenance. The third assumesmandatory stops for train refueling, one for each train. Finally,the last case considers both, time window for rail linemaintenance and refueling stops. For each case both, the

Fig. 5. Optimal solution provided by the mathematical programmingmodel.

334

I

Fij

.0Hzg000=00042Hzk 4s:C1Hz aH8Hz10XsHz2Hz 14 1 Generally speaking, we note that the fuzzy decision

oTAA W ...............:makingalgorithm is able to follow optimal referencetrajectories closely in a fast and efficient way. Themathematical programming model spent one minute to solve

TACAOI :j.~~~~~~~, ~~ the first and five minutes to solve the last case whereas thegenetic fuzyalgorithm spent about 3 minutes and the fuizzy

M~14TIE 2 t:< g>i; | ;:ks :W. 0 \ ''"XtX tkgiXFiX decision-making algorithm less than 50 ms in all four cases.

'1'0z 2Hz 4Hz -oH s 8z 10z 121 1

ESTAA.

TACA04 - : - .

8STACA02'-~

. 6. Fuzzy decision mnakdng solution using WM reference.

TACAboX-1'EMET

MENTOI

wMENT02 = 9 si f lil Fig. 9. Fuzzy decision-making solution using Trains Activities Programreference.

tACA03

MENT0 It is worth to note that, as Case I of Table I shows, that theTACA ~~~~~~~~~~fuizzydecision-making algorithm does follow the optimal

MEN4ThTrl base, hoee,de oeuse specific knowledgeconcerning the remaining cases situations, although they are

n ~~~~~ mirrored~~~~~~~in the reference trajectories. This means total delayFig. 7. Solution developed by the genetic fizzy algorthm values about 100% greater that the optimal values. This

OHs .HZis 4Hi z HaH ..10Hz12Hz 1I4 suggests that the rule base must also encode information about

rail line improve performance. This issue is currently

TACAQOOm iE under investigation. Interestingly, the GFA, as well asIMENTOO conventional genetic algorithm, were not able to achieve the

TACA0O An instance of train circulation planning using actual dataMENtObI 0 0 0 0 0 fro a major Brazilian railway section with 40 trains and 32

psigyards has also been considered as a test case. In thisTACAO2~~ ~ ~ ~ ~ ~ ~ ~~isanethe mathematical programmning model needs several

MEN T0 daysto solve it using conventional optimization packages,weesthe genetic algorithm needs 15 minutes. The

TACA01 knowledge-based system with the fuzzy decision-makingalgorithm described in this paper requires 200 ms to produceMENTO3anacceptable schedule.

3TACAOSl3'.,.3rfrne

MENTO I~~~~~~~~~~~~~~V.CONCLUSIONS

tME>NTACA X0;f iX =:t; g;C4:0 i; ts ki-'0 oE34 ItiswoTheknowledge-based system suggested in this paper hasFig. 8. Fuzzy decision-making solution using GFA reference shown to provide a promising mechanism to obtain

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satisfactory solutions in real-time train dispatch planning andtraffic management. This feature is of utmost importance intrain dispatch and control systems, where the unpredictablyand intractability of the problem makes conventionalapproaches and systems ineffective. In addition, since thesystem uses a detailed discrete event model of the railroad,the solutions obtained are often closer to the practicalrequirements than conventional mathematical programmingmodels.

Future work will focus on the frizzy decision-algorithmand the underlying heuristics to improve performance incomplex operational while keeping low processing timerequirements. The use of a short-term look-ahead searchprocedure is an approach currently under investigation.

[13] Iyer R. V., Ghosh S., "DARYN - A Distributed Decision-MakingAlgorythm for Railway Networks: Modeling and Simulation", IEEETransactions on vehicular technology, vol. 44, no. 1, 1995

[14] A. Higgins, E. Kozan, L. Ferreira.,"Heuristic Techniques for SingleLine Train Scheduling', J. Heuristics, vol.3, no. 1, pp. 43 - 62, 1997

[15] G. Vemazza, R. Zunino, "A distributed intelligence methodology forrailway traffic control". IEEE Trans. on Vehicular Technology, 39:263-270, 1990.

[16] F. Gomide, W. Pedrycz,. An Introduction to Fuzzy Sets: Analysis andDesign. MIT Press, Cambridge, 1998.

[17] R.Yager, L. Zadeh, An Introduction to Fuzzy Logic Applications inIntelligent Systems, Kluwer Academic Publishers, Boston, 1992.

ACKNOWLEDGMENT

The authors are grateful to Magali Rondon, Francisco Mota,Alexander Do Valle, Luis Elesbao Oliveira Neto for theirhelpful contributions.

REFERENCES

[1] B. Szpigel, "Optimal Train Scheduling on Single TrackRailway",Operations Research, vol. 20, 1972.

[2] R. L Sauder, M. Eaterman, "Computer-Aided Train DispatchingDecision Support Through Optimization", Interfaces, vol.13, 1983.

[3] D.Jovanovic et al., Improving Railroad on-time Performance: models,algorithms and applications, Ph.D thesis, University of Pennsylvania,1989.

[4] C. Jiaming, "The optimal model and algorithms for single track lines",Railway Transaction, vol. 16, no. 3, pp. 72-78, 1994

[5] A. Higgins, E. Kozan, L. Ferreira., "Heuristic Techniques for SingleLine Train Scheduling", J. Heuristics, vol.3, no. 1, pp. 43 - 62, 1997

[6] A. Vale, F. Gomide, R. Goncalves, "Fuzzy Optimization Model forTrain Dispatch Systems", IFSA World Congress, Beijing, China, July2005 (to be published).

[7] A.L. Chemiavsky, "A Program for Timetable Compilation by a Look-Ahead Method", Artificial Intelligence, vol3, pp-61-67, 1972. Instituteof Control Science, Moscow.

[8] H. Chiang, T. Chiang, H. Hau, C. Hsieh, S. Ko, "Knowledge-basedsystem for railway scheduling". Data & Knowledge Engineering 27, pp,289-312, 1998.

[9] E. Petersen, A. Taylor, "A Structured Model for Rail Line Simulationand Optimization", Transportation Science 16312), pp. 192-205, 1982.

[10] M. Rondon, Modelagem Estruturada e Sistemas Inteligentes: UmaAplicafdo a Sistemas de Transporte Ferroviario, MSc. thesis,Universidade Estadual de Campinas, 2000.

[11] X. Cai, V. Salim, "Scheduling Cargo Trains Using Genetic Algorithms",1995.

[12] F. Mota, F. Gomide, R. Gon9alves, "Genetic Algorithm, FuzzyClustering and Discrete Event Systems: An Application in Scheduling",Proc. of the I Workshop on Genetic Fuzzy Systems, Granada, Spain,March 2005, pp. 83-88.

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