Modeling and Optimization of Traffic Flow in Urban Areas - Czech ...

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60 Chapter 5 Traffic Flow Optimization>> T56 = task(’T(5,6)’, 21.3);>> T26 = task(’T(2,6)’, 68.7);>> T96 = task(’T(9,6)’, 24.4);>> prec6 = [0 1 1; 0 0 0; 0 0 0];>> TS6 = taskset([T56 T26 T96],prec6);>> T67 = task(’T(6,7)’, 29.6);>> T37 = task(’T(3,7)’, 60.4);>> T97 = task(’T(9,7)’, 7.5);>> prec7 = [0 1 1; 0 0 0; 0 0 0];>> TS7 = taskset([T67 T37 T97],prec7);>> T78 = task(’T(7,8)’, 18.4);>> T228 = task(’T(22,8)’, 71.6);>> prec8 = [0 1; 0 0];>> TS8 = taskset([T78 T228],prec8);>> TSall = [TS6 TS7 TS8];>> TSall.Prec(1,4) = 1;>> TSall.Prec(4,7) = 1;>> D = zeros(size(TSall.Prec));>> D(1,4) = 9.5;>> D(4,7) = 8;>> prob = ...problem(’Pinf|prec,out-tree,cjk|Cmax’);>> TSall = chretienne(TSall,p,Inf,D);>> plot(TSall);Fig. 5.3: Solution of the scheduling problem in the toolbox5.5 SummaryIn this chapter, the offset ϕ uv and the split τ vj of intersections were found.The offset is respecting the green wave strategy and the split is optimalunder considered criterion (minimizes the time spend on the road — from thescheduling point of view minimizes C max ). The data from real light controlledintersections in an urban traffic region in Prague was used to demonstrateconsecutive steps of the problem solution.
5.5 Summary 61Intersection 6T 2,6T 9,6T 5,6Intersection 7' 6,7=9.5T 3,7T 9,7T 6,7Intersection 8' 7,8=8T 7,8 T 22,8(a) Precedence constraintsIntersection 6T 2,6T 9,6T 5,6Intersection 7T 3,7T 9,7T 6,7Intersection 8T 7,8¿ 6,1 ¿ 6,2¿ 6,3p 2,6p 5,6p 9,6p 6,7p 9,7' 6,7¿ 7,1¿ 7,2¿ 7,3p 3,7' 7,8¿ 8,1¿ 8,2T 22,8p 7,8p 22,8100 150 200 250 300(b) Gantt chart for vehicle flow including split and offset markFig. 5.4: The intersections (6, 7 and 8) controlt
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Czech Technical University in Pragu
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This thesis is dedicated to my wife
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Nomenclatureα|β|γ Graham and B̷
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NomenclatureviiR 0+ set of non-nega
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AbbreviationsCCPN Constant speed Co
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Goals and ObjectivesThis thesis wil
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ContentsGoals and Objectivesxi1 Int
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List of Tables2.1 Traffic data for
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Chapter 1IntroductionThe urban traf
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1.1 Related Work 3Traffic stream mo
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Chapter 2Simple Light ControledInte
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2.2 Extended Queue Model 72 E( k)AB
Page 27 and 28: 2.2 Extended Queue Model 9E [s]4000
Page 29 and 30: 2.4 Control of The Simple Intersect
Page 37 and 38: Chapter 3General Light ControlledIn
Page 39 and 40: 3.2 Continuous Petri Net 21take the
Page 41 and 42: 3.2 Continuous Petri Net 23v 5V 5vD
Page 43 and 44: 3.3 Light Controlled Intersection M
Page 49 and 50: 3.4 Performance Evaluation 31-1V[uv
Page 51 and 52: 3.4 Performance Evaluation 33-1V [u
Page 53 and 54: Chapter 4TORSCHE SchedulingToolbox
Page 55 and 56: 4.2 Tool Architecture and Basic Not
Page 63 and 64: 4.3 Implemented Algorithm 45tasks a
Page 65 and 66: 4.3 Implemented Algorithm 47leg 1p=
Page 67 and 68: 4.3 Implemented Algorithm 49unit j
Page 69 and 70: 4.3 Implemented Algorithm 51P 2P 1
Page 71 and 72: Chapter 5Traffic Flow OptimizationT
Page 73 and 74: 5.2 Traffic Region Model 5514 1516
Page 75 and 76: 5.3 Tasks Definition for Intersecti
Page 77: 5.4 Scheduling with Communication D
Page 82 and 83: 64 Chapter 6 Conclusionsoptimal und
Page 85 and 86: Appendix ATORSCHE AlgorithmsA.1 Sch
Page 87 and 88: Bibliography[Abou 09] K. Aboudolas,
Page 89 and 90: Bibliography 71[Febb 06] A. D. Febb
Page 91 and 92: Bibliography 73[Kwak 72]Cybernetics
Page 93: Bibliography 75[Tolb 01] C. Tolba.
Page 96 and 97: 78 Indexphase, 3offset, 3, 53split,
Page 99 and 100: List of Author’s PublicationsPape
Page 101: List of Author’s Publications 83P
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