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

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16 Chapter 2 Simple Light Controled Intersection Modellowing experiment was performed. In addition to the original incoming flow,Queue 2 was subjected to one additional vehicle per second from time 30000 sto time 30100 s. Fig. 2.8(a) shows that the increase in the number of vehiclesin Queue 1 is partially compensated by the NMPC, which leads to an increasein the number of vehicles in Queue 2. From the principle (see Equation 2.3)the number of vehicles in Fig. 2.8(a) holds for both models. The mean valueof the waiting times in the extended queue model (shown in Fig. 2.8(b)) isused to calculate the switching time t sw by the NMPC. The same t sw is appliedto the complete queue model (see Fig. 2.8(c)) showing that E in bothqueues is quite well balanced.Table 2.2 shows the complexity of the NMPC calculations in terms ofthe number iterations for the one-day experiment. The left half of the tableshows the complexity results for a prediction horizon of 90 s. The first rowin the first column refers to the complexity of experiments reported up tonow. In general, the control performance can be increased by prolongation ofthe predictive horizon. In the right half of the table, the complexity resultsfor a predictive horizon of 180 s is shown. In all cases, the time complexityis negligible with respect to the cycle time. Nevertheless, we propose twosimple approaches for reducing the problem complexity. First, the t sw doesnot need to be an integer variable (as assumed in the first row in Table 2.2),but can be assumed to achieve a value divisible by 2 (the second row) or by5 (the third row). Second, for practical reasons, t sw (k + 1) does not need tovary from 0 to 90 s (as assumed in the columns with δ = 90, µ = 0), but couldbe allowed to vary only from max{t sw (k) − δ, µ} to min{t sw (k) + δ, 90 − µ}where δ stands for maximal allowed difference of t sw and µ defines a minimalTable 2.2: NMPC complexity in number of iterationspredictive horizon 90 s predictive horizon 180 sδ = 90 δ = 30 δ = 5 δ = 90 δ = 30 δ = 5µ = 0 µ = 10 µ = 10 µ = 0 µ = 10 µ = 101 87451 41328 9905 7958041 1980255 1106212 45167 21927 5995 2122849 548138 393165 19220 10055 3587 384400 113237 13724
2.4 Control of The Simple Intersection Model 17n [uv]100806040200Queue 1Queue 20 1 2 3 4 5 6 7 x 10 4 t [s](a) Number of vehicles in the queueE [s]1400120010008006004002000Queue 1Queue 20 1 2 3 4 5 6 7 x 10 4 t [s](b) Mean waiting times in the extended queue modelE [s]16001400120010008006004002000Queue 1Queue 20 1 2 3 4 5 6 7 x 10 4 t [s](c) Mean waiting times in the complete queue modelFig. 2.8: NMPC with extra vehicles at t = 3 · 10 4 s
Page 1 and 2: Czech Technical University in Pragu
Page 3 and 4: This thesis is dedicated to my wife
Page 5 and 6: Nomenclatureα|β|γ Graham and B̷
Page 7 and 8: NomenclatureviiR 0+ set of non-nega
Page 9 and 10: AbbreviationsCCPN Constant speed Co
Page 11 and 12: Goals and ObjectivesThis thesis wil
Page 13 and 14: ContentsGoals and Objectivesxi1 Int
Page 17 and 18: List of Tables2.1 Traffic data for
Page 19 and 20: Chapter 1IntroductionThe urban traf
Page 21 and 22: 1.1 Related Work 3Traffic stream mo
Page 23 and 24: Chapter 2Simple Light ControledInte
Page 25 and 26: 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 31 and 32: 2.4 Control of The Simple Intersect
Page 33: 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 and 78: 5.4 Scheduling with Communication D
Page 79: 5.5 Summary 61Intersection 6T 2,6T
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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