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研究生：	陳麗雯 Chen, Li-Wen
論文名稱：	號誌控制與動態交通量指派考量下路網車流動態均衡之研究 DYNAMIC EQUILIBRIUM OF TRAFFIC NETWORKS UNDER SIGNAL SETTINGS AND DYNAMIC TRAFFIC ASSIGNMENT
指導教授：	胡大瀛 Hu, Ta-Yin
學位類別：	博士 Doctor
系所名稱：	管理學院 - 交通管理科學系 Department of Transportation and Communication Management Science
論文出版年：	2012
畢業學年度：	100
語文別：	英文
論文頁數：	122
中文關鍵詞：	雙層問題、動態路網車流均衡、號誌控制、動態使用者均衡、巨集式啟發式演算法
外文關鍵詞：	bi-level problem, dynamic equilibrium, signal control, dynamic user equilibrium, meta-heuristic algorithm
相關次數：	點閱：79 下載：12
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交通控制策略與使用者路徑選擇行為的互動是影響交通管理的重要的議題。尤其在智慧型運輸系統 (Intelligent Transportation Systems, ITS) 環境下，即時性與動態性的短期預測與策略研擬可增進路網績效。現今在ITS技術的支援下，可取得追蹤動態依時資料，透過適當方法的研析，可獲得較符合車流需求的管理策略訊息。在都市路網設計以及先進交通管理系統 (Advanced Traffic Management Systems, ATMS) 中，交通號誌與使用者路徑選擇行為的議題是供給與需求面重要管理控制項目，在兩者對路網同時產生擾動的情況下，本研究希望探討路網動態車流的均衡狀態。此課題有兩個基本問題：(1) 動態均衡之存在情形；(2) 動態均衡之求解方法。本研究以雙層問題來剖析路網變化情形，上層討論號誌最佳化問題，下層考慮動態依時性使用者均衡問題，並定義動態均衡的條件。研究中首先以數學模型來描述動態均衡路網問題，考慮可能的影響變數，並透過巨集啟發式演算法的應用 (包含隨機性禁制搜尋演算法與貪婪式隨機禁制搜尋演算法) 來協助尋找問題中時制計畫設定值的全域最佳解。在ITS環境下，此研究成果可作為協助路網管理單位研擬道路管理策略之方法，使其擬定的策略考慮隨時間變化的路網車流特性，將更符合路網車流管理需求，並可協助減少車流延滯、提升車流運行績效以及車輛的有效使用。研究中透過一系列的實際路網-高雄市三民區路網進行數值實驗，首先透過定時與觸動號誌控制策略的實驗，觀察到在號誌控制與交通指派交互作用下所呈現的動態均衡趨勢。而在隨機性禁制演算法應用於雙層架構的研究中，由實驗觀察到，與動態使用者均衡結果相比，整體平均旅行時間改善的幅度可達20%。此外，本研究以貪婪式隨機演算法進一步深入討論號誌最佳化求解，透過實驗數據觀察到時差的可能效用，最後將此法以逐日實驗觀察在雙層架構運作下的效果，實驗中則發現，在中流量需求 (約56,400部車) 下，整體平均旅行時間可減少達25%。由此可見本研究所提出方法所具備的效益與潛在能力。

In the network design problem, the issue of finding equilibrium under traffic control and route choice is important, especially in designing efficient Advanced Traffic Management Systems (ATMS). From previous research and empirical studies, inappropriate signal settings might lead to worsen the traffic travel condition. In order to design efficient ATMS, time-dependent flow distributions should be considered in signal control optimization process. In Intelligent Transportation Systems (ITS) environment, new technologies provide time-dependent traffic monitoring capability and real-time/dynamic data under ITS environment. Two basic questions arise in these representations: (1) the existence of dynamic equilibrium; (2) approaches to solve this equilibrium problem.
This research proposes a bi-level framework for solving the problem combined with signal settings and route control strategies. The upper-level deals with signal control strategies under given flow distributions, and the lower-level deals with time-dependent route assignment under assumed traffic control policies. In this study, solution procedure is constructed to illustrate the evolution of flow distributions under the interaction of signal and route control. Then, mathematical formulations are developed to discuss the dynamic equilibrium issue from a theoretical point of view. Meta-heuristic algorithms, randomized tabu search algorithm and greedy randomized tabu search algorithm are applied for searching near global signal optimal solution. With the flexibility of the meta-heuristic algorithms, detailed representations of signal control settings could be added easily. The study contributions and application is to help making traffic control and route guidance strategies based on dynamic traffic conditions.
Series of numerical experiments are conducted on a real network, Kaohsiung City sub-network. The results indicate that the existence of equilibrium flow under signal control and route assignment can be observed in a dynamic aspect. Under the bi-level framework, the system performance in terms of average travel time can be improved through the proposed randomized tabu search method by 20% in medium demand level (about 56,400 vehicles). The results of the proposed greedy randomized tabu search method illustrate possible benefits such as more than 25% reduction of travel time can be achieved for medium demand level from the proposed approach. The proposed heuristic approach provides easy-to-use and flexibility for practitioners.

TABLE OF CONTENTS
ACKNOWLEDGEMENTS ....................................................................................... I
CHINESE ACKNOWLEDGEMENTS .............................................................................. II
ABSTRACT ............................................................................................. III
CHINESE ABSTRACT ...................................................................................... IV
TABLE OF CONTENTS ...................................................................................... V
LIST OF TABLES ........................................................................................ IX
LIST OF FIGURES ....................................................................................... XI

CHAPTER 1 INTRODUCTION ................................................................................. 1
1 Background and Motivation .......................................................................... 1
2 Objectives ......................................................................................... 2
3 Research Flowchart ................................................................................. 3
4 Overview ........................................................................................... 5

CHAPTER 2 LITERATURE REVIEW ............................................................................ 6
1 Problem Classifications ............................................................................ 6
2 Network Dynamic Equilibrium Condition and Approaches ............................................... 7
2.1 Definition of Network Equilibrium ................................................................ 7
2.2 Approaches to Achieve the Dynamic Equilibrium .................................................... 7
3 Description of Sub-problems ........................................................................ 8
3.1 Sub-problem: Signal Settings Problem.............................................................. 8
3.2 Sub-problem: Traffic Assignment Problem .......................................................... 9
3.3 Problems Combined with Two Sub-problems ......................................................... 10
4 Solution Approaches ............................................................................... 11
5 Mixed Approaches and Applications ................................................................. 13
6 Mathematical Formulation .......................................................................... 14
7 Dynamic Traffic Assignment ........................................................................ 16
7.1 TrEPS ........................................................................................... 16
7.2 DYNASMART ....................................................................................... 16
7.3 DynaMIT ......................................................................................... 17
7.4 DynaTAIWAN ...................................................................................... 18
8 Heuristic Approach ................................................................................ 20
9 Summary ........................................................................................... 21

CHAPTER 3 RESEARCH METHODOLOGY ........................................................................ 23
1 Problem Statement ................................................................................. 23
2 Bi-level Framework ................................................................................ 24
3 Mathematical Formulation for the Bi-level Problem ................................................. 26
3.1 Upper Level ..................................................................................... 28
3.2 Lower Level ..................................................................................... 33
4 Dynamic Equilibrium ............................................................................... 35
5 Summary ........................................................................................... 37

CHAPTER 4 SIMULATION-BASED DYNAMIC TRAFFIC ASSIGNMENT MODEL ........................................... 38
1 Conceptual Framework .............................................................................. 38
2 Solution Algorithms and Implementation Issues ..................................................... 40
2.1 Solution Algorithm .............................................................................. 40
2.2 MSA and Convergence ............................................................................. 43
3 Numerical Experiment for a Test Network ........................................................... 44
3.1 Experimental Setups ............................................................................. 44
3.2 Result Analysis ................................................................................. 46
4 Numerical Experiments for the Taichung City Network ............................................... 52
5 Summary ........................................................................................... 54

CHAPTER 5 FLOW EQUILIBRIUM UNDER DYNAMIC TRAFFIC ASSIGNMENT AND SIGNAL CONTROL ........................ 55
1 Bi-level Framework ................................................................................ 55
2 Signal Settings Strategies ........................................................................ 56
3 Experimental Setups ............................................................................... 60
4 Result Analysis ................................................................................... 62
5 Summary ........................................................................................... 74

CHAPTER 6 RANDOMIZED TABU SEARCH ALGORITHM WITH BI-LEVEL FRAMEWORK .................................... 76
1 Randomized-based tabu Search ...................................................................... 76
2 Implementation of the Algorithm ................................................................... 78
2.1 Solution Algorithm .............................................................................. 79
2.2 Encoding of Solution ............................................................................ 80
3 Experimental Setups ............................................................................... 81
4 Result Analysis ................................................................................... 82
5 Summary ........................................................................................... 91

CHAPTER 7 TRAFFIC SIGNAL OPTIMIZATION WITH GREEDY RANDOMIZED TABU SEARCH ALGORITHM .................... 93
1 Greedy Randomized tabu Search ..................................................................... 93
2 Implementation of the Algorithm ................................................................... 94
2.1 Solution Algorithm .............................................................................. 94
2.2 Encoding of Solution ............................................................................ 95
3 Genetic Algorithm ................................................................................. 96
4 Numerical Experiments for a Test Network .......................................................... 97
4.1 Network Description ............................................................................. 97
4.2 Experimental Setups ............................................................................. 99
4.3 Result Analysis ................................................................................. 99
5 Numerical Experiments for Kaohsiung City Sub-network ............................................. 102
5.1 Experimental Setups ............................................................................ 102
5.2 Result Analysis ................................................................................ 104
6 Summary .......................................................................................... 109

CHAPTER 8 CONCLUSIONS ................................................................................ 110
1 Overall Conclusions .............................................................................. 110
2 Research Contributions ........................................................................... 112
3 Limitations and Future Comments .................................................................. 112

REFERENCES ........................................................................................... 114

VITA ................................................................................................. 122
                                    

Abdelfatah, A.S. and Mahmassani, H.S. (1998) “System optimal time-dependent path assignment and signal timing in traffic network,” Transportation Research Record No. 1645, Transportation Research Board, Washington, D.C., pp. 185-193.

Abdelfatah, A.S. and Mahmassani, H.S. (2001), “A simulation based signal optimization algorithm within a dynamic traffic assignment framework,” In: Proceedings of 2001 IEEE Intelligent Transportation Systems Conference, Oakland, CA, pp. 428–433.

Abu-Lebdeh, G. and Benekohal, R.F. (2000), “Genetic algorithms for traffic signal control and queue management of oversaturated two-way arterials,” Transportation Research Record, No. 1727, Transportation Research Board, Washington, D.C., pp. 61–67.

Allsop, R.E. (1974), “Some possibilities for using traffic control to influence trip distribution and route choice,” In: Buckley DJ, (Ed.), The Proceeding of the 6th International Symposium on Transportation and Traffic Theory. New York: Elsevier; pp. 345-374

Allsop, R.E. and Charlesworth, J.A. (1977), “Traffic in a signal-controlled road network: an example of different signal timings including different routings,” Traffic Engineering Control, Vol. 18, No. 5, pp. 262-264.

Balakrishna, R., Koutsopoulos, H.N., Ben-Akiva, M., Ruiz, B.M.F., and Mehta, M.(2005), “Simulation-based evaluation of Advanced Traveler Information Systems,” Transportation Research Record, No, 1910, Transportation Research Board, Washington, D.C., pp. 90-98.

Beard, C. and Ziliaskopoulos, A. (2006), “System optimal signal optimization formulation,” Transportation Research Record, No. 1978, Transportation Research Board, Washington, D.C., pp. 102-112.

Beckmann, M.J., McGuire, C.B. and Winston, C.B. (1956), “Studies in the economics of transportation,” Yale University Press, Cooecticut.

Booch, G., Rumbaugh, J., and Jacobson, I. (1999), “The unified modeling language: user guide,” Addison-Wesley, Reading, MA.

Cantarella, G.E., Improta, G. and Sforza, A. (1991a), “An iterative procedure for equilibrium network traffic signal setting,” Transportation Research, Vol. 24A, pp. 241-249.

Cantarella, G.E., Improta, G. and Sforza, A. (1991b), “Road network signal setting: equilibrium conditions,” In: Papageorgiou, M. (Ed.), Concise Encyclopedia of Traffic and Transportation Systems. Pergamon Press, pp. 366-371.

Cantarella, G.E. and Sforza, A. (1995), “Network design models and methods for urban traffic management,” In: Gartner, N.H., Improta, G. (Eds.), Urban Traffic Networks–Dynamic Flow Modeling and Control. Springer, pp. 123-153.

Cascetta, E., Gallo, M. and Montella, B. (1998), “Optimal signal setting on traffic networks with stochastic equilibrium assignment,” In: Proceedings of TRISTAN III, Puerto Rico, Vol. 2.

Ceylan, H. (2002), “A genetic algorithm approach to the equilibrium network design problem,” Ph.D. Dissertation, University of Newcastle upon Tyne.

Ceylan, H. and Bell, M.G.H. (2004), “Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing,” Transportation Research Vol.38B, No. 4, pp. 329-342.

Ceylan, H. and Bell, M.G.H. (2005), “Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion,” Transportation Research Vol. 39B, No. 2, pp. 169-185.

Chan, K.S. and Lam, H.K. (1998), “A sensitivity analysis based algorithm for determining the optimal detector density of the network with variable message sign,” In: Proceedings of the 3rd Conference of the HongKong Society for Transportation Studies, HongKongPolytechnic University, HongKong, pp. 247-256.

Chen, O.J. and Ben-Akiva, M.E. (1998), “Game-theoretic formulations of the interactions between dynamic traffic control and dynamic traffic assignment,” Transportation Research Record, No. 1617, Transportation Research Board, Washington, D.C., pp. 179-188.

Chen, H.K. and Hsueh C.F. (1997), “Combing signal timing plan and dynamic traffic assignment,” In: Proceedings of the 76th Annual Meeting of the Transportation Research Board, Washington D.C., U.S.A.

Chiou, S.W. (1999), “Optimisation of area traffic control for equilibrium network flows,” Transportation Science, Vol. 33, No. 3, pp. 279-289.

Cipriani, E. and Fusco, G. (2004), “Combined signal setting design and traffic assignment problem,” European Journal of Operational Research, Vol. 155, No. 3, pp. 569-583.

FHWA (2001), “A roadmap for the research: development and deployment of traffic estimation and prediction systems for real-time and off-line applications (TREPS, TREPS-P)” (Revised Version, 2001).

Fowler, M., and Scott, K., UML Distilled (2000), “A brief guide to the standard object modeling language,” Addison Wesley Longman, Inc.

Foy, M. D. , Benekohal, R. F. and Goldberg, D. E. (1992). “Signal timing determination using genetic algorithms.” Transportation Research Record, No. 1365, Transportation Research Board, Washington, D.C., pp.108-115.

Gao and Song (2002), “A reserve capacity model of optimal signal control with user-equilibrium route choice,” Transportation Research, Vol. 36B, No4, pp. 313-323.

Gartner, N.H. (1974) Area traffic control and network equilibrium methods, In: Floran, M. (Ed.), Traffic Equilibrium Methods. Springer-Verlag, Berlin, pp. 274-297.

Gartner, N.H. (1977), “Analysis and control of transportation networks by Frank–Wolfe decomposition,” In: Proceedings of the 7th International Symposium on Transportation and Traffic Theory, Kyoto.

Gartner, N.H. and Al-Malik, M. (1996), “Combined model for signal control and choice in urban networks,” Transportation Research Record, No. 1554, Transportation Research Board, Washington, D.C., pp. 27-35.

Gartner, N.H. and Stamatiadis, C. (1997), “Integration of dynamic traffic assignment with real-time traffic adaptive control system,” Transportation Research Record, No. 1644, Transportation Research Board, Washington, D.C., pp. 150-156.

Ghatee, M. and Hashemi, S.M. (2007) Descent direction algorithm with multicommodity flow problem for signal optimization and traffic assignment jointly, Applied Mathematics and Computation, Vol. 188, No. 1, pp. 555-566.

Girianna, M. and Benekohal, R.F. (2001), “Dynamic signal coordination for networks with oversaturated intersections,” Transportation Research Record, No. 1811, Transportation Research Board, Washington, D.C., pp. 122-130.

Glover, F. and Laguna, M. (1997), “Tabu search,” Kluwer academic publishers, Norwell, MA, U.S.A..

Glover, F., Kelly, J. P. and Laguna, M. (1995), “Genetic algorithms and tabu search: hybrids for optimization,” Computers & Operations Research, Vol. 22, No. 1, pp. 111-134.

Hadi, M. A. and Wallace, C. E. (1993). “Hybrid genetic algorithm to optimize signal phasing and timing.” Transportation Research Record, No 1421, Transportation Research Board, Washington, D.C., pp. 104-112.

Hu, T.Y., Liao, T.Y. Chen, L.W., Huang, Y.K., and Chiang, M.L. (2007), “Dynamic simulation-assignment model (DynaTAIWAN) under mixed traffic flows for ITS applications,” In: Proceedings of 86th Transportation Research Board Annual Meeting, Washington D.C., U.S.A..

Hu, T.Y., Tong, C.C., Liao, T.Y. and Chen, L.W.(2008), “Simulation-based dynamic traffic assignment model for mixed traffic flows,” In: Proceedings of 87th Transportation Research Board Annual Meeting, Washington D.C., U.S.A..

Husch, D. and Albeck, J. (2006), “SYNCHRO Studio 7 User Guide,” Trafficware, Albany, California.

Kesur, K. B. (2009). “Advances in genetic algorithm optimization of traffic signals.” Journal of Transportation Engineering, Vol. 135, No. 4. pp.160-173.

Karoonsoontawong, A. and Waller, S.T., (2009), “Combined dynamic user equilibrium and traffic signal optimization problem: reactive Tabu search Approach,” In: Proceedings of 88th Transportation Research Board Meeting, Washington, D.C., U.S.A..

Lee, C. (1998), “Combined traffic signal control and traffic assignment: algorithms, implementation and numerical results,” Ph.D. Dissertation, The University of Texas at Austin, Austin, Texas.

Lee, C. and Machemehl, R.B. (1998), “Genetic algorithm, local and iterative searches for combining traffic assignment and signal control,” In: Proceedings of ICTTS’98, Traffic and transportation studies, pp. 489-497.

Lee, C. and Machemehl, R. (1999), “Local and iterative search for the combined signal control and assignment problem: Implementation and numerical examples,” In: Proceedings of the 78th Transportation Research Board Meeting, Washington, D.C..

Lewis, C.D. (1982), “Industrial and business forecasting methods,” London, Butterworths Science, pp. 37-42,.

Liao, T.Y., Hu, T.Y., Chen, L.W., and Ho W.M. (2010), “Development and empirical study of real-Time simulation-based dynamic traffic assignment model,” ASCE Transportation Engineering, Vol. 136, No. 11, pp. 1008-1020.

Mahmassani, H.S. and Peeta, S. (1993),”Network performance under system optimal and user equilibroum dynamic assignments: implications for ATIS,” Transportation Research Record No. 1408, Transportation Research Board, Washington, D.C., pp. 83-93.

Mahmassani, H.S., Hu, T.Y., and Peeta, S. (1994). "Microsimulation-based procedures for dynamic network traffic assignment", In: Proceedings of the PTRC Conference, London, UK, September 1994.

Mahmassani, H.S. (2001), “Dynamic network traffic assignment and simulation methodology for advanced system management applications,” Networks and Spatial Economics, Vol. 1, No. 3, pp. 267-292.

Mahmassani, H.S., Sbayti, H., Victoria, Zhou, Z., and Chiu, Y.C. (2003a), “DYNASMART-P calibration and evaluation, TREPS-P phase 1.5C final report,” Volume I, February,2003.

Mahmassani, H.S., Sbayti, H., Victoria, Zhou, X.S., and Chiu, Y.C.(2003b), “DYNASMART-P evaluation for operational planning applications, TREPS-P phase 1.5C final report,” Volume II, February, 2003.

Mahmassani, H.S., Sbayti, H., and Zhou, X.S.(2004a), “DYNASMART-P version 1.0 user’s guide,” Technical Report, August, 2004.

Mahmassani, H.S., Qin, Z. and Zhou, Z. (2004b) “DYNASMART-X evaluation for real-time TMC application: Irvine test bed, TREPS Phase 1.5B final technical report,” January, 2004.

Marcotte, P. (1983), “Network optimization with continuous control parameters,” Transportation Science, Vol.17, No. 2, pp. 181 - 197.

MIT (2002), “Development of a dynamic traffic assignment system for planning purposes: DynaMIT-P user’s guide version 2.0,” Technical Report, 2002.

MIT (2003a), “Development of a deployable real-time dynamic traffic assignment system-- evaluation report (Part A): evaluation of estimation and prediction capabilities,” Technical Report, February, 2003.

MIT (2003b), “Development of a deployable real-time dynamic traffic assignment system--evaluation report (Part B): planning functionality (DynaMIT-P) and applications,” February, 2003.

MIT (2003c), “Development of a deployable real-time dynamic traffic assignment system--DynaMIT-R user's guide version 2.0,” Technical Report, February, 2003.

Newell, G. (1989), “Theory of highway traffic signals,” Course Notes UCB-ITS- CN- 89-1, Institute of Transportation Studies, U.C. Berkeley.

Park, B. (1998), “Development of genetic algorithm-based signal optimization program for oversaturated intersection,” Ph.D. Dissertation, University of Texas A&M.

Park, B., Messer, C. J. and Urbanik II, T. (1999). “Traffic signal optimization program for oversaturated conditions genetic algorithm approach.” Transportation Research Record, No. 1683, Transportation Research Board, Washington, D.C., pp. 133-142.

Park, B., Rouphail, N.M. and Sacks, J. (2001). “Assessment of stochastic signal optimization method using microsimulation.” Transportation Research Record, No. 1748, Transportation Research Board, Washington, D.C., pp. 40-45.

Pavese, O. (1968), “La funzione delle intersezioni in un modello di assegnazione del traffico a reti stradali,” Segnalazioni stradali, n. 34, marzo-aprile.

Peeta, S. (1994), “System optimal dynamic traffic assignment in congested networks with advanced information systems,” Ph.D. Dissertation, University of Texas at Austin.

Resende, M. G.C. and Ribeiro, C.C. (2003). “Greedy randomized adaptive search procedures.” International Series in Operations Research & Management Science #57, pp. 219-249.

Sheffi, Y. and Powell, W. (1983), “Optimal signal settings over transportation networks,” Journal of Transportation, Engineering, Vol. 109, No. 6, pp. 824-839.

Smith, M.J. (1979a), “The existence, uniqueness and stability of traffic equilibria,” Transportation Research Vol. 13B, No. 4, pp. 295-304.

Smith, M.J. (1979b), “Traffic control and route choice; a simple example,” Transportation Research Vol. 13B, No. 4, pp. 289-294.

Smith, M.J. (1980), “A local traffic control policy which automatically maximises the overall travel capacity of an urban road network,” Traffic Engineering and Control, pp.298-302.

Smith, M.J. and Ghali, M, (1990), “The dynamics of traffic assignment and traffic control : a theoretical study,” Transportation Research, Vol. 24B, pp. 409-422.

Smith, M.J. and van Vuren, T. (1993), “Traffic equilibrium with responsive traffic control,” Transportation Science, Vol. 17, No. 2, pp. 118-132.

Stevanovic, A., Martin, P.T., and Stevanovic, J. (2007). “VISSIM-based genetic algorithm optimization of signal timings.” Transportation Research Record, No. 2035, Transportation Research Board, Washington, D.C., pp. 59-68.

Sun, W. and Mouskos, K.C. (2000), “Network-wide traffic responsive signal control in urban environments,” In: Proceedings of the 4th International Conference on Computational Intelligence and Neurosciences, Atlantic City, New Jersey.

Sun, D., Benekohal, R.F. and Waller, S.T. (2006), “Bi-level programming formulation and heuristic solution approach for dynamic traffic signal optimization,” Computer-Aided Civil and Infrastructure Engineering, Vol. 21, pp. 321-333.

Tan, H.N., Gershwin, S.B., and Athans, M. (1979), “Hybrid optimization in urban traffic networks,” Department of Transportation Report DOT-TSC-RSPA-79-7, 106 pages + appendices.

Teklu, F., Sumalee, A. and Watling, D. (2007), “A genetic algorithm approach for optimizing traffic control signals considering routing,” Computer-Aided Civil and Infrastructure Engineering Vol. 22 No. 1, pp. 31-43.

Tourism and Traffic Bureau of Kaohsiung County Government (2006), “The fundamental planning of light rail transportation (Yenchao line) in Kaohsiung urban area,” Technical Report.

U.S. DOT (2005), “Traffic control systems handbook,” FHWA-HOP-06-006, Washington, D.C. (http://ops.fhwa.dot.gov/publications/fhwahop06006/index.htm)

Varia H.R. and Dhingra S.L. (2004), “Dynamic optimal traffic assignment and signal time optimization using genetic algorithms,” Computer aided Civil and Infrastructure Engineering (CACAIE), Vol. 19, pp. 260-273.

Yang, H. and Bell, M.G.H. (1998), “Models and algorithms for road network design: a review and some new development,” Transportation Review, Vol. 18, No. 3, pp. 257-278.

Yang, H. and Lam, H.K. (1996), “Optimal road tolls under conditions of queuing and congestion,” Transportation Research, Vol. 30A, No. 5, pp. 319-332.

Yang, H. and Yagar, S. (1994), “Traffic assignment and traffic control in general freeway-arterial corridor systems,” Transportation Research, Vol. 28B, No. 6, pp. 463-486.

Yang, H. and Yagar, S. (1995), “Traffic assignment and signal control in saturated road networks,” Transportation Research, Vol. 29A, No. 2, pp. 125-139.

Yang, H., Yagar, S., Iida, Y. and Asakura, Y. (1994), “An algorithm for the inflow control problem on urban freeway networks with user-optimal flows,” Transportation Research, Vol. 28B, No. 2, pp. 123-139.

Yin, Y. (2000), “Genetic-algorithms-based approach for bi-level programming models,” Journal of Transportation Engineering, Vol. 126, No. 2, pp. 115-120.

Wardrop, J.G. (1952), “Some theoretical aspects of road traffic research,” In: Proceedings of Institute of Civil Engineering II(1), pp. 325-378.

Waller, S.T. (2000), “Optimization and control of stochastic dynamic transportation systems: formulations, solution methodologies, and computational experience,” Ph.D. Dissertation, Northwestern University.

Wong, S.C. and Yang, H. (1997), “Reserve capacity of a signal controlled road network,” Transportation Research, Vol. 31B, No. 5, pp. 397-402.

Wong, S.C.and Yang, H. (1999), “An iterative group-based signal optimization scheme for traffic equilibrium networks,” Journal of Advanced Transportation, Vol. 33, pp. 201-217.

Zhou, X.S. and Mahmassani, H.S.(2006), “Dynamic origin-destination demand estimation using automatic vehicle identification data,” IEEE Transactions on Intelligent Transportation Systems, Vol. 7, No. 1, pp. 105-114.

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