運輸走廊為一條帶狀的區域，是區域中交通的主要幹道，包括高速公路主線、上下匝道及市區道路，而由於車輛快速的成長，尤其在尖峰時段，運輸走廊上常出現重現性的壅塞情形，且車輛容易回堵至附近市區道路，甚至影響到高速公路主線上的車輛運行，因此車輛壅塞問題不僅是對於單一市區道路，影響的範圍觸及市區道路和高速公路。
智慧型運輸系統(Intelligent Transportation Systems, ITS)以通信、電子、導航以及控制等先進技術加以整合，其目的是將車輛和道路的即時資訊，透過適當的方式傳遞给用路人，提昇整體運輸系統的效率。在智慧型運輸走廊管理中，利用偵測器及相關資料，藉由匝道管理、路口號誌控制及路徑導引等交通控制策略可管理運輸走廊中的車流情形，以達到導引路網中車流行駛最佳路徑，並紓緩道路擁擠情形，以提升運輸效率及安全。
本研究主要藉由智慧型交通管理(Intelligent Traffic Management, ITM)紓解運輸走廊中產生的車輛擁擠情形，根據Store-and-Forward概念，並整合本土車流特性，建構線性最佳化控制模式，其控制變數包括綠燈時間、匝道儀控率及各路段車輛停等長度。最佳化使用數學規劃軟體CPLEX求取最佳解，進行不同交通管理策略之分析，並求取運輸走廊中目標函數總延滯時間(Total Delay)最小化。因此該最佳化控制模型優點為可做離線型運輸走廊控制策略的評估，且可為線上型交通控制策略的擬定參考。
首先將針對虛擬走廊進行多種需求量之實驗，求解其總延滯時間以及車輛等候長度大小及分配情形。此外，將最佳化控制模型套用至實際桃園路網進行實驗求解，以觀察最佳化控制模型於實際路網的應用。最後將最佳化控制模型最佳解輸入至DynaTAIWAN進行實證分析和驗證。最後根據上述智慧型交通管理模式的整體分析結果討論結論與建議。

Traffic congestion arises in metropolis are common problems due to insufficient traffic facilities and major bottlenecks including main arterials roads, freeways and traffic corridor. A traffic corridor basically includes freeway segments, on-ramps, off-ramps and arterial streets. Possible traffic control strategies include ramp metering, traffic signal, and route guidance.
Intelligent Transportation Systems (ITS) integrate advanced techniques, such as telecommunication, navigation and control, with transportation systems, thus enhance transportation efficiency. The major purpose of ITS is to provide appropriate traffic information and/or route guidance to travelers, and travelers can respond to traffic conditions quickly.
This research focuses on Intelligent Traffic Management (ITM) to relieve possible congestion on traffic corridors through traffic control strategies. Based on Papageorgiou(1995) and the concept of the store-and-forward method (Gazis, 2002), the problem is formulated as linear optimal control models, and the objective function is to minimize total queue length for considered traffic corridors. Objective values for different control strategies are solved by CPLEX, and the optimal control settings are obtained based on these calculations. The advantage of optimal linear control model can be used to evaluate the offline control strategy and online control.
Numerical experiments are conducted to illustrate the proposed models. Total delay and queue length distribution are used as performance indexes. Numerical results are explored and discussed to study the advantages and disadvantages of the model. In order to verify the results, traffic control strategies are used in DynaTAIWAN to simulate traffic flow distributions.

1. 交通部(2004)，智慧型運輸走廊路況動態即時資訊系統之開發與建置(一)-台北都會區至中正機場智慧運輸走廊交通資訊與控制示範系統建置。
2. 交通部台灣區國道高速公路局(2003)，高快速公路整體路網交通管理系統綜合規劃成果報告。
3. 交通部運輸研究所(2001)，台灣地區公路容量手冊。
4. 交通部運輸研究所(2005)，區域智慧型運輸系統示範計畫－核心交通分析與預測系統(第二年期)。
5. 江立仁(2008)，整合型運輸走廊交通管理模式之研究，成功大學交通管理科學系碩士論文。
6. 彭柏凱(2007)，應用機率型動態規劃構建動態路徑導引之研究，淡江大學運輸管理科學系碩士論文。
7. 鼎漢國際工程顧問股份有限公司(2005)，桃園整體運輸規劃案。
8. Aboudolas, K., Papageorgiou, M., and Kosmatopoulos, E. (2009), “Store-and-forward based methods for the signal control problem in large-scale congested urban road networks,” Transportation Research C, Vol. 17, No. 2, pp. 164-174.
9. Department of Transportation - Research Innovative Technology Administration, U.S., http://www.its.dot.gov/icms/.
10. Diakaki, C., Papageorgiou, M., and Mclean, T. (1997), “Simulation studies of integrated corridor control in Glasgow,” Transportation Research C, Vol. 5, No. 3, pp. 211–224.
11. FHWA (1996), Traffic Control Systems Handbook, FHWA-SA-95-032, Federal Highway Administration, Department of Transportation, Washington, D.C.
12. FHWA (2006), Ramp Management and Control Handbook, FHWA-HOP-06-001, Federal Highway Administration, Department of Transportation, Washington, D.C.
13. Gazis, D. C. (2002), Traffic Theory, Kluwer Academic Publishers, pp. 165-168.
14. Haj-Salem, H., and Papageorgiou, M. (1995), “Ramp metering impact on urban corridor traffic: field results,” Transportation Research A, Vol. 29A, No. 4, pp. 303–319.
15. Kotsialo, A., and Papageorgiou, M. (2004), “Motorway network traffic control systems,” European Journal of Operational Research, Vol. 152, No. 2, pp. 321-333.
16. Kotsialos, A., Papageorgiou, M., Mangeas, M., and Haj-Salem, H. (2002), “Coordinated and integrated control of motorway networks via non-linear optimal control,” Transportation Research C, Vol. 10, No. 1, pp. 65-84.
17. Mehta, M. (2001), Design and implementation of an interface for the integration of DynaMIT with the traffic management center, Department of Civil and Environmental Engineering, Massachusetts Institute of Technology.
18. Michalopoulos, P., and Stephanopoulos, G. (1977a), “Oversaturated signal system with queue length constraints: single intersection,” Transportation Research, Vol. 11, No. 6, pp. 413–422.
19. Michalopoulos, P., and Stephanopoulos, G. (1977b), “Oversaturated signal system with queue length constraints: systems of intersections,” Transportation Research, Vol. 11, No. 6, pp. 423–428.
20. Moreno-Banos, J. C., Papageorgiou, M., and Schaffnet, C. (1993), “Integrated optimal flow control in traffic networks,” European Journal of Operational Research, Vol. 71, No. 2, pp. 317-323.
21. Papageorgiou, M. (1995), “An integrated control approach for traffic corridors, Transportation Research C, Vol. 3, No. 1, pp. 19–30.
22. Papageorgiou, M., Haj-Salem, H., and Blosseville, J. M. (1991), “ALINEA: A local feedback control law for on-ramp metering,” Transportation Research Record, Vol. 1320, pp. 58-64.
23. Papamichail, I., Kotsialos, A., Margonis, I., and Papageorgiou, M. (2008), “Coordinated ramp metering for freeway networks – A model predictive hierarchical control approach,” Transportation Research C, in press.
24. Rathi, A. K. (1988), “A control scheme for high traffic density sectors,” Transportation Research B, Vol. 22, No. 2, pp. 81-101.