目前國內的交通設施無法隨車輛高成長率立即調整擴充，導致通勤在各大城市間的交通量於高速公路和主要幹道所組成之運輸走廊(Traffic Corridor)交通雍塞情況更加嚴重。因此運用先進科技提升運輸走廊的運行效率，已成為交通管理上重要課題之一。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用路人，提昇整體運輸系統的效率。
研究主要對整合型交通管理(Integrated Traffic Management, ITM)進行討論，透過不同交通管理策略如匝道儀控、交通號誌控制以及路徑導引等，紓緩由高速公路、上下匝道以及平行於高速公路之主要幹道和相鄰市區道路所組成之運輸走廊的交通擁擠情形。透過建置之線性規劃模式，進行不同之交通管理策略並使用數學規劃軟體CPLEX求取運輸走廊中目標函數總延滯時間(Total Delay Time)最小，根據上述結果得到最佳化之控制設定。
研究將建構兩組數學模式，數學模式一主要考慮匝道儀控以及交通號誌控制等交管策略；數學模式二除上述兩者並引入路徑導引。模式中之變數根據所定義之時間區段(Time Intervals)具有依時變化的性質，其中控制變數為各路段之車輛等候長度(Queue Length)以及定時號誌控制下之綠燈時比(Green Splits)。
首先透過數學模式一的概念建構兩不同地理性質虛擬運輸走廊進行多種需求量之實驗，討論其總延滯時間以及車輛等候長度大小及分配情形；引入路徑導引之數學模式二與數學模式一比較，根據實驗結果探討績效的差異及模式的優缺點；探討求解之最佳化整合型交通管理結果，透過數學模式初始解輸入至DynaTAIWAN進行實證分析和驗證，最後根據上述整合型交通管理模式的整體分析結果撰寫結論與建議。

Traffic congestion arises in big cities is common problems due to insufficient traffic facilities, and major bottlenecks include main arterials roads, freeways and traffic corridor. Intelligent Transportation Systems (ITS) integrate advanced techniques, such as telecommunication, navigation, 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 Integrated Traffic Management (ITM) to relieve possible congestion for traffic corridors through traffic control strategies. 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. The problem is formulated as linear programming models, and the objective function is to minimize total queue length for considered traffic corridors. Objective values for different control strategies are solved through CPLEX. Optimal control settings are obtained based on these calculations.
Two sets of models are formulated. The first model considers ramp metering and traffic signal, and the second model includes route guidance constraints. The control variables include queue length for each roadway segment, and green splits for each pre-timed signal. Variable are time-dependent according to definition of time intervals.
Numerical experiments are conducted for two traffic corridors to illustrate the proposed models. Total delay time and queue length distribution are used as performance indexes. Numerical results are explored and discussed to study the advantages and disadvantages of the models. In order to verify the results, traffic control strategies are used in DynaTAIWAN to simulate traffic flow distributions.

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