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研究生:張韶容
研究生(外文):Shao-JungChang
論文名稱:運輸走廊管理最佳化模式之建構
論文名稱(外文):An Optimal Control Model for Integrated Traffic Corridor Management
指導教授:胡大瀛胡大瀛引用關係
指導教授(外文):Ta-Yin Hu
學位類別:碩士
校院名稱:國立成功大學
系所名稱:交通管理學系碩博士班
學門:運輸服務學門
學類:運輸管理學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:84
中文關鍵詞:交通運輸走廊整合性交通管理與控制智慧型運輸系統
外文關鍵詞:Traffic CorridorIntegrated Traffic Management and ControlIntelligent Transportation Systems
相關次數:
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這幾年,隨著私人運具的成長,交通擁擠情形變成一個棘手的問題。尤其在都會區,上下班的尖峰時刻總會湧入過多的車輛,導致運輸走廊的擁擠嚴重;所謂的運輸走廊包括了一條高速公路主線、上下匝道、以及周遭的市區平面道路。因此,交管單位運用了多種不同的交通管理與控制的手段去增加道路的服務水準,較常見的包括:市區號誌設計、匝道儀控、路徑導引、高速公路可變速線控制。本研究目標為發展一個單純、且可以快速求解的整合型控制模式,去解決運輸走廊依時性的擁擠問題。
智慧型運輸系統(ITS)整合了運輸系統與多項先進技術,例如通訊、導航、控制…等等,以增加交通效率,改善交通線有問題,且ITS目標在於提供即時且適當的交通資訊給用路人,用路人在接收後能適時地針對即時的交通情形作不同的反應,同時提高交通的安全與效率。
在本研究中,結合了store-and-forward概念以及交通流特性,並同時考慮了上述所示的四項常見交通管理控制手段,建立出一整合性的交通控制模式,利用數學規劃的方式以求得最佳化的控制組合與參數(包含了號誌路口最佳綠燈時間、匝道儀控率、速限控制率),本模式目標為最小整體路網之延滯時間,並利用套裝軟體GAMS做求解,將所得結果帶入模擬指派軟體,DynaTAIWAN,進行績效的評估。
在數值實驗中,本研究選定高雄市市區運輸走廊進行探討,並依據不同的需求水準設計出三種情境分別做討論,並選定出最佳的控制組合,由所得結果得知,雖然實施交控均可以增加運輸走廊的交通效率,但是仍須依照不同的需求水準給予個別適合的控制手段,以使效用達到最大。
With the growth of private vehicles, serious traffic congestion has been a critical problem in recently year. In metropolitan area, traffic congestions are present on traffic corridors during peak hours. A traffic corridor includes freeway segments, ramps, and urban streets. Traffic control and management strategies are applied to enhance level of traffic service. The common strategies are signal design, ramp metering, route guidance and variable speed limits (VSLs). The aim in this research is to develop a simple integrated control model to solve the time-depended problem.
Intelligent Transportation Systems (ITS) integrate advanced techniques, such as telecommunication, navigation and control, with transportation systems, thus improve transportation efficiency. ITS aims to provide appropriate and real-time traffic information to drivers, and drivers can response to various traffic conditions in order to enhance traffic efficiency and safety.
In this research, an integrated control model is formulated based on the concept of store-and-forward and traffic flow characteristics, and the four common traffic management strategies are considered. The objective of mathematical model is to minimize total delay in traffic corridor. The model are solved through GAMS to obtain the optimal control settings, including effective green time, metering rates, control rates of VSLs. The performance of the proposed model is evaluated by simulation-assignment model, DynaTAIWAN.
In the numerical experiment, the model is implemented in Kaohsiung network. The results present that the influence of the various strategies is different in different cases, and the integrated control approaches can enhance traffic efficiency significantly. The advantage and disadvantage of the model are discussed and analyzed according to the numerical results.
CONTENTS
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND AND MOTIVATION 1
1.2 RESEARCH OBJECTIVES 3
1.3 RESEARCH FLOW CHART 3
1.4 OVERVIEW 6
CHAPTER 2 LITERATURE REVIEW 7
2.1 FREEWAY MANAGEMENT AND CONTROL 7
2.2 URBAN ARTERIAL MANAGEMENT 14
2.3 ROUTE GUIDANCE 16
2.4 TRAFFIC FLOW THEORY 17
2.5 TRAFFIC CORRIDOR MANAGEMENT AND SOLUTION METHOD 22
2.6 TRAFFIC SIMULATION AND ASSIGNMENT 25
2.7 SUMMARY 26
CHAPTER 3 RESEARCH METHODOLOGY 27
3.1 PROBLEM STATEMENT 27
3.2 RESEARCH FRAMEWORK 29
3.3 MODEL FORMULATION 31
3.4 TRAFFIC FLOW CHARACTERISTICS 35
3.5 MATHEMATICAL FORMULATION 36
3.6 SUMMARY 38
CHAPTER 4 IMPLEMENTATION AND PROGRAM DEVELOPMENT 40
4.1 NETWORK APPLICATION 40
4.2 PARAMETER ESTIMATION 48
4.3 EXPERIMENT DESIGN AND PROCESSES 50
4.4 BASIC EXPERIMENT 53
4.5 VARIABLE SPEED LIMIT EXPERIMENT 56
4.6 SUMMARY 59
CHAPTER 5 NUMERICAL EXPERIMENTS 60
5.1 EXPERIMENT SETUPS 60
5.2 EXPERIMENT RESULTS 62
5.3 RESULT ANALYSIS 68
5.4 SUMMARY 77
CHAPTER 6 CONCLUSIONS AND SUGGESTIONS 78
6.1 CONCLUSIONS 78
6.2 SUGGESTIONS 79
REFERENCE 81

LIST of FIGURES
FIGURE1.1 RESEARCH FLOW CHART 5
FIGURE 2.2(A) THE FUNDAMENTAL DIAGRAM WITH VSLS 13
FIGURE 2.2(B) THE FUNDAMENTAL DIAGRAM WITH VSLS 13
FIGURE 2.3 TYPICAL FREEWAY WORK ZONE 21
FIGURE 3.1 FOUR CONTROL STRATEGIES ON TRAFFIC CORRIDOR 28
FIGURE 3.2 RESEARCH STRUCTURE 30
FIGURE 4.1 THE TRAFFIC CORRIDOR NETWORK IN KAOHSIUNG 41
FIGURE 4.2 THE SKETCH MAP OF KAOHSIUNG TRAFFIC CORRIDOR 42
FIGURE 4.3 THE MULTI-LINEAR REGRESSION ANALYSIS 49
FIGURE 4.4 THE SIGNIFICANCE LEVEL OF PARAMETERS 49
FIGURE 4.5 THE VARIABLE DECLARATION IN GAMS 51
FIGURE 4.6 (A) THE CONSTRAINTS LISTS IN GAMS 51
FIGURE 4.6 (B) C++ PROGRAM LANGUAGES CODING 52
FIGURE 4.7 THE MODEL AND SOLVING OPTION IN GAMS 52
FIGURE 4.8 THE SOLVING OF THIS MODEL IN GAMS 53
FIGURE 4.9 THE CUMULATIVE THROUGHPUT OF NETWORK 58
FIGURE 4.10 THE CUMULATIVE THROUGHPUT OF FREEWAY MAINSTREAM 58
FIGURE 5.1 THE SENSITIVITY OF DEMAND 61
FIGURE 5.2 THE MOES OF AVERAGE TRAVEL TIME 74
FIGURE 5.3 THE MOES OF FREEWAY THROUGHPUT 75

LIST of TABLES
TABLE 2.1 THE METERING RATE UNDER DIFFERENT OCCUPANCIES 10
TABLE 4.1 THE TRAFFIC CHARACTERISTIC OF FREEWAY MAINSTREAM 43
TABLE 4.2 THE TRAFFIC CHARACTERISTIC OF RAMP REGION 44
TABLE 4.3 THE TRAFFIC CHARACTERISTIC OF URBAN STREETS 44
TABLE 4.4 PARAMETERS OF RAMP CONTROL MODEL (ALINEA) 46
TABLE 4.5 PARAMETERS OF THE SPEED-DENSITY RELATIONSHIP 46
TABLE 4.6 THE PARAMETERS OF SIGNAL IN URBAN INTERSECTION 47
TABLE 4.7 DEMANDS FOR THE BASE EXPERIMENT 53
TABLE 4.8 THE OPTIMAL GREEN TIME IN BASIC EXPERIMENT 54
TABLE 4.9 THE OPTIMAL RAMP METERING IN BASIC EXPERIMENT 55
TABLE 4.10 THE OPTIMAL VARIABLE SPEED LIMITS CONTROL RATES IN BASIC EXPERIMENT 55
TABLE 4.11 THE GREEN TIME IN BASE EXPERIMENT 55
TABLE 4.12 THE TRAVEL TIME AND STOPPED TIME IN DEFAULT SETTINGS 56
TABLE 4.13 THE TRAVEL TIME AND STOPPED TIME IN OPTIMAL CONTROL 56
TABLE 4.14 THE OPTIMAL VARIABLE SPEED LIMITS CONTROL RATES IN VSLS EXPERIMENT 57
TABLE 4.15 THE RESULTS OF SIMULATION WITH DEFAULT SETTINGS IN VSL EXPERIMENT 59
TABLE 4.16 THE RESULTS OF SIMULATION WITH VSL IN VSL EXPERIMENT 59
TABLE 5.1 THE O-D PAIR IN KAOHSIUNG NETWORK 60
TABLE 5.2 THE VALUE OF OBJECTIVE IN VARIOUS DEMAND LEVEL 62
TABLE 5.3 THE OPTIMAL GREEN TIME IN LOW DEMAND LEVEL 62
TABLE 5.4 THE OPTIMAL RAMP METERING IN LOW DEMAND LEVEL 63
TABLE 5.5 THE OPTIMAL CONTROL RATE (BI) IN LOW DEMAND LEVEL 64
TABLE 5.6 THE OPTIMAL GREEN TIME IN MEDIUM DEMAND LEVEL 64
TABLE 5.7 THE OPTIMAL RAMP METERING IN MEDIUM DEMAND LEVEL 65
TABLE 5.8 THE OPTIMAL CONTROL RATE (BI) IN MEDIUM DEMAND LEVEL 66
TABLE 5.9 THE OPTIMAL GREEN TIME IN HIGH DEMAND LEVEL 66
TABLE 5.10 THE OPTIMAL RAMP METERING IN HIGH DEMAND LEVEL 67
TABLE 5.11 THE OPTIMAL CONTROL RATE (BI) IN HIGH DEMAND LEVEL 68
TABLE 5.12 THE SCENARIOS FOR TRAFFIC CONTROL STRATEGIES 69
TABLE 5.13 RESULTS OF SIMULATION IN DEFAULT CONTROL OF LOW DEMAND LEVEL 70
TABLE 5.14 RESULTS OF SIMULATION IN LOW DEMAND LEVEL 70
TABLE 5.15 RESULTS OF SIMULATION IN DEFAULT CONTROL OF MEDIUM DEMAND LEVEL 71
TABLE 5.16 RESULTS OF SIMULATION IN MEDIUM DEMAND LEVEL 72
TABLE 5.17 RESULTS OF SIMULATION IN DEFAULT CONTROL OF HIGH DEMAND LEVEL 72
TABLE 5.18 RESULTS OF SIMULATION IN HIGH DEMAND LEVEL 73
TABLE 5.19 THE MOES IN LOW DEMAND LEVEL (%) 76
TABLE 5.20 THE MOES IN MEDIUM DEMAND LEVEL (%) 76
TABLE 5.21 THE MOES IN HIGH DEMAND LEVEL (%) 77
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