成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	張韶容 Chang, Shao-Jung
論文名稱：	運輸走廊管理最佳化模式之建構 An Optimal Control Model for Integrated Traffic Corridor Management
指導教授：	胡大瀛 Hu, Ta-Yin
學位類別：	碩士 Master
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：	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 Corridor, Integrated Traffic Management and Control, Intelligent Transportation Systems
相關次數：	點閱：161 下載：6
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

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
                                    

1. Bae, C.H., Cho, K.Y., and Ji, S.H. (2009),” Multi-agent based traffic simulation and integrated control of freeway corridors: Part 1 simulation and control model,” Journal of Mechanical Science and Technology, Vol.23, No.5, P.P. 1365-1373.
2. Bae, C.H., Cho, K.Y., and Ji, S.H. (2009),” Multi-agent based traffic simulation and integrated control of freeway corridors: Part 2 integrated control optimization,” Journal of Mechanical Science and Technology, Vol.23, No.5, P.P. 1374-1382.
3. Carlson, R.C., Papamichail, I., and Papageorgiou, M. (2010),” Optimal Motorway Traffic Flow Control Involving Variable Speed Limits and Ramp Metering,” Transportation Science, Vol.44, No.2, P.P.238-253
4. Gazis, D.C. (2002), Traffic Theory, Kluwer Academic Publishers, pp. 165-171
5. Chen, H.K., Chiou, Y.C., and Chu, C.U. (2010), Traffic Engineering, Wu-Nan Publisher.
6. FHWA (2005), Traffic Control Systems Handbook, FHWA-HOP-06-006, Federal Highway Administration, Department of Transportation, Washington, D.C.
7. FHWA (2008),Traffic Signal Timing Manual, FHWA-HOP-08-024 Federal Highway Administration, Department of Transportation, Washington, D.C.
8. Website of Google Map, Google Inc , https://maps.google.com.tw/
9. HCM (2011), Highway Capacity Manual in Taiwan, Chinese Institute of Transportation, Taiwan.
10. Hsueh, P.Y. (2011), ”A Study on Optimal Freeway Control of Integrating Variable Speed Limit and Ramp Metering,” Master dissertation, Department of Civil Engineering College of Engineering, National Taiwan University, Taiwan, ROC.
11. Hu, T.Y., Chen, L.W., Hung, P.H., Huan,g Y.K. and Chiang, M.L. (2005), “A New Simulation-Assignment Model DynaTAIWAN for Mixed Traffic Flows,” Proceedings of the 12th World Congress on Intelligent Transportation Systems, San Francisco, U.S..
12. Hu, T.Y. and Wu, T.Y (2011),”An optimal control model on traffic corridors with route guidance,” Transportation Planning Journal, Vol.40, No.1, P.P.31-62.
13. Institute of Transportation, Ministry of Transportation and Communications, (2005), ”Demonstration project of Regional Intelligent Transportation System: Traffic Analysis and Prediction Systems (II).”
14. Kang, K.P., Chang, G.L., and Zou, N (2004),” Optimal dynamic speed-limit control for highway work zone operations,” Transportation Research Record, No.1977, P.P.77-84.
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., and Kosmatopoulos, E. (2009),” Store-and – forward based methods for the signal control problem in large-scale congested urban road networks,” Transportation Research Part C-Emerging Technologies, Vol. 17, No.2 P.P.163-174.
17. NCHRP (2010), Highway Capacity Manual, Transportation Research Board, National Cooperative Highway Research Program, Washington, D.C.
18. Papageorgiou, M. (1995), “An Integrated Control Approach for Traffic Corridors,” Transportation Research Part C-Emerging Technologies, Vol.3, No.1, P.P.19-30.
19. Papageorgiou, M., Hadj-Salem, H. and Middelham, F. (1997),”ALINEA Local Ramp Metering: Summary of Field Results,” Transportation Research Record, Vol.1603, P.P. 90-98.
20. Papageorgiou, M. and Kotsialos, A. (2002),” A Freeway ramp metering: An overview,” IEEE Transactions on Intelligent Transportation Systems, Vol.3, No.4, P.P. 271-281.
21. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., and Wang, Y. (2003), “Review of road traffic control strategies,” Proceedings of the IEEE, Vol.91, No.12, P.P. 2043-2067
22. Papageorgiou, M., Kosmatopoulos, E., and Papamichail, I.(2008).” Effects of Variable Speed Limits on Motorway Traffic Flow,” Transportation Research Record, No. 2047, P.P.37-48
23. Website of Taiwan Area National Freeway Bureau, Ministry of Transportation and Communications, http://www.freeway.gov.tw.
24. Transportation Bureau, Kaohsiung City Government (2009), “The report of survey of the travel characteristics of Kaohsiung metropolitan area.”
25. Wu, J.J, Sun, H.J, and Gao, Z.J. (2007), “Effects of route guidance systems on small-world networks,” International Journal of Modern Physics C, Vol.8, No.8, P.P. 1243-1250.
26. Wu, T.Y. (2010),”A Study on Intelligent Corridor Management Model,” Master dissertation, Department of Transportation and Communication Management Science, National Cheng Kung University, Taiwan, ROC.
27. Zhang, H.M., Ma, J., Nie, Y. (2009),” Local Synchronization Control Scheme for Congested Interchange Areas in Freeway Corridor,” Transportation Research Record, No.2128 P.P.173-183.
28. Kotsialos, A. and Papageorgiou, M. (2004),” Motorway network traffic control systems,” European Journal of Operational Research, Vol.152, No.2, P.P.321-333.

校內：2013-08-21公開
校外：2014-08-21公開

簡易檢索 / 詳目顯示

相關論文