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研究生:鄭卓群
研究生(外文):Cheng, Cho-Chun
論文名稱:應用依時擁擠定價模式於運輸走廊最佳化之研究
論文名稱(外文):A Time-Varying Congestion Pricing Model for Optimization of Transportation Corridor
指導教授:張學孔張學孔引用關係
指導教授(外文):Chang, Shyue-Koong
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:99
中文關鍵詞:運輸需求管理依時擁擠定價最適控制
外文關鍵詞:Transportation Demand ManagementTime-varying congestion tollOptimal control
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近年來,包括彈性工作時間制度、共乘制度或是擁擠定價等運輸需求管理策略,強調運輸主管機關應從時間或空間的層面來調節並舒緩尖峰需求,以解決交通擁擠問題,而非一昧的提高道路供給以滿足不斷成長的私人運具使用率。
本研究係應用最適控制理論構建出一固定需求限制下之依時擁擠定價模式,透過對通勤旅次選擇行為之調整(包括出發時間、運具、路徑),使都會區聯外運輸走廊之績效達到最佳之狀態。同時藉由不同情境之設定,以探討不同運輸需求管理策略的實施,對於通勤者旅次選擇行為與運輸系統整體績效的影響。
本研究中所應用之系統動態邊際成本的定義,係藉由模式最佳化條件之推導以及其對應經濟乘數之詮釋而得。此外透過外部性的分析,分別定義出靜態之擁擠外部性及動態之擁擠外部性,以推演出系統最佳流量型態下所須之依時擁擠費率型態。藉由網路分析中的系統最佳下之使用者均衡的概念,本研究建立了一套包括出發時間、運具與路徑的最佳化演算機制,以求解最佳的系統績效。
透過數值分析的比較可以發現,使用共乘的通勤者所需付出的擁擠費率較一般通勤者所需付出的費率低廉許多,因此擁擠定價除了可以有效的促使通勤者轉換其運具使用習慣,同時也可被視為是一鼓勵共乘之配套措施。而對於彈性工時政策的分析中則顯示,藉由最佳出發型態之尖峰舒緩的情形與出發時間擴散的情形可以發現,彈性工時的實施的確對於通勤旅次之出發時間有相當顯著的影響。而在高承載專用道的設置方面,即使相關的分析指出,高承載專用道的設置的確可以有效的提高系統容量並減低通勤旅次成本,但其設置之效益則會產生邊際效益遞減的情形。模式敏感度分析則顯示,在路網需求量過大時,路網績效將會對於系統參數的選取十分敏感,但在一般情況下,路網績效與系統參數間之關係則呈現相當穩定的情形。
The traditional approach to relieving or eliminating transportation system congestion has been to provide the spatial organization of capacity needed to satisfy travel demand. Recently, Transportation Demand Management (TDM) strategies, such as flexible work hours, carpool incentives, congestion pricing, indicate that travel demand should be viewed as a quantity whose size and distribution can be carefully controlled. From a modeling perspective, the most important aspect of these individual or combined policies of TDM implementing is the need to model the dynamics of traffic congestion in order to evaluate the impacts of these policy combinations.
In this work, a time-varying congestion pricing model with alternative mode, departure time, and route choices of fixed commuting demand are formulated as an optimal control problem. Through the derivation of optimality of the proposed model, the dynamic marginal travel costs can then be defined through the model multipliers’ economic meanings.
It is shown that a user equilibrium flow pattern can be shifted toward a system optimal flow pattern by imposing the congestion tolls equal to the externalities. The congestion externalities, which can be seen as the major effect of inefficient traffic allocations, are derived analytically, including the dynamic externality and the static externality. Furthermore, a time-varying congestion toll, which is equal to the congestion externalities, is determined to ensure the users’ optimal private choices can also be the system optimal choices. A solution algorithm employing augmented Lagrangian method in conjunction with gradient method is applied in this study, and a double set of iterations’ algorithm is revised to handle simultaneous departure time/route/mode choices.
Through the demonstrations of numerical examples, even though all of the links in the test network have to be charged with the congestion toll when a traffic jam has formed, we found that the congestion toll for each HOV user is much lower. So the congestion pricing policy may be seen as another incentive of the commuters for transferring their current modes to the higher occupancy modes. Through the scenario analyses, we can conclude that the flexible work hours policy has significant impact on commuting system performances. System users will shift their departure time to avoid traffic jams more efficiently through the application of a flexible work hours policy. Moreover, when the commuting demand remains the same, the marginal benefit of providing extra HOV lanes will decrease.
By applying the sensitivity analyses, the result appears to be that the system optimal flow pattern is not sensitive to the value of travel time, unless a serious congestion delay caused by oversized traffic demand has occurred. And the commuters can always accept a certain range of delay during their journeys, but once this certain range of delay is overtaken, the average travel cost will increase instantaneously. In addition, the tendency of operation cost parameter shows the advantage of sharing the operation costs can be greater only when demand is large enough.
TABLE OF CONTENTS
List of Tables viii
List of Figures ix
Chapter 1 Introduction 1-1
1.1 Problem Statement 1-1
1.2 Objectives and Scope 1-3
1.3 Technical Approach 1-4
1.4 Study Content and Flowchart 1-5
1.5 Organization of Thesis 1-7
Chapter 2 Literature Review 2-1
2.1 Flexible Work Hours 2-1
2.1.1 Theoretical Studies 2-1
2.1.2 Implementation Examples 2-3
2.2 Carpooling Incentive by Means of The High Occupancy Vehicle Lane 2-5
2.2.1 Theoretical Studies 2-6
2.2.2 Implementation Examples 2-7
2.3 Congestion Pricing 2-8
2.3.1 Static Congestion Pricing 2-9
2.3.2 Dynamic Congestion Pricing 2-11
Chapter 3 Model Formulation And Derivation 3-1
3.1 System Assumptions 3-1
3.2 Key Notations 3-2
3.3 System Descriptions 3-5
3.3.1 Flow Propagation 3-5
3.3.2 Demand Function 3-7
3.3.3 Nodal Flow Conservation 3-8
3.3.4 Cost Functions 3-8
3.4 Objective Function and Constraints 3-12
3.5 Model Derivation 3-14
3.6 Optimality Condition 3-16
Chapter 4 Externalities Analysis 4-1
4.1 Economic Interpretations of Multipliers 4-1
4.2 System Optimum and User Equilibrium Analysis 4-4
4.2.1 Single O-D Case 4-4
4.2.2 General Network Case 4-8
4.3 Externalities Analysis 4-11
4.4 Optimal Congestion Toll 4-13
Chapter 5 Model Solution Algorithm 5-1
5.1 Discrete Time Varying Congestion Pricing Model 5-1
5.2 Solution Algorithm 5-3
Chapter 6 Numerical Analysis 6-1
6.1 Numerical Presentations 6-1
6.1.1 Single O-D Case 6-2
6.1.2 Multiple O-D Case 6-8
6.2 Scenario Analysis 6-13
6.2.1 System Performance Under Different Demand 6-13
6.2.2 System Performance Under Different Flexible Interval 6-16
6.2.3 System Performance Under Different HOV Lane Provided 6-18
6.2.4 System Performance Under Different Occupancy Requirement of HOV 6-21
6.2.5 Traffic Volume Share of Each Link 6-22
6.3 Sensitivity Analysis 6-25
6.3.1 Parameter of Travel Time Value 6-25
6.3.2 Parameter of Early or Late Arrival Penalty & 6-27
6.3.3 Parameter of Operation Cost 6-27
6.3.4 Parameter of Carpool Disutility Parameter 6-30
Chapter 7 Conclusions and Recommendations 7-1
7.1 Conclusions 7-1
7.2 Recommendations for Further Researches 7-4
References R-1
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