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研究生： 劉瀚聰
Liou, Han-Tsung
論文名稱： 交通路網車輛偵測器佈設策略與旅次起迄量推估之整合架構
An Integrated Framework for the Determination of Vehicle Sensor Deployment Strategy and Vehicular Trip Origin-Destination Matrix
指導教授： 胡守任
Hu, Shou-Ren
學位類別： 博士
Doctor
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： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 152
中文關鍵詞： 旅次起迄矩陣 、智慧型運輸系統 、異質性交通偵測器 、偵測器佈設問題
外文關鍵詞： origin-destination (O-D) matrix, intelligent transportation systems (ITS), heterogeneous traffic sensors, sensor deployment problem
相關次數： 點閱：111  下載：7 
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    • 運輸路網的旅次起迄(Origin-Destination, O-D)需求量(以下簡稱O-D旅次量)是運輸科學的重要元素之一，以運輸需求為基礎所構成的O-D旅次量矩陣可以描述車輛在特定運輸路網的時空分佈，同時反映路網的負荷情況。在過去四十年的相關研究主要利用易收集的路段流量或路徑流量估計法(Path Flow Estimator, PFE)，以進行運輸路網O-D旅次量之估計。該做法可以有效克服傳統O-D旅次量調查方法所衍生的缺點，包括：耗時、費力、抽樣誤差等問題，因而廣泛且持續受到重視與研究。此外，由於資訊和通訊技術(Information Communication Technologies, ICTs)的快速發展，相關技術應用於智慧型運輸系統(Intelligent Transportation Systems, ITS)的各項工作日漸普遍。因此，路段流量不再是推估O-D旅次量的唯一資料來源，透過若干先進式車輛偵測設施的研發與應用，以異質性交通資料於運輸路網O-D旅次需求量的推估，不僅逐漸具成本效益，同時可以有效改善O-D旅次量的估計結果。
    然而，實務上運輸路網管理單位在考量交通偵測器佈設計畫時，通常面臨預算限制的問題，因而無法全面性的進行車輛偵測器的佈設。因此，如何策略性的決定異質性車輛偵測器佈設計畫，並有效的結合應用於運輸路網O-D旅次量之推估，為一重要的研究課題。本研究主要的目的在於研擬一整合型決策模式架構，包括：異質性偵測器佈設模式與運輸路網O-D旅次量推估模式，在有限的車輛偵測器預算限制之下，求解異質性偵測器佈設策略與對應的O-D旅次量矩陣之估計量。本研究所提出的整合型架構，對於實務上進行運輸路網管理與偵測設施佈設決策，具重要的參考價值與政策意涵。

    A trip origin-destination (O-D) matrix in a vehicular network is one of the important components in network sciences, and this information provides vehicular spatiotemporal patterns to describe traffic loading situations in a given network. In the past four decades, estimating network O-D matrices from relatively easily collected link flows and path flows has been developed and studied to overcome the problems associated with traditional network O-D matrix survey approaches, including time consuming, labor intensive and sampling errors in the survey process. Because of the rapid development of information and communication technologies (ICTs), advanced technologies in sensor surveillance have been widely used in intelligent transportation systems (ITS) related applications. Hence, traffic information obtained from heterogeneous sensors including link and path flows as input data for the network O-D matrix estimation gradually becomes cost-effective and has its potential for improving estimation performance.
    However, in practice, highway management agencies always face a budgetary constraint issue related to implementing a sensor deployment plan, and a full-scale sensor deployment strategy is usually not available. As a result, how to strategically deploy heterogeneous traffic sensors for the purpose of vehicular tip O-D matrix estimation becomes essential in transportation network sciences. The main purpose of this research is to develop an integrated model framework consisting of a heterogeneous sensor deployment strategy and vehicular trip O-D matrix estimation under a budgetary constraint. The proposed integrated framework can efficiently provide desirable solutions in terms of an acceptable level of accuracy of network O-D estimated matrix estimate and has an implication for strategic sensor deployment strategy.

    誌謝 I 摘要 III ABSTRACT IV TABLE OF CONTENTS V LIST OF TABLES VIII LIST OF FIGURES IX CHAPTER 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Research Scope 3 1.3 Research Objective 4 1.4 Methodology 5 1.5 Outline 5 CHAPTER 2 LITERATURE REVIEW 7 2.1 Network O-D Matrix Estimation 7 2.1.1 Generalized least squares estimator (GLS) 9 2.1.2 Maximum Likelihood (ML) Estimator 10 2.1.3 Entropy Maximizing (EM) Estimator 11 2.1.4 Information Minimization (IM) Estimator 13 2.1.5 The Kalman Filter (K-F) Algorithm 15 2.1.6 The Bayesian Inference Approach 16 2.1.7 Other Related Network O-D Matrix Estimation Methods 17 2.1.8 Network O-D matrix Estimation from Different Resources 17 2.2 Intersection Turning Proportions Estimation 18 2.3 Path Flow Estimation (PFE) 20 2.4 Network Sensor Location Problem (NSLP) 21 2.4.1 Graph Theory for the NSLP 21 2.4.2 Algebraic Approach for the NSLP 22 2.4.3 Mathematical Optimization Approach for the NSLP 24 2.5 NSLP-OD/HSDP-OD Problems 29 2.5 Traffic Sensor Technologies 43 2.5.1 Sensor Types for Traffic Data Collection 43 2.5.2 Location of Traffic Sensors 46 2.5.3 Measurement of Traffic Sensors 46 2.5.4 Classification of Traffic Sensors 46 2.5.5 Summary of Traffic Sensor Technologies 47 2.6 Summary of the Literature Review 50 CHAPTER 3 MODEL FORMULATION 54 3.1 Notations 55 3.2 Sequential HSDP-OD model 57 3.2.1 HSDP Model Formulation in the Sequential Framework 60 3.2.2 Information Transformation Layer in the Sequential Framework 73 3.2.3 O-D Matrix Estimation Model Formulation in the Sequential Framework 74 3.2.4 Model Performance Measures 76 3.2.5 Solution Procedure for the Sequential HSDP-OD model 77 3.3 Two-stage HSDP-OD model 79 3.3.1 HSDP Model Formulation in the Two-stage Framework 82 3.3.2 O-D Matrix Estimation Model Formulation in the Two-stage Framework 86 3.3.3 Feedback Mechanism of Iterative Update 88 3.3.4 Solution Procedure for the Two-stage HSDP-OD model 90 3.4 Summary of the HSDP-OD Models 94 CHAPTER 4 NUMERICAL ANALYSIS 96 4.1 Numerical Analysis for the Sequential HSDP-OD Model 96 4.1.1 Hypothetical Network: Fishbone Network 97 4.1.2 Simplified Real Network: National Cheng Kung University (NCKU) Network 100 4.2 Interactive Effect between HSDP and O-D Models 107 4.2.1 Fishbone network without budget constraints in different installed locations 108 4.2.2 Fishbone network with budget constraints in different installed locations 110 4.2.3 Discussions 111 4.3 Numerical Analysis for the Two-stage HSDP-OD Model 112 4.3.1 Hypothetical Network: Fishbone Network 112 4.3.2 Simplified Real Network: Sanmin Network 115 4.3.3 Discussion of the Two-stage HSDP-OD Model 119 4.3.4 Comparisons Between Sequential and Two-stage HSDP-OD Models 121 CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 124 5.1 Conclusions 124 5.1.1 Conclusions about the Integrated Framework for the HSDP-OD Problem 124 5.1.2 Conclusions about the HSDP Models 125 5.1.3 Conclusions about the O-D Matrix Estimation Models 126 5.2 Findings of the Research 126 5.3 Limitations of the Research 129 5.4 Recommendations 129 LIST OF REFERENCES 131 APPENDICES 141 Appendix A: Upper Bound of the Number of Traffic Sensors 141 A.1 Upper Bound of the Equipped Passive-type Sensors 141 A.2 Upper Bound of the Equipped Active-type Sensors 143 Appendix B: Bound of the HSDP Model 145 B.1 Lower Bound of the Required Sensors from the Set Covering Constraint 145 B.2 Upper Bound of the Available Sensors from the Budget Constraint 146 VITA 148

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