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研究生:蕭偉政
研究生(外文):Wei-Cheng Hsiao
論文名稱:行動電話網路資料為基礎的動態交通資訊系統之研究
論文名稱(外文):Dynamic Traffic Information System Based on Cellular Network Data
指導教授:張學孔張學孔引用關係
指導教授(外文):Shyue-Koong Chang
學位類別:博士
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
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:129
中文關鍵詞:交通資訊智慧運輸系統行動電話普及率定位更新廣播呼叫
外文關鍵詞:Traffic InformationIntelligent Transportation SystemsMobile Phone Penetration RateLocation UpdatePaging
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以行動電話網路資料為基礎的交通資訊系統,通常倚賴行動電話回傳之網路資料據以推估行動電話精確位置,並進而分析交通相關資訊。然而,行動電話位置推估不準確以及行動電話網路資料樣本數不充足,一直是行動電話交通資訊系統無法實際運用的主要兩大原因。
就行動電話位置推估不準確議題而言,傳統的交通資訊,例如行駛速率,是利用前後不同時間點之車輛行駛距離所計算出來。然而,由於行動電話定位誤差會產生車輛預測位置變動或波動情形,進而導致計算出來的交通資訊不穩定。考量最新的行動電話定位技術發展之後,本研究提出一種新的分析方法,該方法以「區段」(Segment)為基礎的交通資訊計算方法。經由模擬分析結果證實,此方法在行動電話定位誤差範圍小於區段長度時,比傳統的距離計算方法更接近實際交通狀況。除了過飽和交通狀況以及行動電話定位誤差相當大的狀況以外,以區段為基礎的交通資訊計算方法,在其他各種交通狀況下,都比傳統的計算方法更準確。最後,透過模擬分析可知,足夠的樣本數是影響交通資訊正確與否的重要關鍵,而車流量、資料收集頻率、定位更新頻率與行動電話普及率等因素,則會影響樣本數的多寡,因此交通資訊系統設計時首要考量是否具備這些條件。
就行動電話網路資料樣本數不充足議題而言,除了通話中與開關機過程,行動電話回傳網路資料的時機主要是在「位置更新」(Location Update)時進行。行動電話位置更新的目的是在確保發話方可以迅速取得受話方所在的基地台位置,進而連通電話。位置更新的頻率越高則交通資訊樣本來源越充足,交通資訊服務品質愈佳。同時因為位置不斷即時更新,故「來話呼叫」(Paging)時可更迅速取得受話方所在的基地台位置,但相對地也越加損耗行動電話網路系統資源,反而影響電信服務品質。因此,如何在兼顧電信營運成本與交通資訊服務品質之考量下決定最佳的位置更新策略,成為一個值得探討的課題。本文採用解析性方法建構位置更新與來話呼叫之總成本最低目標下,最佳週期與最佳距離位置更新模式,具有操作應用方便之優點,交通資訊系統可依據交通環境變化即時計算取得最佳更新週期,亦可針對特定參數進行敏感度分析,因此具有實用性價值。並針對各種移動速度下之交通情境,分析不同行動電話位置更新策略之總成本。結果顯示出最佳位置更新週期與旅行速度之三分之二方根成反比關係,而最佳位置更新距離與旅行速度之三分之一方根成正比關係;因此,隨著旅行速度降低時應動態提高位置更新週期。電信營運廠商與交通資訊提供商可利用本研究發展出的最佳化求解公式以及是否達到最佳化目標之判斷方法,在兼顧交通資訊以及電信服務品質情況下,分析求算合理的收付費機制。
Traffic information system based on mobile phone location technology relies on cellular network data reported by mobile phones to determine accurate location and generate traffic information. However, inaccurate location and insufficient cellular network data become the two major barriers for implementation.
For the first issue, in traditional traffic information estimation method, the difference in positions is used to estimate traffic information. However, variation or flutter of positioning caused by imprecise mobile phone location accuracy may lead to the unstable measurement. Take the latest mobile phone location positioning technology into consideration, this dissertation introduces a new segment-based traffic information estimation method. It is proved by simulation approach in this research that segment-based method performs better than traditional distance-based method when location error ranges within the length of a segment. Simulation results also demonstrate that segment-based method is better than traditional one under all kinds of traffic condition except for the conditions of high location error and over saturation. Through simulation analysis, we conclude that enough sample size, i.e. larger vehicle generation rate, longer data collection interval, shorter location update interval, and larger mobile penetration rate are crucial factors to generate accurate traffic information.
For the second issue, mobile phone reports cellular network data to the mobile switching center when it is necessary to update the location when mobile phone is switched on, active or update-timer expired during idle. The purpose of Location Update is to ensure each call can be delivered to an exact destination on time and efficiently. The higher the update frequency is, the faster each call can be connected and therefore, more traffic data can be obtained to generate reliable traffic information. However, frequent updates increase network load and decrease service level. Therefore, there is a trade-off between operation cost and traffic information quality for the purpose of generating the best update strategy. This research develops analytical models to analyze the optimal Location Update strategy with the objective of minimum total cost. The optimal analytical results and numerical analysis results show that the optimal timer (t) is inversely proportional to cube root of square speed (S); moreover, the optimal distance (m) is proportional to cube root of travel speed (S). When traffic condition becomes worse, it’s suggested to increase the update timer. Finally, a maneuverable model and rule of optimality developed by this study can be used as a price evaluation tool for both telecom operators and traffic information providers.
TABLE OF CONTENTS
ABSTRACT i
TABLE OF CONTENTS vi
LIST OF FIGURES ix
LIST OF TABLES xii
Chapter 1 Introduction 1
1.1 Problem Statement 1
1.2 Objectives and Scope 3
1.3 Methodology 4
1.4 Research Tasks 5
1.5 Organization of the Dissertation 8
Chapter 2 Underlying System Design 9
2.1 State of the Mobile Phone Location Positioning Technology 9
2.2 New Generation MPL-based Traffic Information System Architecture 13
2.3 Operational Process of MPL-based System 16
2.4 Summary 23
Chapter 3 Literature Reviews 24
3.1 MPL-based System 24
3.1.1 Simulation studies 25
3.1.2 Field operational tests 27
3.2 Location Positioning Technology 32
3.3 Location Update Technology 40
3.4 Summary 46
Chapter 4 Modeling Segment-based Method 47
4.1 Traffic Information Definition 47
4.2 Segment-based Model 49
4.3 Simulation 52
4.4 Location Accuracy Impact Analysis 58
4.5 Segment Length Impact Analysis 62
4.6 Mobile Penetration Rate Impact Analysis 63
4.7 Summary 67
Chapter 5 Modeling Location Update Strategy 68
5.1 The Optimal Timer-based Location Update Model 68
5.1.1 Model Formulation 68
5.1.2 Numerical Analysis 74
5.2 The Optimal Distance-based Location Update Model 87
5.2.1 Model Formulation 87
5.2.2 Numerical Analysis 90
5.3 Summary 103
Chapter 6 Conclusions and Recommendations 107
6.1 Novelty and Contribution of this Research 107
6.2 Conclusions 110
6.3 Recommendations 114
References 116
Appendix – Cardano Formula 123
Appendix – List of Acronym 128


LIST OF FIGURES
Figure 1.1 Statistical Information of Mobile Phone Subscribers 2
Figure 1.2 Study Process 7
Figure 2.1 Triangulation Positioning Method 10
Figure 2.2 Cellular Network Architecture 14
Figure 2.3 Operational Process of Traffic Information System 17
Figure 2.4 Location Variation of Distance-based Method 20
Figure 3.1 GeoMode Cumulative Probability Chart 39
Figure 4.1 Segment-based Model Diagram 50
Figure 4.2 Simulation Flow Chart of Segment-based Method 54
Figure 4.3 Simulation Program of Segment-based Method 58
Figure 4.4 Simulation Results of General Condition 60
Figure 4.5 Simulation Results of Near Saturation 61
Figure 4.6 Simulation Results of Over Saturation 61
Figure 4.7 Simulation Results of Light Traffic 62
Figure 4.8 Simulation Results of Segment Length 63
Figure 4.9 Simulation Result of Mobile Penetration Rate 64
Figure 5.1 Location Update and Paging Sequence Diagram 71
Figure 5.2 Potential Cells Passed From Last Update 72
Figure 5.3 Total Cost of Timer-based Model for Speed of 10 KPH 76
Figure 5.4 Total Cost of Timer-based Model for Speed of 20 KPH 76
Figure 5.5 Total Cost of Timer-based Model for Speed of 30 KPH 77
Figure 5.6 Total Cost of Timer-based Model for Speed of 40 KPH 77
Figure 5.7 Total Cost of Timer-based Model for Speed of 50 KPH 78
Figure 5.8 Total Cost of Timer-based Model for Speed of 60 KPH 78
Figure 5.9 Total Cost of Timer-based Model for Speed of 70 KPH 79
Figure 5.10 Total Cost of Timer-based Model for Speed of 80 KPH 79
Figure 5.11 Total Cost of Timer-based Model for Speed of 90 KPH 80
Figure 5.12 Relation of Optimal Timer and Cruising Speed 81
Figure 5.13 Relation of Optimal Timer vs. Update Cost (Speed=60KPH) 82
Figure 5.14 Relation of Optimal Timer vs. Approximate Value 84
Figure 5.15 Difference of Total Cost between Optimal Timer and Approximate Value 85
Figure 5.16 Total Cost of Distance-based Model for Speed of 10 KPH 92
Figure 5.17 Total Cost of Distance-based Model for Speed of 20 KPH 92
Figure 5.18 Total Cost of Distance-based Model for Speed of 30 KPH 93
Figure 5.19 Total Cost of Distance-based Model for Speed of 40 KPH 93
Figure 5.20 Total Cost of Distance-based Model for Speed of 50 KPH 94
Figure 5.21 Total Cost of Distance-based Model for Speed of 60 KPH 94
Figure 5.22 Total Cost of Distance-based Model for Speed of 70 KPH 95
Figure 5.23 Total Cost of Distance-based Model for Speed of 80 KPH 95
Figure 5.24 Total Cost of Distance-based Model for Speed of 90 KPH 96
Figure 5.25 Relation of Optimal Distance vs. Cruising Speed 97
Figure 5.26 Relation of Optimal Distance vs. Update Cost (Speed=60KPH) 97
Figure 5.27 Relation of Optimal Timer vs. Approximate Value (Raw Value) 101
Figure 5.28 Difference of Total Cost between Optimal Timer and Approximate Value (Raw Value) 101
Figure 5.29 Relation of the Optimal Timer and Approximate Result 102
Figure 5.30 Difference of Total Cost between the Optimal Timer and Approximate Result 102


LIST OF TABLES
Table 2.1 Characteristics of All Network-based Positioning Technologies 12
Table 4.1 Calculation of Segment-based Method 52
Table 4.2 Value of Variables in Simulation Program 55
Table 4.3 Minimum Sample Sizes Analysis 66
Table 5.1 Variables in Timer-based Model 69
Table 5.2 Illustration of Optimal Timers (t) 75
Table 5.3 Numerical Result of Approximated Optimal Timers (t) 83
Table 5.4 Variables in Distance-based Model 88
Table 5.5 Illustration of Optimal Distance (m) 91
Table 5.6 Numerical Result of Approximated Optimal Distance (m) 99
Table 5.7 Comparison of the Minimum Total Cost 105
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