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Student Number 91322080
Author Cheng-Yen Wang(王政彥)
Author's Email Address s1322080@cc.ncu.edu.tw
Statistics This thesis had been viewed 1721 times. Download 1076 times.
Department Civil Engineering
Year 2003
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Applying automatic vehicle identification on freeway incident detection
Date of Defense 2004-06-21
Page Count 92
Keyword
  • automatic incident detection
  • automatic vehicle identification
  • average speed
  • electronic toll collection
  • freeway
  • travel time
  • Abstract Incidents on freeways generally cause tremendous calamities than those on other types of road systems due to its closed system and higher speed limit. These calamities always include serious traffic delay and the loss of lives and financial affairs. Developing rapid and correct incident detection algorithms for freeway automatic incident detection can minimize damages from incidents, because it provides incident-related information for the traffic operators who can exclude the incident quickly and reduce the impact of freeway traffic flow efficiently.
    In 2004, Taiwan is going to build up an electronic toll collection system applying automatic vehicle identification. Except for collecting tolls, automatic vehicle identification can gather traffic parameters for freeway automatic incident detection. Worldwide, there has been little literature published about automatic vehicle identification on automatic incident detection. In Taiwan research in that subject has just started in the recent years. This research develops an automatic incident detection algorithm which combines: (1) automatic vehicle identification to obtain traffic parameters, and, (2) statistic methods to judge the incident happened or not. Because of automatic vehicle identification facility not being constructed yet, this research verifies the performance of the incident detection algorithm by utilizing traffic simulation.
    After series analysis of the incident algorithm, it is concluded that the detection rate is 97%, mean detective time is 8.58 min, and false alarm rate is 0.576%. Comparing with the algorithms in literature, it is shown that performance of this research is greater than those in detection rate and false alarm rate. And the performance in mean detective time is also acceptable. Therefore, the algorithm developed by this research can be provided to other researches and relative associations for reference.
    Table of Content 摘要III
    AbstractIV
    誌謝VI
    目錄IX
    圖目錄XII
    表目錄XIV
    第一章 緒論1
    1.1 研究背景與動機1
    1.2 研究目的3
    1.3 研究對象與範圍3
    1.4 研究內容4
    1.5 研究流程與方法4
    第二章 文獻回顧6
    2.1 AVI技術收集資料方法簡介6
    2.2 事件偵測演算法文獻回顧7
    2.2.1 非利用AVI技術之事件偵測演算法文獻8
    2.2.2 利用AVI技術之事件偵測演算法文獻14
    2.3 事件偵測演算法績效評估指標15
    第三章 事件偵測演算法架構研擬18
    3.1 事件偵測演算法邏輯架構18
    3.2 旅行時間判斷方法22
    3.3平均速率檢定方法24
    第四章 事件偵測演算法建立28
    4.1 旅行時間分配配適28
    4.1.1 車輛路段旅行時間資料取得28
    4.1.2 路段旅行時間分配配適29
    4.2 低流量最適分配:Log-logistic分配32
    4.2.1 Log-logistic分配配適與參數推估33
    4.3 中、高流量最適分配-Pearson V分配35
    4.3.1 Pearson V分配配適與參數推估35
    4.4 旅行時間門檻值訂定36
    4.5 利用平均速率檢定事件發生與否37
    4.6 決策40
    第五章 車流模擬模式43
    5.1 模擬模式架構43
    5.1.1 系統基本假設43
    5.1.2 模擬模式架構與流程45
    5.1.3 模擬模式初始條件設定50
    5.2 模擬模式驗證53
    5.2.1 模擬程式確認53
    5.2.2 模擬程式校估53
    5.2.3 模擬程式驗證54
    第六章 績效評估與敏感度分析57
    6.1 演算法績效評估背景57
    6.2 演算法績效評估59
    6.2.1 偵測率60
    6.2.2 平均偵測時間61
    6.2.3 誤報率63
    6.2.4 演算法績效比較64
    6.3 敏感度分析66
    6.3.1 偵測時距66
    6.3.2 AVI車輛比例69
    6.3.3 AVI偵測器佈設間距73
    6.3.4事件發生地點75
    6.3.5事件發生時點77
    6.3.6旅行時間門檻值80
    6.3.7平均速率檢定之顯著水準83
    6.3.8小結86
    第七章 結論與建議87
    7.1 結論87
    7.2 建議89
    參考文獻90
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    Advisor
  • Jiann-Sheng Wu(吳健生)
  • Files
  • 91322080.pdf
  • approve immediately
    Date of Submission 2004-07-05

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