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研究生:李翎嘉
研究生(外文):Lin-ChiaLee
論文名稱:基於M2M網路架構並使用交通資訊探勘之事件偵測演算法
論文名稱(外文):Mining Traffic Pattern for Incident Detectionover M2M network
指導教授:蘇淑茵蘇淑茵引用關係
指導教授(外文):Sok-Ian Sou
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:63
中文關鍵詞:M2M網路事件偵測演算法資料探勘階層式路徑
外文關鍵詞:M2M networkIncident DetectionData Mininghierarchypath
相關次數:
  • 被引用被引用:0
  • 點閱點閱:129
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
近年來行車安全一直為大眾所關心的議題,各種事件偵測演算法被提出用以解決多變的車流環境,共通點都是希望在事件發生後,我們能準確預測同時縮短偵測時間讓駕駛有充足的時間去做反應。惟大多數的演算法主要皆是針對高速公路的事件偵測情形,若直接用於一般道路上會造成偵測效率不佳、誤報率過高和精確度較差的情形。
本論文中我們將利用資料探勘技術並利用階層式的觀念提出一套適用於一般市區道路的事件偵測演算法。藉由車輛的移動路徑趨勢,我們可以分析出目前的車流趨勢。同時,根據前段時間的車流資訊與歷史車流資料庫的比對,我們可以針對目前車流情形偵測出是否有事件發生影響交通。除此之外,我們也利用M2M的網路架構幫助我們蒐集車流資訊,如此一來我們僅需設置少量的RSU即可蒐集完整的車流資訊。實驗結果顯示我們提出的系統在一般市區道路的車流情形下可以提供較佳的事件偵測率、低誤報率及一定程度的精確性。同時,我們的系統亦可以在車流量較為稀疏的環境中藉由參數的調整保持一定的事件偵測率和良好的精確度,提供正確行車資訊給駕駛。

One of the fundamental requirements of a traffic management system is the ability to determinately response when an incident has occurred. Most of the existing incident detection techniques suffer from many limitations including their inability to correctly detect incidents under urban traffic conditions and generation of many false alarms.
In this thesis, we propose a novel data mining algorithm named TS-Mine to solve the problem on incident detection. The TS-Mine algorithm includes two stages. On the first stage, the gateways filter the lower variations of traffic patterns with DF-Algorithm. Simultaneously, we propose a path table to record the flow distribution of traffic patterns. Secondly, we explore the abnormal pattern with the historical database to detect the incident location with IM-Algorithm. Experimental results show that our system can provide higher detection rate and accuracy in normal traffic flow. Moreover, the system also fits for low traffic flow environment without lower false alarm rate.

摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄: VI
表目錄: VIII
一、 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究對象與範圍 5
二、 文獻探討 6
2.1 M2M網路回顧 6
2.2 WAP-tree回顧 6
2.3 事件自動偵測演算法回顧 7
三、 系統設計 11
3.1 系統架構 11
3.2 問題描述 14
3.2.1. 欲解決的問題 14
3.2.2. 定義 14
3.2.3. 問題假設 16
3.2.4. 設計概念 16
3.2.5. 系統輸出 17
3.3 行車資料處理 17
3.4 交通資訊探勘演算法 21
3.4.1. 交通資訊探勘演算法(TS-Mine Algorithm) 21
3.4.2. 資料篩選演算法DataFiltering Algorithm(DF-Algorithm) 25
3.4.3. 事件探測演算法IncidentMining Algorithm(IM-Algorithm) 26
3.5 評估指標 29
3.5.1 效能分類 29
3.5.2 效能評估 30
四、 系統模擬 33
4.1 系統設定 33
4.2 高車流量下門檻值與系統效能的影響 36
4.3 高車流量下W與Δt對系統效能的影響 41
4.4 低車流量下門檻值與系統效能的影響 47
4.5 低車流量下W與Δt對系統效能的影響 52
4.6 實驗討論 56
五、 結論與未來展望 58
5.1 結論 58
5.2 未來展望 59
參考文獻 60


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