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研究生:劉明竑
研究生(外文):Ming-Hung Liu
論文名稱:植基於雲端技術最佳行車時間之路徑搜尋方法
論文名稱(外文):Finding Shortest-Driving-Time Paths Based on Cloud Technology
指導教授:林祝興林祝興引用關係
指導教授(外文):Chu-Hsing Lin
口試委員:詹進科賴威伸劉榮春楊中皇
口試日期:2013-06-26
學位類別:碩士
校院名稱:東海大學
系所名稱:資訊工程學系
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:2013
畢業學年度:101
語文別:中文
論文頁數:61
中文關鍵詞:雲端運算智慧型運輸系統MapReduce基因演算法路徑再利用
外文關鍵詞:Cloud computingITSGenetic algorithmPartial paths reusabilityMapReduce
相關次數:
  • 被引用被引用:1
  • 點閱點閱:648
  • 評分評分:
  • 下載下載:1
  • 收藏至我的研究室書目清單書目收藏:0
智慧型運輸系統(Intelligent Transportation Systems, ITS)係透過通訊系統即時的溝通與連結,改善或強化人、車、路之間的互動關係,提升用路人的交通服務品質與績效。本論文以基因演算法為主要演算法,並輔以雲端運算架構(MapReduce)加速地圖資訊的運算,再配合圖層的切割與雲端計算結果再利用概念,實現”部分路徑可再利用性”,以達到節省時間並增進導航效能目的,並有助於提升整體導航之效率與準確度。從實驗結果中,我們觀察到在地圖上節點數目愈多的情況下,基因演算法所需要的執行時間也相對的愈多,而求得的近似最佳解的誤差也愈大。且根據不同染色體初始化的方式,以及使用不同雲端運算架構之回饋運算機制,在節點數目龐大的地圖上可以避免初始化失敗或陷入演算法容易過早收斂而導致無法找到近似最佳解的情形。隨著節點數劇烈增加,基因演算法配合雲端技術(MapReduce)可以在不需耗用太多資源就可以解決這類問題。實驗結果也顯示我們獲得較快及較佳的近似最佳解。
Intelligent Transportation System (ITS) provides real-time communications and connections; it increases and enhances interactions among people, and vehicles and improves traffic service quality. In this thesis, we propose to use a genetic algorithm, which is boosted by cloud computing infrastructure (MapReduce) to accelerate computation of map information. In addition, we use the concept of map layers to realize the ideas of “partial paths reusability” and “cloud computing results reusability” to enhance the navigation performance and improve the efficiency and accuracy of the overall navigation system. From the experiments, we observed that when the number of nodes of the map increases, the required execution time of the genetic algorithm increases, and the difference between the approximately optimal solution and the optimal solution increases as well. Also, for maps with a large number of nodes, one can use different chromosome initialization settings and different feedback mechanisms in cloud computing, to avoid problems such as chromosome initialization failure or premature convergence of the genetic algorithm that makes it very hard or impossible to find the approximately optimal solution in time. For applications with dramatic number of nodes, the proposed approach using the genetic algorithm and cloud technologies (MapReduce) is a suitable solution since it can offer approximate solutions without consuming lots of resources. The experimental results also showed that the proposed approach can obtain approximately optimal solutions faster and better.
內容
圖表列表
CHAPTER 1 簡介
CHAPTER 2 背景知識與相關技術
2.1智慧型行車資訊系統
2.2雲端運算
2.3 Hadoop
2.3.1 HDFS
2.3.2 HBase
2.3.3 MapReduce
2.3.4 Hive
2.4 基因演算法
2.5 地圖圖層概念
2.6 Google Earth
2.7 最短路徑問題
2.8 Quantum GIS
2.9 Quantum Navigator
CHAPTER 3 最短行車時間問題
3.1 最短行車時間問題數學模型
3.2 使用雲端技術結合基因演算法解決最短行車時間問題
3.3 群體初始化問題
CHAPTER 4 實驗結果與分析
4.1實驗環境
4.2雲端系統伺服端演算法
4.3使用節點模擬以計算平均尋找最佳路徑所需要的演化代數
4.4使用節點模擬以計算平均尋找最佳路徑所需要的收斂代數
4.5基因演算法多次平均與單次的誤差百分比比較
4.6 使用變動速率以基因演算法結合雲端架構尋找路徑
Chapter 5 結論與未來展望
參考文獻


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