系統識別號 U0026-2911201208310200
論文名稱(中文) 提供獎勵機制給以參與式感知為基礎的交通預測系統
論文名稱(英文) Providing Incentives for a Road Traffic Prediction System based on Participatory Sensing
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 101
學期 1
出版年 101
研究生(中文) 王涵毅
研究生(英文) Han-Yi Wang
電子信箱 wanghy917@gmail.com
學號 P76984144
學位類別 碩士
語文別 英文
論文頁數 20頁
口試委員 指導教授-藍崑展
中文關鍵字 參與式感知  交通預測系統  虛擬貨幣  獎勵機制  預測精度 
英文關鍵字 Participatory Sensing  Traffic Prediction System  Virtual Credit  Incentive  Mechanism  Prediction Accuracy 
中文摘要 道路交通預測系統需要有充足的有效輸入資料,才能夠有準確的預測結果。相對於固定安裝於路邊的感測器,透過在車上使用者以參與式感知的方式收集交通資料,是一個更具擴展性且有效的方法。另一方面,因為感測設備的使用者參與了收集資料,如何提供獎勵機制,讓使用者能有更多貢獻成為了一個很重要的議題。在本論文中,我們提出了一個新的獎勵機制,使用者可以透過上傳資料來賺取虛擬貨幣。而當他們想透過我們的交通預測系統知道預測的路況時,也必須用所賺到的貨幣來付款。為了訂定使用者上傳資料的合理價格,我們量化了在交通預測系統中單一資料對預測準確率的貢獻度,並用它作為該資料的價錢,進而鼓勵人們收集更多有用的資料。我們使用了詳細的車輛模擬器來評估這個獎勵機制。首先我們驗證了它的公正性,我們證明了該獎勵機制能分辨資料的好壞,在實驗中,無法反映真實交通的資料將會獲得比較低的價格。在第二個實驗中,我們透過分析節點數目、速度變異,以及預測準確度的關係。最後,我們讓每台車子根據目前每條道路的可賺到價錢,來決定他們的路徑。實驗結果顯示,當車子數量很多時,交通預測系統的準確率可以因為該獎勵機制而有明顯的改善。
英文摘要 The prediction accuracy of road traffic prediction systems are based on sufficient and validate input data. Comparing with fixed installation of road side sensors, Using GPS probe vehicles incorporating with participatory sensing to collect traffic data is a more scalable and efficient method. On the other hand, when the device owner are participant, how to providing an incentive mechanism to promote users’ contribution becomes an important issue. In this paper, we propose a new incentive mechanism for participatory sensing based road traffic prediction system. Users could earn virtual credits by uploading their data, and they need to pay credits when they want to know the future traffic condition by accessing our prediction service. To define a reasonable price of users’ data, we quantify the contribution of it, and the quantified contribution is used as the price to encourage people to collect more useful data. We use a detailed vehicular simulator to evaluate our incentive mechanism. In the first experiment, we prove that the proposed pricing scheme could distinguish the quality of data. Data which can’t reflect the real road speed will have lower price. In the second experiment, we analyze the relationship between the number of nodes, variation of speed, and prediction accuracy. Finally, we let the nodes follow the current price of data on roads to decide their route. The experiment result shows that the proposed incentive mechanism could improve the prediction accuracy in evidence when the node number is large.
論文目次 中文摘要 I
英文摘要 II
誌 謝 III
1. Introduction 1
2. Related Works 4
3. System Overview 9
3.1 Architecture 9
3.2 Prediction model 10
3.3 Incentive mechanisms 11
4. Simulation 13
5. Conclusion 17
Bibliography 18
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