博碩士論文 105221026 詳細資訊




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姓名 吳柏汎(Bo-Fan Wu)  查詢紙本館藏   畢業系所 數學系
論文名稱 基於偏微分方程的模擬,機器學習工具及其混合技術在交通流量預測中的比較研究
(A comparative study of PDE based simulation, machine learning tool, and their hybrid technique for traffic flow prediction)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2019-8-31以後開放)
摘要(中) 現今以深度學習方式預測交通流量、旅行時間的相關研究已經非常純熟,但是結合交通基本理論的相關討論研究比較缺乏,所以此篇論文探討的是結合交通流理論模型、深度學習、非線性雙曲型守恆定律,預測高速公路下交通流量預測,深度學習、數值模擬、結合數值模擬跟深度學習模型的比較研究。
摘要(英) Using deep learning model to predict traffic flow nowadays is a very popular method for research, but most of the traffic flow research only put data into deep learning model without traffic flow fundamental theorem. We combine the numerical simulation which is partial differential equation model with deep learning model which is recurrent neural networks model and predict the traffic flow. In partial difference equation, there is some theorem of traffic flow, and we have assumption by the theorem. We use a machine learning tool, partial differential equation based numerical simulation, and their hybrid technique for traffic flow prediction.
關鍵字(中) ★ 機器學習
★ 交通流模型
★ 數值模擬
關鍵字(英) ★ machine learning
★ traffic flow model
★ numerical simulation
論文目次 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Background and motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.1 Macroscopic stream model . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3.2 Numerical simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.3 LSTM and GRU neural network for prediction . . . . . . . . . . . . . . . . 16
4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.1 Data description and features . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Performance measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Experiments result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.1 Prediction by PDE model (verification) . . . . . . . . . . . . . . . . 22
4.3.2 Prediction by DNNs model . . . . . . . . . . . . . . . . . . . . . . 30
4.3.3 PDE with average boundary and DNNs predicted difference . . . . 35
5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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指導教授 黃楓南(Feng-Nan Hwang) 審核日期 2018-7-25
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