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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]   [檢視]  [下載] 本電子論文使用權限為同意立即開放。 已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。 請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。
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
參考文獻	[1] E. Cascetta. Transportation Systems Engineering: Theory and Methods. Springer Science & Business Media, 2013. [2] W.-T. Sung. A study of multilane highway traffic flow models. Master’s thesis, National Chiao Tung University, 2012. [3] S. Ardekani, M. Ghandehari, and S. Nepal. Macroscopic speed-flow models for characterization of freeway and managed lanes. Buletinul Institutului Politehnic din lasi. Sectia Constructii, Arhitectura, 57:149, 2011. [4] M. M. Hamed, H. R. Al-Masaeid, and Z. M. B. Said. Short-term prediction of traffic volume in urban arterials. J. Transp. Eng., 121:249–254, 1995. [5] V. D. V. Mascha, M. Dougherty, and S. Watson. Combining kohonen maps with arima time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4:307–318, 1996. [6] A. Bressan and K. T. Nguyen. Conservation law models for traffic flow on a network of roads. Networks Heter. Media, 10:255–293, 2015. [7] Y.-T. Lee. Nonlinear Balance Laws in Traffic Flow - A Model with Lane-changing Intensity. Master’s thesis, National Central University, 2013. [8] C.-W. Liang. A Study of Some Numerical Schemes for Hyperbolic PDE Problems. Master’s thesis, National Central University, 2017. [9] C.-H. Lin. Applications of High-order Markov Chains in Traffic Flow. Master’s thesis, National Chiao Tung University, 2017. [10] C. Siripanpornchana, S. Panichpapiboon, and P. Chaovalit. Travel-time prediction with deep learning. In Region 10 Conference (TENCON), 2016 IEEE, pages 1859– 1862. IEEE, 2016. [11] Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang. Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst., 16:865–873, 2015. [12] E. I. Vlahogianni, M. G. Karlaftis, and J. C. Golias. Optimized and meta-optimized neural networks for short-term traffic flow prediction: A genetic approach. Transport. Res. C Emerg. Tech., 13:211–234, 2005. [13] R. Fu, Z. Zhang, and L. Li. Using LSTM and GRU neural network methods for traffic flow prediction. In Chinese Association of Automation (YAC), Youth Academic Annual Conference of, pages 324–328. IEEE, 2016. [14] M. Levin and Y.-D. Tsao. On forecasting freeway occupancies and volumes. Transp. Res. Rec., pages 47–49, 1980. [15] S. Lee and D. Fambro. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp. Res. Rec., pages 179–188, 1999. [16] B. Williams. Multivariate vehicular traffic flow prediction: Evaluation of arimax modeling. Transp. Res. Rec., pages 194–200, 2001. [17] W.-L. Jin. A multi-commodity lighthill-whitham-richards model of lane-changing traffic flow. Procedia Soc Behav Sci, 80:658–677, 2013. [18] P. Goatin and S. Scialanga. The Lighthill-Whitham-Richards traffic flow model with non-local velocity: analytical study and numerical results. PhD thesis, Inria Sophia Antipolis; INRIA, 2015.
指導教授	黃楓南(Feng-Nan Hwang)	審核日期	2018-7-25
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