English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70548/70548 (100%)
Visitors : 23221100      Online Users : 250
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81528

    Title: Data assimilation with Long Short-Term Memory Networks based on Attention for Highway Traffic Flow Prediction
    Authors: 張家銘;Chang, Chia-Ming
    Contributors: 數學系
    Keywords: 交通流;機器學習;卡爾曼濾波;Traffic flow;LSTM;Kalman Filter
    Date: 2019-08-21
    Issue Date: 2019-09-03 16:00:24 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 交通流量預測是交通工程中一個活躍的研究課題。大致上,交通流量預測模型可以分為三種,第一種是有母數方法,第二種是無母數方法,最後一種是基於PDE的模擬。另外還有有母數方法和無母數方法的混合方法。在這項工作中,我們建議將數據驅動的仿真技術與機器學習工具相結合,以減少預測誤差,並使用卡爾曼濾波器(KF)實現數據同化。


    在本研究中,我們使用SARIMA和具有注意力機制的LSTM作為基線方法。自回歸整合移動平均線(ARIMA)是用於單變量時間序列數據預測的最廣泛使用的預測方法之一。SARIMA是ARIMA的延伸,有季節性的成分。長短期記憶網絡(LSTM NN)是一種可以學習長期依賴關係的特殊RNN。此外,加入注意機制可以幫助我們更好的預測未來。我們將它們與我們提出的方法比較,實驗結果表明,該方法優於SARIMA和具有注意機制的LSTM。;Traffic flow prediction an active research topic in transportation engineering. In general, the traffic flow prediction model can be divided into three categories, one is PDE-based simulation, another one is parametric approaches, and the other is non-parametric approaches. There are further hybrid approaches to parametric approaches and non-parametric approaches. In this work, we propose combining the data-driven simulation technique with machine learning tools to decrease prediction error, and use the Kalman Filter (KF) on this basis to achieve the effect of data assimilation.

    The KF consists of two steps: prediction and correction. In the prediction step, we use the EX method to discretize the LWR model where the MacNicholas model is used as the fundamental relation between the velocity and density. Since the data at the boundary points in the future period are not available. The predicted values obtained by using LSTM with the attention mechanism are used for setting the boundary condition. In the correction step, we use the predicted value obtained by the LSTM with attention mechanism as the observation value, which is used to weight our predicted value and get the correction predicted value.

    In this study, we use SARIMA and the LSTM Attention as the baseline methods. Autoregressive Integrated Moving Average (ARIMA) is one of the most widely used methods of prediction for university time series data prediction. SARIMA is an extension of ARIMA with seasonal components. Long Short Term Memory networks (LSTMs) is a special kind of RNN that can learn long-term dependencies better than RNN. In addition, adding attention mechanisms can help us better predict the future. We compare them with our proposed method. The experimental results demonstrate that our method outperforms SARIMA and LSTM Attention.
    Appears in Collections:[數學研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明