隨著人口不斷增長，交通壅塞的情況變得越來越頻繁。因此，旅行時間已成為交通狀況的重要指標。準確的旅行時間訊息可幫助駕駛人更明智地規劃路線，從而有效緩解交通壅塞的情形。在本研究中，我們提出了一種用於高速公路交通的車輛行駛時間預測模型。本研究使用的數據源自於台灣高速公路局的交通資料庫，並預測了中山高速公路中台北到新竹路段的旅行時間。首先，原始數據的缺失值將由Autoencoder進行插補。然後根據時間序列對數據進行分段，並用於預測模型的構建。為了有效捕捉預測高速公路行駛車輛行駛時間所需的隱藏特徵，我們的系統採用深度學習架構，包括GRU神經網絡模型，XGBoost模型和透過線性迴歸將GRU和XGBoost結合為一個新的混合模型。基於實際交通數據的實驗結果表明，我們所提出的系統在預測精確度和執行時間方面都可以取得良好的性能。;As the population keeps growing, traffic congestion becomes more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for highway traffic. The data used in this research is derived from the traffic database of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the highway, the deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.
