Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example
Pei-Ya Ting, Tomotaka Wada, Yi-Lun Chiu, Min-Te Sun, Kazuya Sakai, Wei‐Shinn Ku, Andy An-Kai Jeng, Jing-Shyang Hwu
Abstract
As the population keeps growing, traffic congestion happens 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 freeway traffic. The data used in this research are derived from the traffic dataset 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 freeway, a 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. To increase computational efficiency, the travel time predictions for consecutive toll gates every 5 minutes apart are pre-computed offline, so that the online travel time prediction of the whole trip can be obtained by simply summing up a few numbers. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.