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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example
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Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example

机译：使用深杂种模型的高速公路旅行时间预测 - 以孙中山高速公路为例

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摘要

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.
机译：随着人口的不断发展，交通拥堵越来越多。因此，旅行时间已成为驾驶经验的重要指标。准确的旅行时间信息有助于驾驶员更明智地规划他们的路线，从而有效缓解交通拥堵。在这项研究中，我们提出了一种用于高速公路交通的车辆行程时间预测模型。本研究中使用的数据来自台湾高速公路局的交通数据集，旅行时间预测是为台北和新竹之间的孙中山高速公路。首先，AutoEncoder避免了原始数据的缺失值。然后根据时间序列分段数据并用于构建预测模型。为了有效地捕获预测车辆在高速公路上行驶的行程所需的隐藏特征，我们的系统采用了深度学习架构，包括GRU神经网络模型，XGBoost模型和结合GRU的混合模型通过线性回归和xgboost。为了提高计算效率，每5分钟的连续收费门的行进时间预测被预先计算离线，从而通过简单地概括几个数字来获得整个行程的在线行驶时间预测。基于实际交通数据的实验结果表明，所提出的系统可以在预测准确性和执行时间方面实现良好的性能。

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