为克服现有方法预测步长短、无法充分利用路网时空特征的局限性，实现高速公路行程时间的准确预测，基于高速公路起讫点（OD）数据集，采用随机森林模型（RF）、极端梯度提升模型（XGBoost）、长短时记忆神经网络模型（LSTM）、K-最近邻模型（KNN）、支持向量机回归模型（SVR）等5种常用算法对车辆行程时间进行多步长预测，并采用贝叶斯回归方法对各算法进行融合，融合模型可综合各预测算法的优点，具有更高的精度和鲁棒性。以广东水官高速龙岗至布龙段为例，对未来2h内每15min的车辆行程时间进行预测，并从预测精度和训练时长等方面对各算法的性能进行了对比分析。结果表明：多种步长下，随机森林算法和XGBoost算法的整体预测效果稳定；在步长较短（未来30min）的预测中，LSTM具有最高的精度；基于贝叶斯回归的融合预测方法综合了各预测算法的优点，整体预测精度最高。

In order to overcome the shortcomings of the existing prediction methods, such as short prediction steps and insufficient utilization of spatial-temporal characteristics of road networks, and to predict the freeway travel time accurately, five commonly used prediction models, namely RF(Random Forests),XGBoost(Extreme Gradient Boosting),LSTM(Long Short-Term Memory),KNN(K-Nearest Neighbor), and SVR(Support Vector Regression), were taken to carry out multi-steps prediction of freeway travel time based on the origin and destination data set. A fusion model based on Bayesian linear regression method was proposed. The Long-gang to Bu-long section of Shui-guan Expressway in Guangdong province was taken as a case study. We predicted the travel time of every 15 minutes in the next 2 hours. The results show that the prediction performance of RF model and XGBoost model is stable under multi-steps; the LSTM model has superior prediction performance in the case of short prediction steps; the fusion method integrates the advantages of various prediction methods and has higher accuracy and robustness. The experiments also demonstrate that it has the best prediction performance.