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摘要: 为从路网速度中完整提取路段速度的时空特征，实现高精度路段速度预测，通过调用在线地图的路径规划应用程序接口，采集路段的在线地图速度；利用图卷积神经网络(GCN)提取空间特征，利用长短期记忆(LSTM)神经网络提取时间特征，建立面向在线地图的GCN-LSTM神经网络，提取路段速度的时空特征，预测路段速度；为测试面向在线地图的GCN-LSTM神经网络表现，并评价在线地图下GCN-LSTM神经网络的优势与面向检测器速度预测模型的可替代性，以局部路网为例分析模型表现，并对比在线地图下不同模型的表现与不同数据源下近似模型的表现。研究结果表明：GCN-LSTM神经网络在训练集和测试集上的平均绝对误差(MAE)均低于5，均方根误差(RMSE)均低于6，平均绝对百分比误差(MAPE)均低于30%，训练误差和测试误差均处于较低水平，总体表现良好；GCN-LSTM神经网络的路段MAPE服从Gumbel分布，均值均落在19%±4%之间，85%分位点均落在34%±5%之间，2项指标均处于较低水平，个体表现良好；在面向在线地图的速度预测模型中，GCN-LSTM神经网络的MAE、RMSE、MAPE以及MAPE拟合曲线均值、85%分位点最低，总体和个体表现均为最佳，在面向在线地图的速度预测中具有一定优势；在近似模型中，GCN-LSTM神经网络的MAE、RMSE、MAPE以及MAPE拟合曲线均值、85%分位点最低，总体和个体表现均为最佳，则面向在线地图速度预测的可靠性高，可代替面向检测器的速度预测。Abstract: Online map speeds of roads were collected by calling the path-planning application programming interface of the online map to completely extract the spatio-temporal features of the road speed from road network speed and then achieve high-precision road speed prediction. The spatial features were extracted using a graph convolutional network (GCN), and the temporal features were extracted using a long short-term memory (LSTM) neural network. An online map-based GCN-LSTM neural network was established, the spatio-temporal features of the road speed were extracted, and the road speed was predicted. The performance of the online map-based GCN-LSTM neural network was assessed, and the advantages of the online map-based GCN-LSTM neural network and the substitutability of the detector-based speed prediction model were evaluated. By using the local road network as an example, the performance of the model was analyzed, and the performances of different online map-based models and similar models with different data sources were compared. Analysis results show that the mean absolute errors(MAEs) of the GCN-LSTM neural network are lower than 5, the root mean square errors (RMSEs) are lower than 6, and the mean absolute percentage errors (MAPEs) are lower than 30% in the training and testing sets. Hence, the training and testing errors are low, indicating good comprehensive performance. The MAPE of the GCN-LSTM neural network of the roads follows a Gumbel distribution, whose mean ranges between 19%±4%, and the 85% quantile ranges between 34%±5%. Hence, both indexes are low, indicating good individual performance. Among the online map-based speed prediction models, the MAE, RMSE, MAPE, mean, and 85% quantile of the MAPE fitting curve of the GCN-LSTM neural network have the lowest values. Hence, its comprehensive and individual performances are the best, and it exhibits advantages in online map-based speed prediction. Among the similar models, the MAE, RMSE, MAPE, mean, and 85% quantile of the MAPE fitting curve of the GCN-LSTM neural network have the lowest values. Hence, its comprehensive and individual performances are the best. Furthermore, the reliability of online map-based speed prediction is high, so that it can be used as a substitute for detector-based speed prediction. 4 tabs, 13 figs, 30 refs.
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图 2 面向在线地图的GCN-LSTM神经网络速度预测
Figure 2. Speed prediction by online map-based GCN-LSTM neural network
图 3 基于GCN神经网络的空间特征提取与映射
Figure 3. Spatial features extraction and mapping based on GCN neural network
图 4 基于LSTM神经网络的时间特征提取与映射
Figure 4. Temporal features extraction and mapping based on LSTM neural network
图 6 检测器速度及其与在线地图速度差值小提琴图
Figure 6. Violin plots of detector speed and differences with online map speed
图 10 在线地图下不同模型的路段MAPE Gumbel曲线对比
Figure 10. Gumbel curves comparison of roads' MAPE of different online map-based models
图 11 在线地图下不同模型的Gumbel曲线参数对比
Figure 11. Parameters comparison of Gumbel curves of different online map-based models
图 12 不同数据源下近似模型的路段MAPE Gumbel曲线对比
Figure 12. Gumbel curves comparison of roads' MAPE of similar models with different data sources
图 13 不同数据源下近似模型的Gumbel曲线参数对比
Figure 13. Parameters comparison of Gumbel curves of similar models with different data sources
表 1 百度地图速度
Table 1. Speed of Baidu map
(km·h-1) 时刻 17:00 17:05 … 19:30 路段1速度 26.0 25.2 … 25.8 路段2速度 28.0 27.1 … 27.1 ⋮ ⋮ ⋮ ⋮ ⋮ 
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表 2 Gumbel曲线拟合结果
Table 2. Results of Gumbel curve fitting
数据集 拟合结果 17:30 18:00 18:30 19:00 训练集 R2 0.83 0.80 0.66 0.77 μ/% 19.41 22.27 21.23 19.91 85%分位点/% 32.82 34.50 36.32 31.00 测试集 R2 0.71 0.74 0.89 0.63 μ/% 18.53 21.33 17.87 16.26 85%分位点/% 32.36 38.52 29.44 31.38
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表 3 在线地图下不同模型的总体表现对比
Table 3. Comprehensive performance comparison of different online map-based models
模型名称 MAE RMSE MAPE/% 17:30 18:00 18:30 19:00 17:30 18:00 18:30 19:00 17:30 18:00 18:30 19:00 SVM 6.48 6.20 5.01 4.97 8.05 7.66 6.44 6.29 35.98 37.55 30.97 33.82 LSTM 5.60 5.58 5.28 6.34 6.89 6.92 6.48 7.43 27.01 32.89 28.62 37.31 GCN 6.73 6.51 5.41 6.87 8.03 8.22 6.57 8.02 30.64 37.94 29.41 37.44 GCN-Seq2Seq 5.71 5.34 4.77 5.75 6.94 6.64 6.00 6.85 27.29 31.95 26.86 34.35 GCN-LSTM 4.17 4.44 3.99 4.28 5.33 5.61 5.13 5.27 22.48 27.69 24.28 28.44
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表 4 不同数据源下近似模型的总体表现对比
Table 4. Comprehensive performance comparison of similar models with different data sources
模型名称 数据源 MAE RMSE MAPE/% 17:30 18:00 18:30 19:00 17:30 18:00 18:30 19:00 17:30 18:00 18:30 19:00 GCN 检测器 8.01 8.24 8.01 9.70 9.38 9.76 9.23 10.95 34.33 41.10 37.91 46.26 GCN-Seq2Seq 5.30 5.47 4.96 5.67 6.53 6.76 6.20 6.82 25.93 31.33 27.80 34.72 GCN-LSTM 在线地图 4.17 4.44 3.99 4.28 5.33 5.61 5.13 5.27 22.48 27.69 24.28 28.44
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