A Method for Predicting Speed of Vehicles on Expressways Based on a ST-GCAN Model
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摘要: 车辆速度是影响高速公路通行效率和安全的重要指标，因此实现对高速公路车辆速度的精准预测有助于减少交通事故进而提升交通智能管控服务水平。基于现有深度学习模型，研究了融合图卷积网络（convo-lutional neural network，GCN）、长短期记忆网络（long short-term memory network，LSTM）和注意力机制的车辆速度预测模型（ST-GCAN）：利用图卷积网络提取复杂高速路网的空间关联特征；使用长短期记忆网络提取车辆速度的历史数据间的时间关联特征；结合注意力机制聚集并分析车辆速度的历史数据和预测值之间的相关性。此外为保障预测模型网络信息完整并解决训练时协变量偏移问题，模型使用密集连接和层归一化技术以提升模型性能表现。利用青海省西宁市的高速公路车辆速度数据集开展实例分析，研究区域包括8个收费站共49条路段，时间跨度为2020年5月1日—8月31日，以小时为步长，共计94 777条数据。实验得到未来1小时高速公路车辆速度的预测效果：平均绝对误差（mean absolute error，MAE）为12.762，均方根误差（root mean square error，RMSE）为21.535，决定系数（R2）为0.651。与传统的时间序列模型和自回归移动平均模型相比，ST-GCAN模型的MAE误差分别降低了约11.1%和19.7%，而对比现有多种深度学习预测模型，ST-GCAN模型的MAE误差降低了约8.0%~10%。ST-GCAN模型在高速公路路网可以实现良好的车辆速度预测效果，满足交通智能管控中的实际预测需求。Abstract: The speed of vehicles on expressways is a significant indicator for describing the effectiveness and safety of road transportation system. Accurate prediction of vehicle speed on expressways can contribute to reduction of traffic accidents and improvement of the level of services. In this sense, a prediction method for vehicle speed, called ST-GCAN, is developed, which integrates graph convolutional neural network (GCN), long short-term memo-ry network (LSTM) and attention mechanism into one model. Graph convolutional network is used to extract the spatial correlations of complex networks of expressways, long-short term memory network is used to extract the temporal correlations of historical data of vehicle speed, and attention mechanism is used to aggregate and analyze the correlation between historical data and predicted vehicle speed. In addition, the model employs dense connec-tions and layer normalization to ensure the integrity of information in the prediction model and to solve the problem of covariate shift during training. The model is tested with a dataset of vehicle speed on expressways of the City of Xining, Province of Qinghai, which contains a total of 94 777 hourly observations on 49 road sections at 8 toll sta-tions from May 1 to August 31, 2020. The ST-GCAN model predicts the vehicle speed in the next hour withthe mean absolute error (MAE) of 12.762%, root mean square error (RMSE) of 21.535%, and R2 of 0.651.Compared to the HA model and the ARIMA model, the MAE of the ST-GCAN model is reduced by 11.1% and 19.7%, respec-tively. Compared to other deep learning models, it is reduced by approximately 8.0% to 10%. In conclusion, the ST-GCAN model can accurately estimate vehicle speed on expressways and shall be able to meet the requirements of intelligent traffic control systems.
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Key words:
- traffic engineering /
- expressway vehicle speed /
- prediction model /
- neural network /
- attention mechanism
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表 1 预测指标对比
Table 1. Comparison of forecasting indicators
模型 MAE/（km/h） RMSE/（km/h） R2 HA 14.360 22.867 0.427 ARIMA 15.896 23.137 0.269 SVM 14.342 23.095 0.603 Bi-LSTM 13.912 23.714 0.583 FI-RNNs 13.760 23.721 0.583 HyperNet 13.875 23.714 0.583 Multi-view NN 14.205 22.274 0.632 ST-GCAN 12.762 21.535 0.651 
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