A Forecasting Model of Short-term Traffic Flow on Expressways During Holidays Based on ETC Data and A-BiLSTM Neural Network Models
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摘要: 电子不停车收费（electronic toll collection，ETC）门架系统为节假日高速公路短时交通流预测提供了数据支撑。针对节假日场景下高速公路交通流的非线性和复杂性特征，基于ETC门架数据研究了由注意力机制（attention）和双向长短期记忆（bidirectional long/short-term memory，BiLSTM）神经网络组成的Attention-BiLSTM（A-BiLSTM）组合模型。通过对ETC门架数据进行预处理，保证模型输入的可靠性；采用滑动窗口方法构建监督学习样本，提高模型学习效率。在模型中，使用BiLSTM神经网络，实现对交通流数据前向和后向时间依赖性特征的深入提取；引入注意力机制动态地权衡网络提取信息的重要程度，增强隐藏层特征的非线性表达能力；利用贝叶斯优化方法对模型进行超参数调优，提高模型的预测性能。采集大理-丽江高速公路白汉场至拉市镇的门架数据，处理成时间粒度为5，10，15 min的交通流数据进行模型验证。实验结果表明：①相比于自回归移动平均模型、支持向量机的预测结果，A-BiLSTM组合模型的均方根误差（root mean square error，RMSE）分别降低了73.3%和49.1%，平均绝对误差（mean absolute error，MAE）分别降低了76.0%和56.3%，预测效果好，可应用于实际的交通运营管理。②相比于未引入注意力机制的BiLSTM，A-BiLSTM组合模型的RMSE降低了41.9%，MAE降低了46.0%。③A-BiLSTM组合模型在5 min的时间粒度下表现最好，与输入数据时间粒度为10，15 min情况下所构建的模型预测误差相比，RMSE分别降低34.5%和42.1%，MAE分别降低39.9%和46.3%。Abstract: Data collected from the electronic toll collection (ETC) gantry system can be used to support the short-term traffic flow forecasting for expressways during holidays. An Attention-BiLSTM (A-BiLSTM) hybrid model, composed of the attention mechanism and bidirectional long/short-term memory (BiLSTM) neural network, is proposed to address the issues of high nonlinearity and complexity within traffic flow forecasting tasks for holidays based on ETC gantry data. The input data is preprocessed to improve the effectiveness of model training. A sliding window method is used to generate samples of supervised learning to improve the efficiency of model training. Forward and backward time-dependent features of traffic flow data is extracted based on the BiLSTM neural network. An attention mechanism is introduced to dynamically weigh the importance of the information extracted from the neural network, enhancing the ability of nonlinear expression of features in hidden layers. A Bayesian optimization method is applied to tune hyperparameters of the model, which can improve the performance of the proposed model. The gantry data is collected from Baihanchang to Lashi on the Dali-Lijang Expressway, and is divided into the data with a time granularities of 5, 10, and 15 min for model development and validation. Experiment results show that: ①Compared with the prediction results of autoregressive moving average (ARIMA) model and support vector machine (SVM) model, the root mean square error (RMSE) of A-BiLSTM hybrid model is reduced by 73.3% and 49.1%, and mean absolute error (MAE) is reduced by 76.0% and 56.3% respectively, which shows that the proposed A-BiLSTM hybrid model has a better prediction capability and can be applied to real-world traffic operation and management. ②Compared with the BiLSTM model without the attention mechanism, the RMSE and MAE of A-BiLSTM hybrid model is reduced by 41.9% and 46.0%, respectively. ③Compared with the models developed using the traffic flow data with a time granularity of 10 and 15 min, the RMSE of the model developed with the data with a time granularity of 5 minutes decreases by 34.5% and 42.1%, respectively; and the MAE decreases by 39.9% and 46.3%, respectively. Therefore, it can be concluded that the A-BiLSTM model performs best with the input data with a time granularity of 5 min.
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图 1 节假日与非节假日交通流变化趋势对比
Figure 1. Comparison of traffic flow trend between holidays and non-holidays
图 5 不同时间粒度下的模型预测结果
Figure 5. Prediction results of model under different time granularities
表 1 ETC交易数据部分字段
Table 1. Part fields of ETC transaction
序号 字段名称 字段说明 1 通行标识ID 车辆当次通行的唯一ID 2 门架编号 ETC门架的编号 3 门架hex字符串 ETC门架的hex值 4 行驶方向 1：上行；2：下行 5 门架类型 1：路段；2：省界入口；3：省界出口 6 通过时间 计费交易时间 7 OBU序号编码 不超过20个字符 8 计费车辆车牌号 计费车辆的车牌号码及颜色 9 计费车型代码 计费车辆的车型 10 入口站hex字符串 入口站的hex值 11 入口日期及时间 入口交易发生的时间 12 交易后累计里程 本次交易后标签累计里程 
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表 2 超参数约束条件与结果
Table 2. Constraints and results of hyperparameters
超参数 约束条件 结果 batch size [2, 128] 96 units [2, 256] 96 epochs [100, 500] 435 optimizer [Adam，SGD，RMSprop] Adam
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表 3 不同窗口下的误差
Table 3. Error under different windows
窗口大小ΔX值/h 时间粒度：5 min 时间粒度：10 min 时间粒度：15 min ERMSE EMAE ERMSE EMAE ERMSE EMAE 0.5 5.046 3.406 7.906 6.022 9.717 8.016 1 5.216 3.525 7.707 5.668 9.352 7.519 3 5.419 3.662 7.860 5.678 8.743 6.678 6 5.493 3.887 8.894 6.476 8.708 6.338
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表 4 预测误差对比
Table 4. Comparsion of the prediction error
数据 ERMSE EMAE 加入4月30日的交通流数据 5.046 3.406 不加入4月30日的交通流数据 5.451 3.822
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表 5 8种模型预测误差对比
Table 5. Comparison of prediction errors of 8 models
模型 ERMSE EMAE ARIMA 18.867 14.189 SVM 9.908 7.789 BiLSTM 8.685 6.303 LSTM 6.846 4.885 TCC-LSTM 5.401 3.598 1DCNN-LSTM-Attention 5.292 3.686 LSTM-BP 5.084 3.475 A-BiLSTM 5.046 3.406
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