Traffic Flow Prediction of Expressway Bottlenecks by an Attention Long Short-Term Memory Model
Traffic Flow Prediction of Expressway Bottlenecks by an Attention Long Short-Term Memory Model
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基于注意力长短期记忆模型的高速公路瓶颈交通流量预测
DOI：
10.11175/easts.14.1989
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kuniaki SASAKI
中科院分区：
文献类型：
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作者：
Liu;Xingwei;Kuniaki SASAKI
深度学习方法最近已被应用于预测领域，具有高精度。然而，深度学习所涉及的过程代表了一种难以解释的“黑匣子”。此外，最近提出的注意力机制提供了与深度学习模型相同的准确性和可解释性。在这项研究中，我们提出了一种具有注意力机制框架的长短期记忆（LSTM）方法，我们称之为注意力LSTM模型，它完成了交通流序列并学习了交通网络的时间特征。实证结果表明，该模型的性能优于经典的ARIMA模型，随机森林和传统的LSTM模型，这两个模型已经产生了良好的效果。这种更高的性能水平反映了更少的错误，更快的收敛和更准确的预测。此外，所提出的模型提供了一个可能的解释，高精度的交通流预测。注意机制说明除了早晚高峰外，下午的某些时段对后续交通流也有较大影响，为交通流预测提供了新的可能性。这一发现有助于我们了解高速公路交通流的趋势和模式。
Deep learning methods have recently been applied to the prediction field with high accuracy. However, the processes involved in deep learning represent a kind of “black box” that can be difficult to interpret. In addition, the recently proposed attention mechanism provides both the same level of accuracy and interpretability as deep learning models. In this study, we propose a long short-term memory (LSTM) approach with an attention mechanism framework, which we call the attention LSTM model that completes a traffic flow sequence and learns the temporal features of the traffic network. Empirical results demonstrated that the proposed model performs better than classical ARIMA model, the random forest and a conventional LSTM model, both of which already produce good results. This higher level of performance is reflective of fewer errors, faster convergence, and more accurate prediction. Furthermore, the proposed model provides a possible explanation for the high accuracy of traffic flow prediction. The attention mechanism illustrates that in addition to the morning and evening peaks, some periods in the afternoon also have a greater impact on subsequent traffic flow, suggesting new possibilities for traffic flow prediction. This finding helps us to understand the trends and patterns of expressway traffic flow.