Technology R&D Center, Zhejiang Institute of Mechanical & Electrical Engineering Co., Ltd., Hangzhou 310053, China.
School of Modern Information Technology, Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou 310053, China.
Sensors (Basel). 2023 Mar 31;23(7):3631. doi: 10.3390/s23073631.
Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods to control traffic congestion. However, most of the existing methods of traffic prediction focus on urban road scenarios, neglecting the complexity of multivariate auxiliary information in highways. Moreover, these methods have difficulty explaining the prediction results based only on the historical traffic flow sequence. To tackle these problems, we propose a novel traffic prediction model, namely Multi-variate and Multi-horizon prediction based on Long Short-Term Memory (MMLSTM). MMLSTM can effectively incorporate auxiliary information, such as weather and time, based on a strategy of multi-horizon time spans to improve the prediction performance. Specifically, we first exploit a multi-horizon bidirectional LSTM model for fusing the multivariate auxiliary information in different time spans. Then, we combine an attention mechanism and multi-layer perceptron to conduct the traffic prediction. Furthermore, we can use the information of multivariate (weather and time) to provide interpretability to manage the model. Comprehensive experiments are conducted on Hangst and Metr-la datasets, and MMLSTM achieves better performance than baselines on traffic prediction tasks.
交通流预测是智能交通系统（ITSs）进行交通管理的最重要任务之一，它也是一个受到许多复杂因素影响的挑战性任务，例如天气和时间。许多城市采用高效的交通预测方法来控制交通拥堵。然而，大多数现有的交通预测方法都侧重于城市道路场景，忽略了高速公路中多元辅助信息的复杂性。此外，这些方法仅基于历史交通流量序列进行预测，难以解释预测结果。为了解决这些问题，我们提出了一种新颖的交通预测模型，即基于长短期记忆（LSTM）的多变量和多时段预测（MMLSTM）。MMLSTM 可以有效地结合辅助信息，例如天气和时间，基于多时段的策略来提高预测性能。具体来说，我们首先利用多时段双向 LSTM 模型融合不同时段的多元辅助信息。然后，我们结合注意力机制和多层感知机进行交通预测。此外，我们可以使用多元（天气和时间）的信息来提供可解释性以管理模型。在 Hangst 和 Metr-la 数据集上进行了综合实验，MMLSTM 在交通预测任务上的性能优于基线。
