Traffic prediction is necessary for management departments to dispatch vehicles and for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve the problem of spatial dependencies and temporal dynamics. This paper proposes a useful dynamic model to predict the urban traffic volume by combining fully bidirectional LSTM, a complex attention mechanism, and external features, including weather conditions and events. First, we adopt bidirectional LSTM to obtain temporal dependencies of traffic volume dynamically in each layer, which is different from the hybrid methods combining bidirectional and unidirectional approaches. Second, we use a more elaborate attention mechanism to learn short-term and long-term periodic temporal dependencies. Finally, we collect weather condition and event information as external features to further improve the prediction precision. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets compared to the most recently developed method and is therefore a useful tool for urban traffic prediction.

交通预测对于管理部门调度车辆和驾驶员避免拥挤的道路非常必要。近年来，已经提出了许多基于深度学习的交通预测方法，其主要目的是解决空间依赖性和时间动态问题。本文提出了一个有用的动态模型，通过将完全双向的LSTM，复杂的关注机制以及包括天气条件和事件在内的外部特征相结合，来预测城市交通量。首先，我们采用双向LSTM来动态获取每一层流量的时间依赖性，这与结合了双向和单向方法的混合方法不同。其次，我们使用更精细的注意力机制来学习短期和长期周期性时间依赖性。最后，我们收集天气状况和事件信息作为外部特征，以进一步提高预测精度。实验结果表明，与最新开发的方法相比，该模型对NYC-Taxi和NYC-Bike数据集的预测精度提高了约3-7％，因此是用于城市交通量预测的有用工具。