With the rapid development of social economy, the traffic volume of urban roads has raised significantly, which has led to increasingly serious urban traffic congestion problems, and has caused much inconvenience to people’s travel. By focusing on the complexity and long-term dependence of traffic flow sequences on urban road, this paper considered the traffic flow data and weather conditions of the road section comprehensively, and proposed a short-term traffic flow prediction model based on the attention mechanism and the 1DCNN-LSTM network. The model combined the time expansion of the CNN and the advantages of the long-term memory of the LSTM. First, the model employs 1DCNN network to extract the spatial features in the road traffic flow data. Second, the output spatial features are considered as the input of LSTM neural network to extract the time features in road traffic flow data, and the long-term dependence characteristics of LSTM neural network are adopted to improve the prediction accuracy of traffic flow. Next, the spatio-temporal characteristics of road traffic flow were regarded as the input of the regression prediction layer, and the prediction results corresponding to the current input were calculated. Finally, the attention mechanism was introduced on the LSTM side to give enough attention to the key information, so that the model can focus on learning more important data features, and further improve the prediction performance. The experimental results showed that the prediction effect of the 1DCNN-LSTM-Attention model under the weather factor was better than that without considering the weather factor. At the same time, compared with traditional neural network models, the prediction effect of the proposed model revealed faster convergence speed and higher prediction accuracy. It can be found that for short-term traffic flow prediction on urban roads, the 1DCNN-LSTM network structure considering the attention mechanism provides superior features.

随着社会经济的快速发展，城市道路交通量显着增加，导致城市交通拥堵问题日益严重，给人们的出行带来诸多不便。针对交通流序列对城市道路的复杂性和长期依赖，综合考虑路段的交通流数据和天气状况，提出了一种基于注意力机制的短期交通流预测模型。 1DCNN-LSTM 网络。该模型结合了CNN的时间扩展性和LSTM长时记忆的优点。首先，该模型采用 1DCNN 网络提取道路交通流数据中的空间特征。第二，将输出的空间特征作为LSTM神经网络的输入，提取道路交通流数据中的时间特征，利用LSTM神经网络的长期依赖特性提高交通流的预测精度。接下来，将道路交通流的时空特征作为回归预测层的输入，计算当前输入对应的预测结果。最后在LSTM端引入attention机制，对关键信息给予足够的关注，让模型可以专注于学习更重要的数据特征，进一步提升预测性能。实验结果表明，1DCNN-LSTM-Attention模型在天气因素下的预测效果优于不考虑天气因素的情况。同时，与传统的神经网络模型相比，该模型的预测效果显示出更快的收敛速度和更高的预测精度。可以发现，对于城市道路的短期交通流预测，考虑注意力机制的1DCNN-LSTM网络结构提供了优越的特征。