SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM Network

Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle flow can be more reasonable to plan the release and deployment of public bicycles. There are three difficulties in traffic flow prediction to achieve higher accuracy. Firstly, more accurately to capture the spatio-temporal correlation existing in historical flow data. Secondly, the weight of each channel in the traffic flow data at the same time interval affects the prediction results. Thirdly, the proportion of closeness, period and trend of traffic flow data affects the prediction results. In this paper, we design a deep learning algorithm for short-term traffic flow prediction, called SE-MAConvLSTM. First, we designed Spatio-Temporal Feature Extraction Module (STFEM), which is composed of Convolutional Neural Network (CNN), Squeeze-and-Excitation Network (SENet), Residual Network (ResNet) and Convolutional LSTM Network (ConvLSTM) to solve the above two problems mentioned. In addition, we design multi-attention modules (MAM) to model the closeness, period and trend of traffic flow data to solve the third problem mentioned above. Finally, the aggregation module was used to integrate the output of the last time interval in STFEM and the output of the multi-attention module. Experiments are carried out on two real data sets, and the results show that the proposed model reduces RMSE by 4.5% and 3.7% respectively compared with the best baseline model.