Aiming at the problems of high power consumption and complex scene for scene perception in universal transportation modes, a new transportation mode detection algorithm combining Residual Network (ResNet) and dilated convolution was proposed. Firstly, the 1D sensor data was converted into the 2D spectral image by using Fast Fourier Transform (FFT). Then, the Principal Component Analysis (PCA) algorithm was used to realize the downsampling of the spectral image. Finally, the ResNet was used to mine the local features of transportation modes, and the global features of transportation modes were mined with dilated convolution, so as to detect eight transportation modes. Experimental evaluation results show that, compared with 8 algorithms including decision tree, random forest and AlexNet, the transportation mode recognition algorithm combining ResNet and dilated convolution has the highest accuracy in eight traffic patterns including static, walking and running, and the proposed algorithm has good identification accuracy and robustness.

In the multi-step traffic flow prediction task, the spatial-temporal feature extraction effect is not good and the prediction accuracy of future traffic flow is low. In order to solve these problems, a fusion model combining Long-Short Term Memory (LSTM) network, convolutional residual network and attention mechanism was proposed. Firstly, an encoder-decoder-based architecture was used to mine the temporal domain features of different scales by adding LSTM network into the encoder-decoder. Secondly, a convolutional residual network based on the Squeeze-and-Excitation (SE) block of attention mechanism was constructed and embedded into the LSTM network structure to mine the spatial domain features of traffic flow data. Finally, the implicit state information obtained from the encoder was input into the decoder to realize the prediction of high-precision multi-step traffic flow. The real traffic data was used for the experimental testing and analysis. The results show that, compared with the original graph convolution-based model, the proposed model achieves the decrease of 1.622 and 0.08 on the Root Mean Square Error (RMSE) for Beijing and New York traffic flow public datasets, respectively. The proposed model can predict the traffic flow efficiently and accurately.