TY  - JOUR
PY  - 2023//
TI  - Research on transportation mode recognition based on multi-head attention temporal convolutional network
JO  - Sensors (Basel)
A1  - Cheng, Shuyu
A1  - Liu, Yingan
SP  - e3585
EP  - e3585
VL  - 23
IS  - 7
N2  - Transportation mode recognition is of great importance in analyzing people's travel patterns and planning urban roads. To make more accurate judgments on the transportation mode of the user, we propose a deep learning fusion model based on multi-head attentional temporal convolution (TCMH). First, the time-domain features of a more extensive range of sensor data are mined through a temporal convolutional network. Second, multi-head attention mechanisms are introduced to learn the significance of different features and timesteps, which can improve the identification accuracy. Finally, the deep-learned features are fed into a fully connected layer to output the classification results of the transportation mode. The experimental results demonstrate that the TCMH model achieves an accuracy of 90.25% and 89.55% on the SHL and HTC datasets, respectively, which is 4.45% and 4.70% higher than the optimal value in the baseline algorithm. The model has a better recognition effect on transportation modes.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s23073585
ID  - ref1
ER  -