@article{ref1,
title="Research on transportation mode recognition based on multi-head attention temporal convolutional network",
journal="Sensors (Basel)",
year="2023",
author="Cheng, Shuyu and Liu, Yingan",
volume="23",
number="7",
pages="e3585-e3585",
abstract="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>",
language="en",
issn="1424-8220",
doi="10.3390/s23073585",
url="http://dx.doi.org/10.3390/s23073585"
}