Citation

Drosouli I, Voulodimos A, Miaoulis G, Mastorocostas P, Ghazanfarpour D. Entropy (Basel) 2021; 23(11): e1457.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/e23111457

PMID

34828155

PMCID

PMC8622795

Abstract

The advancement of sensing technologies coupled with the rapid progress in big data analysis has ushered in a new era in intelligent transport and smart city applications. In this context, transportation mode detection (TMD) of mobile users is a field that has gained significant traction in recent years. In this paper, we present a deep learning approach for transportation mode detection using multimodal sensor data elicited from user smartphones. The approach is based on long short-term Memory networks and Bayesian optimization of their parameters. We conducted an extensive experimental evaluation of the proposed approach, which attains very high recognition rates, against a multitude of machine learning approaches, including state-of-the-art methods. We also discuss issues regarding feature correlation and the impact of dimensionality reduction.

Language: en

Keywords

deep learning; LSTM; recurrent neural networks; transportation mode detection