Drosouli Ifigenia, Voulodimos Athanasios, Miaoulis Georgios, Mastorocostas Paris, Ghazanfarpour Djamchid
Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece.
Department of Informatics, University of Limoges, 87032 Limoges, France.
Entropy (Basel). 2021 Nov 3;23(11):1457. doi: 10.3390/e23111457.
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.
传感技术的进步以及大数据分析的快速发展，为智能交通和智慧城市应用带来了新的时代。在此背景下，移动用户的交通方式检测（TMD）是近年来备受关注的领域。在本文中，我们提出了一种深度学习方法，用于使用从用户智能手机获取的多模态传感器数据进行交通方式检测。该方法基于长短期记忆网络及其参数的贝叶斯优化。我们针对包括最先进方法在内的多种机器学习方法，对所提出的方法进行了广泛的实验评估，该方法获得了非常高的识别率。我们还讨论了特征相关性以及降维影响等问题。
