Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

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 y...

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Autores principales:	Ifigenia Drosouli, Athanasios Voulodimos, Georgios Miaoulis, Paris Mastorocostas, Djamchid Ghazanfarpour
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	transportation mode detection deep learning recurrent neural networks LSTM Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/d7a58bcfa6ac428381d307a231c48929
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spelling	oai:doaj.org-article:d7a58bcfa6ac428381d307a231c489292021-11-25T17:29:49ZTransportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data10.3390/e231114571099-4300https://doaj.org/article/d7a58bcfa6ac428381d307a231c489292021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1457https://doaj.org/toc/1099-4300The 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.Ifigenia DrosouliAthanasios VoulodimosGeorgios MiaoulisParis MastorocostasDjamchid GhazanfarpourMDPI AGarticletransportation mode detectiondeep learningrecurrent neural networksLSTMScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1457, p 1457 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	transportation mode detection deep learning recurrent neural networks LSTM Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	transportation mode detection deep learning recurrent neural networks LSTM Science Q Astrophysics QB460-466 Physics QC1-999 Ifigenia Drosouli Athanasios Voulodimos Georgios Miaoulis Paris Mastorocostas Djamchid Ghazanfarpour Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
description	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.
format	article
author	Ifigenia Drosouli Athanasios Voulodimos Georgios Miaoulis Paris Mastorocostas Djamchid Ghazanfarpour
author_facet	Ifigenia Drosouli Athanasios Voulodimos Georgios Miaoulis Paris Mastorocostas Djamchid Ghazanfarpour
author_sort	Ifigenia Drosouli
title	Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
title_short	Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
title_full	Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
title_fullStr	Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
title_full_unstemmed	Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data
title_sort	transportation mode detection using an optimized long short-term memory model on multimodal sensor data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d7a58bcfa6ac428381d307a231c48929
work_keys_str_mv	AT ifigeniadrosouli transportationmodedetectionusinganoptimizedlongshorttermmemorymodelonmultimodalsensordata AT athanasiosvoulodimos transportationmodedetectionusinganoptimizedlongshorttermmemorymodelonmultimodalsensordata AT georgiosmiaoulis transportationmodedetectionusinganoptimizedlongshorttermmemorymodelonmultimodalsensordata AT parismastorocostas transportationmodedetectionusinganoptimizedlongshorttermmemorymodelonmultimodalsensordata AT djamchidghazanfarpour transportationmodedetectionusinganoptimizedlongshorttermmemorymodelonmultimodalsensordata
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Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

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