Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/43389
Title: Unfolding the dynamics of driving behavior: a machine learning analysis from Germany and Belgium
Authors: Roussou, Stella
Michelaraki, Eva
Katrakazas, Christos
Afghari, Amir Pooyan
Al Haddad, Christelle
Alam, Md Rakibul
Antoniou, Constantinos
Papadimitriou, Eleonora
Brijs, Tom 
Yannis, George
Issue Date: 2024
Publisher: SPRINGERONE NEW YORK PLAZA
Source: European transport research review (Print), 16 (1) , p. 1170 -1173 (Art N° 40)
Abstract: The presentation contains the result of a rheoencephalographic study of 67 sons and daughters of the patients with ischemic stroke of a hemispheric localization. Some rheoencephalographic signs testifying to the presence of a cerebro-vascular deficiency in the relatives of the probands were depicted. The use of nitroglycerin sample permitted to reveal in practically healthy relatives certain REG changes which exceeded the normal limits. These shifts were mainly observed in the elder age group of the investigated individuals (above 40 years). The obtained data can promote detection of those individuals who are predisposed to atherosclerosis and hypertensive disease, and to conduct medico-prophylactic measures in due time.
Keywords: On-road field trials;Driving behavior;Long-short-term-memory network (LSTM);Neural network;Machine learning
Document URI: http://hdl.handle.net/1942/43389
ISSN: 1867-0717
e-ISSN: 1866-8887
DOI: 10.1186/s12544-024-00655-z
ISI #: WOS:001258081900001
Rights: The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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