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Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study

Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of...

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Autores principales: Bitkina, Olga Vl., Kim, Jungyoon, Park, Jangwoon, Park, Jaehyun, Kim, Hyun K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539244/
https://www.ncbi.nlm.nih.gov/pubmed/31075920
http://dx.doi.org/10.3390/s19092152
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author Bitkina, Olga Vl.
Kim, Jungyoon
Park, Jangwoon
Park, Jaehyun
Kim, Hyun K.
author_facet Bitkina, Olga Vl.
Kim, Jungyoon
Park, Jangwoon
Park, Jaehyun
Kim, Hyun K.
author_sort Bitkina, Olga Vl.
collection PubMed
description Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications.
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spelling pubmed-65392442019-06-04 Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study Bitkina, Olga Vl. Kim, Jungyoon Park, Jangwoon Park, Jaehyun Kim, Hyun K. Sensors (Basel) Article Many previous studies have identified that physiological responses of a driver are significantly associated with driving stress. However, research is limited to identifying the effects of traffic conditions (low vs. high traffic) and road types (highway vs. city) on driving stress. The objective of this study is to quantify the relationship between driving stress and traffic conditions, and driving stress and road types, respectively. In this study, electrodermal activity (EDA) signals for a male driver were collected in real road driving conditions for 60 min a day for 21 days. To classify the levels of driving stress (low vs. high), two separate models were developed by incorporating the statistical features of the EDA signals, one for traffic conditions and the other for road types. Both models were based on the application of EDA features with the logistic regression analysis. City driving turned out to be more stressful than highway driving. Traffic conditions, defined as traffic jam also significantly affected the stress level of the driver, when using the criteria of the vehicle speed of 40 km/h and standard deviation of the speed of 20 km/h. Relevance to industry: The classification results of the two models indicate that the traffic conditions and the road types are important features for driving stress and its related applications. MDPI 2019-05-09 /pmc/articles/PMC6539244/ /pubmed/31075920 http://dx.doi.org/10.3390/s19092152 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bitkina, Olga Vl.
Kim, Jungyoon
Park, Jangwoon
Park, Jaehyun
Kim, Hyun K.
Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title_full Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title_fullStr Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title_full_unstemmed Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title_short Identifying Traffic Context Using Driving Stress: A Longitudinal Preliminary Case Study
title_sort identifying traffic context using driving stress: a longitudinal preliminary case study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539244/
https://www.ncbi.nlm.nih.gov/pubmed/31075920
http://dx.doi.org/10.3390/s19092152
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