Identifying traffic context using driving stress: a Longitudinal preliminary case study
| dc.contributor.author | Bitkina, Olga Vl. | |
| dc.contributor.author | Kim, Jungyoon | |
| dc.contributor.author | Park, Jangwoon | |
| dc.contributor.author | Park, Jaehyun | |
| dc.contributor.author | Kim, Hyun K. | |
| dc.creator.orcid | https://orcid.org/0000-0002-5264-6941 | en_US |
| dc.creator.orcid | https://orcid.org/0000-0002-5264-6941 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5264-6941https://orcid.org/0000-0002-5264-6941 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5264-6941 | |
| dc.creator.orcid | https://orcid.org/0000-0002-5264-6941 | |
| dc.date.accessioned | 2021-10-27T21:43:14Z | |
| dc.date.available | 2021-10-27T21:43:14Z | |
| dc.date.issued | 2019-05-09 | |
| dc.description.abstract | 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. | en_US |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Bitkina, O.V., Kim, J., Park, J., Park, J. and Kim, H.K., 2019. Identifying traffic context using driving stress: A longitudinal preliminary case study. Sensors, 19(9), p.2152. | en_US |
| dc.identifier.citation | Bitkina, O.V., Kim, J., Park, J., Park, J. and Kim, H.K., 2019. Identifying traffic context using driving stress: A longitudinal preliminary case study. Sensors, 19(9), p.2152. | |
| dc.identifier.doi | https://doi.org/10.3390/s19092152 | |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89878 | |
| dc.language.iso | en_US | en_US |
| dc.language.iso | en_US | |
| dc.publisher | MDPI | en_US |
| dc.publisher | MDPI | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | artificial intelligence | en_US |
| dc.subject | driving stress | en_US |
| dc.subject | electrodermal activity | en_US |
| dc.subject | road traffic | en_US |
| dc.subject | road types | en_US |
| dc.subject | artificial intelligence | |
| dc.subject | driving stress | |
| dc.subject | electrodermal activity | |
| dc.subject | road traffic | |
| dc.subject | road types | |
| dc.title | Identifying traffic context using driving stress: a Longitudinal preliminary case study | en_US |
| dc.title | Identifying traffic context using driving stress: a Longitudinal preliminary case study | |
| dc.type | Article | en_US |
| dc.type | Article |