Examining in-vehicle distraction sources in relation to crashes using a Bayesian Multinomial Logit model.
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| Title: | Examining in-vehicle distraction sources in relation to crashes using a Bayesian Multinomial Logit model. |
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| Authors: | Kutela, B.1 b-kutela@tti.tamu.edu, Kidando, E.2 e.kidando@csuohio.edu, Kitali, A. E.3 akitali@uw.edu, Mwende, S.4 siamwende95@gmail.com, Novat, N.2 |
| Source: | Advances in Transportation Studies. Nov2023, Vol. 61, p3-18. 16p. |
| Subjects: | Logistic regression analysis, Distraction, In-vehicle computing, Distracted driving, Older automobile drivers, Human error, Drunk driving, Electronic equipment |
| Geographic Terms: | Iowa |
| Abstract: | It is well understood that most crashes are the result of human errors. Among human-related errors, distracted driving, particularly related to cellphones, has received significant attention. Conversely, the underlying factors associated with in-vehicle distractions that are non-cellphone use have not been fully explored. Thus, this paper uses data from driver distraction-related crashes to examine various in-vehicle distraction sources. A Bayesian Multinomial Logit (BMNL) model was developed using 5,078 distracted-driving related crashes from Iowa. Four in-vehicle distraction sources - cellphone use, non-cellphone electronic devices, passengers, and reaching in-vehicle fallen objects - were investigated to determine factors that increase their odds of occurrence. The results suggest that drivers under the influence of alcohol are more likely to be involved in crashes associated with the distraction from cellphones. Furthermore, older drivers are less likely to be involved with distracted driving due to passengers. As expected, the more people in the vehicle, the higher the likelihood a driver can be distracted by passengers. Moreover, the association of driver distraction and speed limit, time of the day, vehicle's age, among others, were evaluated. This study provides useful information for developing and implementing strategies that minimize distractions from all in-vehicle sources. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 171889990 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Examining in-vehicle distraction sources in relation to crashes using a Bayesian Multinomial Logit model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kutela%2C+B%2E%22">Kutela, B.</searchLink><relatesTo>1</relatesTo><i> b-kutela@tti.tamu.edu</i><br /><searchLink fieldCode="AR" term="%22Kidando%2C+E%2E%22">Kidando, E.</searchLink><relatesTo>2</relatesTo><i> e.kidando@csuohio.edu</i><br /><searchLink fieldCode="AR" term="%22Kitali%2C+A%2E+E%2E%22">Kitali, A. E.</searchLink><relatesTo>3</relatesTo><i> akitali@uw.edu</i><br /><searchLink fieldCode="AR" term="%22Mwende%2C+S%2E%22">Mwende, S.</searchLink><relatesTo>4</relatesTo><i> siamwende95@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Novat%2C+N%2E%22">Novat, N.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Transportation+Studies%22">Advances in Transportation Studies</searchLink>. Nov2023, Vol. 61, p3-18. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Logistic+regression+analysis%22">Logistic regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Distraction%22">Distraction</searchLink><br /><searchLink fieldCode="DE" term="%22In-vehicle+computing%22">In-vehicle computing</searchLink><br /><searchLink fieldCode="DE" term="%22Distracted+driving%22">Distracted driving</searchLink><br /><searchLink fieldCode="DE" term="%22Older+automobile+drivers%22">Older automobile drivers</searchLink><br /><searchLink fieldCode="DE" term="%22Human+error%22">Human error</searchLink><br /><searchLink fieldCode="DE" term="%22Drunk+driving%22">Drunk driving</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+equipment%22">Electronic equipment</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Iowa%22">Iowa</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: It is well understood that most crashes are the result of human errors. Among human-related errors, distracted driving, particularly related to cellphones, has received significant attention. Conversely, the underlying factors associated with in-vehicle distractions that are non-cellphone use have not been fully explored. Thus, this paper uses data from driver distraction-related crashes to examine various in-vehicle distraction sources. A Bayesian Multinomial Logit (BMNL) model was developed using 5,078 distracted-driving related crashes from Iowa. Four in-vehicle distraction sources - cellphone use, non-cellphone electronic devices, passengers, and reaching in-vehicle fallen objects - were investigated to determine factors that increase their odds of occurrence. The results suggest that drivers under the influence of alcohol are more likely to be involved in crashes associated with the distraction from cellphones. Furthermore, older drivers are less likely to be involved with distracted driving due to passengers. As expected, the more people in the vehicle, the higher the likelihood a driver can be distracted by passengers. Moreover, the association of driver distraction and speed limit, time of the day, vehicle's age, among others, were evaluated. This study provides useful information for developing and implementing strategies that minimize distractions from all in-vehicle sources. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.53136/97912218091901 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 3 Subjects: – SubjectFull: Logistic regression analysis Type: general – SubjectFull: Distraction Type: general – SubjectFull: In-vehicle computing Type: general – SubjectFull: Distracted driving Type: general – SubjectFull: Older automobile drivers Type: general – SubjectFull: Human error Type: general – SubjectFull: Drunk driving Type: general – SubjectFull: Electronic equipment Type: general – SubjectFull: Iowa Type: general Titles: – TitleFull: Examining in-vehicle distraction sources in relation to crashes using a Bayesian Multinomial Logit model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kutela, B. – PersonEntity: Name: NameFull: Kidando, E. – PersonEntity: Name: NameFull: Kitali, A. E. – PersonEntity: Name: NameFull: Mwende, S. – PersonEntity: Name: NameFull: Novat, N. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 18245463 Numbering: – Type: volume Value: 61 Titles: – TitleFull: Advances in Transportation Studies Type: main |
| ResultId | 1 |
