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Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.

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Title: Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.
Authors: Tang, Tianpei1,2 (AUTHOR), Yuan, Meining1 (AUTHOR), Zhang, Nan3 (AUTHOR), Wang, Hua1,4 (AUTHOR) hwang191901@gmail.com, Guo, Yuntao5 (AUTHOR), Shi, Quan1 (AUTHOR)
Source: Transportation Research: Part F. Feb2025, Vol. 109, p1049-1062. 14p.
Subjects: Artificial neural networks, Planned behavior theory, Traffic violations, Econometric models, Road safety measures
Abstract: • An innovative six-step analytical framework integrating ANNs was developed. • Determinants of various red-light running (RLR) groups were studied among e-bikers. • A network weight-based approach quantified the impacts of influencing factors. • Our model surpasses existing models in understanding e-bikers' RLR behavior. Understanding red-light running (RLR) behavior among electric bikers (e-bikers) is critical for addressing the high accident rates associated with this behavior. Traditional analytical methods, such as econometric modeling, often fail to capture the non-linear dynamics of traffic violations, limiting their effectiveness in exploring the complexity of such behaviors. Conversely, Artificial Neural Networks (ANNs) excel in handling non-linear relationships but lack interpretability, making their application in decision-making challenging. This study introduces an innovative six-step analytical framework that integrates hybrid ANNs with the Theory of Planned Behavior (TPB). This integration utilizes a network weight-based approach to quantify the impacts of influencing factors within the ANNs. The results demonstrate that this hybrid framework not only enhances predictive accuracy but also provides a deeper understanding of the motivational drivers behind e-bikers' RLR behavior. The study identifies significant behavioral heterogeneities across e-biker groups, emphasizing the need for targeted interventions. Based on these findings, a multi-faceted intervention strategy is proposed, combining educational campaigns, regulatory measures, and community engagement efforts tailored to distinct behavioral profiles. This research provides a robust foundation for developing safety improvement programs that aim to reduce e-biker accidents and improve overall road safety. [ABSTRACT FROM AUTHOR]
Copyright of Transportation Research: Part F is the property of Pergamon Press - An Imprint of Elsevier Science 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 Text:
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DbLabel: Engineering Source
An: 183337849
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.
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  Data: <searchLink fieldCode="JN" term="%22Transportation+Research%3A+Part+F%22">Transportation Research: Part F</searchLink>. Feb2025, Vol. 109, p1049-1062. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Planned+behavior+theory%22">Planned behavior theory</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+violations%22">Traffic violations</searchLink><br /><searchLink fieldCode="DE" term="%22Econometric+models%22">Econometric models</searchLink><br /><searchLink fieldCode="DE" term="%22Road+safety+measures%22">Road safety measures</searchLink>
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  Data: • An innovative six-step analytical framework integrating ANNs was developed. • Determinants of various red-light running (RLR) groups were studied among e-bikers. • A network weight-based approach quantified the impacts of influencing factors. • Our model surpasses existing models in understanding e-bikers' RLR behavior. Understanding red-light running (RLR) behavior among electric bikers (e-bikers) is critical for addressing the high accident rates associated with this behavior. Traditional analytical methods, such as econometric modeling, often fail to capture the non-linear dynamics of traffic violations, limiting their effectiveness in exploring the complexity of such behaviors. Conversely, Artificial Neural Networks (ANNs) excel in handling non-linear relationships but lack interpretability, making their application in decision-making challenging. This study introduces an innovative six-step analytical framework that integrates hybrid ANNs with the Theory of Planned Behavior (TPB). This integration utilizes a network weight-based approach to quantify the impacts of influencing factors within the ANNs. The results demonstrate that this hybrid framework not only enhances predictive accuracy but also provides a deeper understanding of the motivational drivers behind e-bikers' RLR behavior. The study identifies significant behavioral heterogeneities across e-biker groups, emphasizing the need for targeted interventions. Based on these findings, a multi-faceted intervention strategy is proposed, combining educational campaigns, regulatory measures, and community engagement efforts tailored to distinct behavioral profiles. This research provides a robust foundation for developing safety improvement programs that aim to reduce e-biker accidents and improve overall road safety. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Transportation Research: Part F is the property of Pergamon Press - An Imprint of Elsevier Science 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=183337849
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        Value: 10.1016/j.trf.2025.01.027
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        Text: English
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      – SubjectFull: Traffic violations
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      – SubjectFull: Econometric models
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      – SubjectFull: Road safety measures
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      – TitleFull: Integrating behavioral theory and ANNs for understanding electric bikers' red-light running behavior.
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            NameFull: Tang, Tianpei
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            NameFull: Yuan, Meining
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            NameFull: Zhang, Nan
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              M: 02
              Text: Feb2025
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