Evaluating the Spatiotemporal Distribution of Hazardous Driving States for Buses: An Investigation on GPS Data With a BiLSTM‐CNN.
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| Title: | Evaluating the Spatiotemporal Distribution of Hazardous Driving States for Buses: An Investigation on GPS Data With a BiLSTM‐CNN. |
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| Authors: | Zhang, Wenhui1 (AUTHOR), Xi, Cong1 (AUTHOR) xiao_htc@nefu.edu.cn, Liu, Wei2 (AUTHOR), Song, Ziwen1 (AUTHOR), Qiao, Xiaotian1 (AUTHOR), Tang, Jinjun (AUTHOR) jinjuntang@csu.edu.cn |
| Source: | Journal of Advanced Transportation. 5/31/2026, Vol. 2026, p1-23. 23p. |
| Subjects: | Geospatial data, Deep learning, Spatiotemporal processes, Bus accidents, Motor vehicle driving, Transportation safety measures, Traffic patterns |
| Abstract: | Enhancing bus safety is critical due to the severe societal and economic impacts of bus‐related crashes. This study investigates hazardous driving behaviors in buses by analyzing high‐frequency GPS data, focusing on kinematic parameters such as speed, acceleration, and steering angle. A hybrid convolutional neural network (CNN), bidirectional long short‐term memory (BiLSTM‐CNN) model, is proposed to identify hazardous states, including overspeed, sharp acceleration/deceleration, and sharp turning. Comparative evaluations with standalone CNN, long short‐term memory (LSTM), and BiLSTM models demonstrate the superior performance of the BiLSTM‐CNN, achieving 93.1% recognition accuracy with rapid convergence. Using the identified hazardous state data, a statistical analysis of their spatiotemporal distribution is conducted by segmenting time periods and road sections. Hotspot road segments for dangerous operating states are identified through a spatial autocorrelation analysis. A spatial econometric model is employed to analyze the significant factors of these hazardous operating states. Spatiotemporal analysis reveals distinct risk patterns: overspeed clusters in peripheral road segments with low traffic density, while sharp acceleration and deceleration peak during off‐peak hours. The study proposes targeted recommendations for early warning systems and safety management of hazardous bus operations. These recommendations aim to provide a reference for enterprises and traffic management departments in supervising bus operating conditions. This research establishes a data‐driven framework for mitigating operational risks and enhancing public transportation safety. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194204413 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Evaluating the Spatiotemporal Distribution of Hazardous Driving States for Buses: An Investigation on GPS Data With a BiLSTM‐CNN. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Wenhui%22">Zhang, Wenhui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xi%2C+Cong%22">Xi, Cong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xiao_htc@nefu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Wei%22">Liu, Wei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Ziwen%22">Song, Ziwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiao%2C+Xiaotian%22">Qiao, Xiaotian</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tang%2C+Jinjun%22">Tang, Jinjun</searchLink> (AUTHOR)<i> jinjuntang@csu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Advanced+Transportation%22">Journal of Advanced Transportation</searchLink>. 5/31/2026, Vol. 2026, p1-23. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink><br /><searchLink fieldCode="DE" term="%22Bus+accidents%22">Bus accidents</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+vehicle+driving%22">Motor vehicle driving</searchLink><br /><searchLink fieldCode="DE" term="%22Transportation+safety+measures%22">Transportation safety measures</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+patterns%22">Traffic patterns</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Enhancing bus safety is critical due to the severe societal and economic impacts of bus‐related crashes. This study investigates hazardous driving behaviors in buses by analyzing high‐frequency GPS data, focusing on kinematic parameters such as speed, acceleration, and steering angle. A hybrid convolutional neural network (CNN), bidirectional long short‐term memory (BiLSTM‐CNN) model, is proposed to identify hazardous states, including overspeed, sharp acceleration/deceleration, and sharp turning. Comparative evaluations with standalone CNN, long short‐term memory (LSTM), and BiLSTM models demonstrate the superior performance of the BiLSTM‐CNN, achieving 93.1% recognition accuracy with rapid convergence. Using the identified hazardous state data, a statistical analysis of their spatiotemporal distribution is conducted by segmenting time periods and road sections. Hotspot road segments for dangerous operating states are identified through a spatial autocorrelation analysis. A spatial econometric model is employed to analyze the significant factors of these hazardous operating states. Spatiotemporal analysis reveals distinct risk patterns: overspeed clusters in peripheral road segments with low traffic density, while sharp acceleration and deceleration peak during off‐peak hours. The study proposes targeted recommendations for early warning systems and safety management of hazardous bus operations. These recommendations aim to provide a reference for enterprises and traffic management departments in supervising bus operating conditions. This research establishes a data‐driven framework for mitigating operational risks and enhancing public transportation safety. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Advanced Transportation is the property of Wiley-Blackwell 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: 10.1155/atr/3624178 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Geospatial data Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Spatiotemporal processes Type: general – SubjectFull: Bus accidents Type: general – SubjectFull: Motor vehicle driving Type: general – SubjectFull: Transportation safety measures Type: general – SubjectFull: Traffic patterns Type: general Titles: – TitleFull: Evaluating the Spatiotemporal Distribution of Hazardous Driving States for Buses: An Investigation on GPS Data With a BiLSTM‐CNN. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, Wenhui – PersonEntity: Name: NameFull: Xi, Cong – PersonEntity: Name: NameFull: Liu, Wei – PersonEntity: Name: NameFull: Song, Ziwen – PersonEntity: Name: NameFull: Qiao, Xiaotian – PersonEntity: Name: NameFull: Tang, Jinjun IsPartOfRelationships: – BibEntity: Dates: – D: 31 M: 05 Text: 5/31/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01976729 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Advanced Transportation Type: main |
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