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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 01976729 |
| DOI: | 10.1155/atr/3624178 |
