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A k-nearest text similarity-BiGRU approach for duration prediction of traffic accidents on expressways

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Titel: A k-nearest text similarity-BiGRU approach for duration prediction of traffic accidents on expressways
Autoren: Jiaona Chen, Jin Zhang, Peng Wang, Yinli Jin
Quelle: Discover Applied Sciences, Vol 7, Iss 7, Pp 1-21 (2025)
Verlagsinformationen: Springer, 2025.
Publikationsjahr: 2025
Bestand: LCC:Science (General)
Schlagwörter: Traffic safety, Traffic accident duration, Text similarity, Text vector, BiGRU, Science (General), Q1-390
Beschreibung: Abstract Accurate prediction of traffic accident duration is critical for alleviating congestion and optimizing traffic management. This study proposes a k-nearest text similarity-BiGRU(Bidirectional Gated Recurrent Unit) approach to predict the duration of traffic accidents using textual records. Firstly, text vectors are extracted through a TF-IDF (Term Frequency-Inverse Document Frequency) model to vectorize textual descriptions. Secondly, the text similarity is quantified based on the extracted text vectors. Therefore, the independent variables for predicting accident duration are obtained through a text similarity evaluation, which serves as the feature vector. Finally, BiGRU networks are utilized for the regression prediction of traffic accident duration using the constructed feature vectors. In addition, the proposed approach is evaluated by the RMSE (root mean square error) and MAPE (mean absolute percent error) utilizing survey information gathered from expressways of Shaanxi Province in China. A rigorous analysis of the prediction performances is compared with different regression methods. Experimental results demonstrate that the k-nearest text similarity-BiGRU outperforms other models. Moreover, the RMSE and MAPE of the TF-IDF model based on BiGRU are 47.77 and 43.74%, respectively. In general, the MAPE is much better than that of regression prediction models employed in previous studies. This work validates the feasibility of text-driven feature engineering for predicting the duration of traffic accidents, and provides a robust tool for traffic management systems.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 3004-9261
Relation: https://doaj.org/toc/3004-9261
DOI: 10.1007/s42452-025-07366-7
Zugangs-URL: https://doaj.org/article/36efc89ea6e74a47bc67939e7d9e3f3d
Dokumentencode: edsdoj.36efc89ea6e74a47bc67939e7d9e3f3d
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  Data: <searchLink fieldCode="AR" term="%22Jiaona+Chen%22">Jiaona Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Jin+Zhang%22">Jin Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Peng+Wang%22">Peng Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yinli+Jin%22">Yinli Jin</searchLink>
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  Data: Discover Applied Sciences, Vol 7, Iss 7, Pp 1-21 (2025)
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  Data: <searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+accident+duration%22">Traffic accident duration</searchLink><br /><searchLink fieldCode="DE" term="%22Text+similarity%22">Text similarity</searchLink><br /><searchLink fieldCode="DE" term="%22Text+vector%22">Text vector</searchLink><br /><searchLink fieldCode="DE" term="%22BiGRU%22">BiGRU</searchLink><br /><searchLink fieldCode="DE" term="%22Science+%28General%29%22">Science (General)</searchLink><br /><searchLink fieldCode="DE" term="%22Q1-390%22">Q1-390</searchLink>
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  Data: Abstract Accurate prediction of traffic accident duration is critical for alleviating congestion and optimizing traffic management. This study proposes a k-nearest text similarity-BiGRU(Bidirectional Gated Recurrent Unit) approach to predict the duration of traffic accidents using textual records. Firstly, text vectors are extracted through a TF-IDF (Term Frequency-Inverse Document Frequency) model to vectorize textual descriptions. Secondly, the text similarity is quantified based on the extracted text vectors. Therefore, the independent variables for predicting accident duration are obtained through a text similarity evaluation, which serves as the feature vector. Finally, BiGRU networks are utilized for the regression prediction of traffic accident duration using the constructed feature vectors. In addition, the proposed approach is evaluated by the RMSE (root mean square error) and MAPE (mean absolute percent error) utilizing survey information gathered from expressways of Shaanxi Province in China. A rigorous analysis of the prediction performances is compared with different regression methods. Experimental results demonstrate that the k-nearest text similarity-BiGRU outperforms other models. Moreover, the RMSE and MAPE of the TF-IDF model based on BiGRU are 47.77 and 43.74%, respectively. In general, the MAPE is much better than that of regression prediction models employed in previous studies. This work validates the feasibility of text-driven feature engineering for predicting the duration of traffic accidents, and provides a robust tool for traffic management systems.
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    Subjects:
      – SubjectFull: Traffic safety
        Type: general
      – SubjectFull: Traffic accident duration
        Type: general
      – SubjectFull: Text similarity
        Type: general
      – SubjectFull: Text vector
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      – SubjectFull: BiGRU
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      – TitleFull: A k-nearest text similarity-BiGRU approach for duration prediction of traffic accidents on expressways
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