Traffic accident duration prediction using multi-mode data and ensemble deep learning

Chen, Jiaona; Tao, Weijun; Jing, Zhang; Wang, Peng; Jin, Yinli

doi:10.1016/j.heliyon.2024.e25957

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e25957

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Traffic accident duration prediction using multi-mode data and ensemble deep learning

Jiaona Chen,

Weijun Tao,

Zhang Jing

et al.

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Cited by 2 publications

References 29 publications

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Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Hu,

Bai,

Yang

et al. 2025

Expert Systems with Applications

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Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Hu,

Bai,

Yang

et al. 2025

Expert Systems with Applications

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A Novel Accident Duration Prediction Method Based on a Conditional Table Generative Adversarial Network and Transformer

Wang,

Zhai,

Cao

et al. 2024

Sustainability

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The accurate duration prediction of road traffic accident is crucial for ensuring the safe and efficiency of transportation within social road networks. Such predictive capabilities provide significant support for informed decision-making by transportation administrators while also offering new technological support for the sustainable development of modern road networks. This study introduced a novel predictive model for road traffic accident duration, integrating a Conditional Table Generative Adversarial Network (CTGAN) with a transformer architecture. We initially utilized CTGAN to augment and refine the historical accident dataset. Subsequently, we implemented a wavelet denoising technique to cleanse the expanded dataset. The core of our model lies in the application of the transformer mechanism, which was trained to forecast the accident duration with high precision. To prove the effectiveness of our proposed model, a series of comparative experiments were designed and executed. The experimental results show that the prediction error of CTGAN-Tr for accident duration in the accident area could reach below 0.8. Compared with other models, the MAE of CTGAN-Tr was reduced by 0.31 compared with GRU, and the correlation coefficient was increased by 0.2 compared with TCN. At the same time, the model can show excellent performance in the other two accident areas. The results of these experiments not only substantiate the performance of our model but also demonstrate its robustness and generalizability when applied to traffic accident data from other regions.

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Traffic accident duration prediction using multi-mode data and ensemble deep learning

Cited by 2 publications

References 29 publications

Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

A Novel Accident Duration Prediction Method Based on a Conditional Table Generative Adversarial Network and Transformer

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