ABCNet: A comprehensive highway visibility prediction model based on attention, Bi-LSTM and CNN

Wen Li; Xuekun Yang; Guowu Yuan; Dan Xu; Wen Li; Xuekun Yang; Guowu Yuan; Dan Xu

doi:10.3934/mbe.2024194

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4397-4420. doi: 10.3934/mbe.2024194

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ABCNet: A comprehensive highway visibility prediction model based on attention, Bi-LSTM and CNN

1.
School of Information Science and Engineering, Yunnan University, Kunming 650504, China
2.
National Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants Co., Ltd., Kunming 650299, China
3.
Yunnan Key Laboratory of Digital Communications, Kunming 650504, China

Academic Editor: Jorge Bernardino

Received: 26 December 2023 Revised: 17 February 2024 Accepted: 21 February 2024 Published: 27 February 2024

Meteorological disasters along highways significantly reduce road traffic efficiency. Low visibility caused by heavy fog is a severe meteorological disaster that greatly increases highway traffic accidents. Accurately predicting highway visibility and taking timely response measures can reduce the impact of meteorological disasters and improve traffic safety. We proposed an Attention-based BiLSTM-CNN (ABCNet) model, which synergized attention mechanisms with BiLSTM and CNN technologies to forecast atmospheric visibility more accurately. First, the Bi-LSTM module processed information both forward and backward, capturing intricate temporal dependencies in the model. Second, the multi-head attention mechanism following the Bi-LSTM distilled and prioritized salient features from multiple aspects of the sequence data. Third, the CNN module recognized local spatial features, and a singular attention mechanism refined the feature map after the CNN module, further enhancing the model's accuracy and predictive capability. Experiments showed that the model was accurate, effective, and significantly advanced compared to conventional models. It could fully extract the spatiotemporal characteristics of meteorological elements. The model was integrated into practical systems with positive results. Additionally, this study provides a self-collected meteorological dataset for highways in high-altitude mountainous areas.
- traffic,
- meteorological disaster,
- atmospheric visibility,
- forecasting,
- deep learning
Citation: Wen Li, Xuekun Yang, Guowu Yuan, Dan Xu. ABCNet: A comprehensive highway visibility prediction model based on attention, Bi-LSTM and CNN[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4397-4420. doi: 10.3934/mbe.2024194

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Abstract

Meteorological disasters along highways significantly reduce road traffic efficiency. Low visibility caused by heavy fog is a severe meteorological disaster that greatly increases highway traffic accidents. Accurately predicting highway visibility and taking timely response measures can reduce the impact of meteorological disasters and improve traffic safety. We proposed an Attention-based BiLSTM-CNN (ABCNet) model, which synergized attention mechanisms with BiLSTM and CNN technologies to forecast atmospheric visibility more accurately. First, the Bi-LSTM module processed information both forward and backward, capturing intricate temporal dependencies in the model. Second, the multi-head attention mechanism following the Bi-LSTM distilled and prioritized salient features from multiple aspects of the sequence data. Third, the CNN module recognized local spatial features, and a singular attention mechanism refined the feature map after the CNN module, further enhancing the model's accuracy and predictive capability. Experiments showed that the model was accurate, effective, and significantly advanced compared to conventional models. It could fully extract the spatiotemporal characteristics of meteorological elements. The model was integrated into practical systems with positive results. Additionally, this study provides a self-collected meteorological dataset for highways in high-altitude mountainous areas.

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