Short-Term Prediction of Highway Visibility Level Based on Attention Mechanism LSTM

<div class="section abstract"><div class="htmlview paragraph">To improve the accuracy and reliability of short-term prediction of highway visibility level in key scenarios such as short duration and fast changing speed, this paper proposes a short-term prediction method for highway visibility level based on attention mechanism LSTM. Firstly, XGBoost and SHAP methods are used to analyze the factors affecting highway visibility, determine the importance ranking of different influencing factors, and select the factors that have a greater impact on visibility as inputs for the visibility level prediction model. Secondly, based on LSTM as the model foundation network and innovative coupling attention mechanism, a visibility level prediction model based on attention mechanism LSTM is constructed, which can dynamically update the correlation between meteorological feature information at each historical time point and the visibility level at the current prediction time, thereby dividing the importance of information and flexibly capturing important information in meteorological changes. The method proposed in the paper was validated using data from 30 meteorological stations in China over the past 5 years, and compared with the commonly used BPNN, LSTM, and XGBoost methods for visibility level prediction. The results showed that the overall prediction accuracy of this method reached 88.8%, with an accuracy improvement of 16.7% -30.8% compared to other methods, indicating good accuracy, and for each visibility level, the prediction accuracy of this method is significantly better than other methods, with good stability. Therefore, this method can effectively predict the visibility level of highways and can be applied in practical systems.</div></div>