Forecasting the pavement icing in traffic zone using long short-term memory

Abstract

In winter, driving is a difficult due to road icing. It is one of the most unfavorable weather conditions that endangers traffic safety. We gathered data on pavement temperature, freezing point temperature, friction coefficient, pavement water film thickness, ice content, and pavement condition using sensors. Those data are feed into Long Short-Term Memory (LSTM) to predict road icing in the city. Here the primary issue is forecasting the pavement icing of the traffic zone. Our work is an endeavor to use the deep learning method on LSTM to forecast pavement icing on Ji’nan in China. Those Sequential data of pavement icing can process and memorize by LSTM at a specific time. Finally, research results indicate that the performance of the model is very precise. With LSTM model parameters’ help, the sequential data on road icing prediction can also predict the pavement temperature.