A method for predicting road surface condition based on Multi-LSTM using time-spatial data in winter road environment

Abstract

In snowy and cold regions, snowfall leads to changes in road surface conditions, which can become a factor in traffic accidents. If road administrators can anticipate the road surface conditions on the routes under their management, road safety can be improved. This paper proposesa method to predict road surface conditions using Multi-LSTM, leveraging past road surface and weather data. Specifically, the approach first constructs spatiotemporal data sequences from accumulated road surface and weather data. Next, a prediction model based on Multi-LSTM is developed, integrating temporal and spatial sequence predictions to forecast road surface conditions. Finally, the proposed model predicts road surface conditions using the constructed spatiotemporal data as input. At the conclusion of this paper, experiments are conducted using actual observation data from the Soya region of Hokkaido, and the effectivenessof the proposed method is validated through comparisons with six alternative methods.