Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data

TL;DR

This paper presents an LSTM neural network model that predicts road surface friction using historical data, improving accuracy over baseline models and analyzing factors like water thickness and temperature for better traffic safety management.

Contribution

The study introduces a novel LSTM-based data-driven model for road friction prediction that considers time-series features and influences of environmental factors, outperforming existing methods.

Findings

LSTM model achieved the lowest predictive error among tested models.

Adding environmental factors like water thickness and temperature improved accuracy.

Analysis of time-lags and prediction intervals informed optimal model configurations.

Abstract

Road surface friction significantly impacts traffic safety and mobility. A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity. Most related previous studies are laboratory-based methods that are difficult for practical implementation. Moreover, in other data-driven methods, the demonstrated time-series features of road surface conditions have not been considered. This study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model based on historical data. The proposed prediction model outperformed the other baseline models in terms of the lowest value of predictive performance measurements. The influence of the number of time-lags and the predicting time interval on predictive accuracy was analyzed. In addition, the influence of adding road surface water thickness, road surface temperature and air temperature on predictive accuracy also were investigated. The findings of this study can support road maintenance strategy development and decision making, thus mitigating the impact of inclement road conditions on traffic mobility and safety. Future work includes a modified LSTM-based prediction model development by accommodating flexible time intervals between time-lags.