  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.
