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. Laboratory-based methods were used in most previous studies related to road surface friction prediction model development which are difficult for practical implementations. Moreover, for the existing studies about data-driven method development, the time-series features of road surface friction have not been considered. Thus, to utilize the time-series features of road surface friction for predictive performance improvements, this study employed a Long-Short Term Memory (LSTM) neural network to develop a data-driven road surface friction prediction model. According to the experiment results, the proposed prediction model outperformed the other baseline models in terms of three metrics. The impacts of the number of time-lags, the predicting time interval, and adding other relative variables as training inputs on predictive accuracy were investigated in this research. The findings of this study can support road maintenance strategy development, especially in winter seasons, thus mitigating the impact of inclement road conditions on traffic mobility and safety.

摘要

路面摩擦显着影响交通安全和机动性。精确的路面摩擦预测模型有助于减轻恶劣路况对交通安全、服务水平、交通机动性、燃油效率和持续经济生产力的影响。大多数以前与路面摩擦预测模型开发相关的研究都使用了基于实验室的方法，这些研究难以实际实施。此外，对于现有的数据驱动方法开发研究，还没有考虑路面摩擦的时间序列特征。因此，为了利用路面摩擦的时间序列特征来提高预测性能，本研究采用长短期记忆 (LSTM) 神经网络来开发数据驱动的路面摩擦预测模型。根据实验结果，所提出的预测模型在三个指标方面优于其他基线模型。本研究调查了时滞数、预测时间间隔以及添加其他相关变量作为训练输入对预测准确性的影响。这项研究的结果可以支持道路维护策略的制定，尤其是在冬季，从而减轻恶劣路况对交通流动性和安全性的影响。