Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

在恶劣天气条件下准确预测网络级交通参数可以极大地帮助许多交通应用。降雨往往会对驾驶行为和交通网络性能产生可量化的影响。这种影响通常针对小型道路网络上的低分辨率降雨数据进行研究，而本研究则在大型交通网络和高分辨率降雨雷达图像的背景下对其进行研究。首先，分析了降雨强度对全天和不同道路类别的交通性能的影响。接下来，研究包括降雨信息是否可以提高最先进的交通预测方法的预测精度。数值结果表明，降雨对交通的影响因降雨强度不同以及一天中的不同时间和一周中的天数而异。结果还表明，将降雨数据纳入预测模型可提高其整体性能。具有降雨数据的模型的平均绝对百分比误差 (MAPE) 平均减少了 4.5%。还进行了降采样降雨数据的实验，得出的结论是，合并更高分辨率的天气数据确实会提高交通预测模型的性能。