It is known that rainfall leads to a reduction in driving speed. However, the results of various studies are inconsistent regarding the amount of speed reduction. In this study, we combine high-resolution radar-based rainfall estimates for three days with heavy rainfall with driving speeds derived from floating car data on 1.5 million road sections in Germany. Using linear regression models, we investigate the functional relationship between rainfall and driving speeds depending on road section characteristics like speed limit and number of lanes. We find that the speed reduction due to rainfall is higher at road section with higher speed limits and on multi-lane roads. On highway road section with speed limits of 130 km/h, for example, heavy rainfall of more than 8 L/m2 in five minutes leads to an average speed reduction of more than 30%, although estimates at very high rainfall intensities are subject to increased uncertainty due to data sparsity. Cross-validation shows that including rainfall as a predictor for driving speed reduces mean squared errors by up 14% in general and up to 50% in heavy rainfall conditions. Furthermore, rainfall as a continuous variable should be preferred over categorical variables for a parsimonious model. Our results demonstrate that parsimonious, interpretable models combining radar rainfall data with floating car data can capture systematic rainfall-related speed reductions across a wide range of road types. However, the analysis should be interpreted strictly as a descriptive, event-specific study. It does not support generalizable inference across time, seasons, or broader traffic conditions. To make this approach suitable for operational applications such as real-time speed prediction, route planning, and traffic management, larger multi-event datasets and the consideration of effects like weekday structure and diurnal demand patterns are required to better constrain effects under heavy rainfall conditions.
Becker et al. (2026) studied this question.