Weather conditions can affect the probability of road accidents. However, the effect can be different, depending on the vehicle type, the type of collision or road characteristics at the location of the accident. The aim of this study is to quantify the combined effects of traffic volume and different meteorological parameters on hourly probabilities of different accident types using generalized additive models. These models are based on police reports, which are available at the level of administrative districts, as well as reanalysis data and radar-based precipitation. Using tensor product bases, we model non-linear relationships and combined effects of different meteorological parameters. We evaluate the increase in relative risk of different accident types in case of precipitation, sun glare and high wind speeds.
The largest effect of snow is found in case of single-truck accidents, while rain has a larger effect on single-car accidents. Precipitation particularly increases the relative risk of run-off-road accidents, as well as accidents at curves and descending road sections. A increasing effect of sun glare on accident probability was found in case of multi-car accidents, in particular in case of rear-end crashes. High wind speeds increase the risk of single-truck accidents and, for all vehicle types, the risk of collisions with objects blown on the road. A comparison of the predictive skill of models with and without meteorological variables shows an improvement of scores of up to 24%. This makes the models suitable for applications in real-time traffic management or impact-based warning systems and have the potential to improve risk perception and behavior of warning recipients.
How to cite: Becker, N., Rust, H. W., and Ulbrich, U.: Weather impacts on different types of road accidents, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-455, https://doi.org/10.5194/ems2022-455, 2022.
