Ice formation along the overhead lines is a potential source of infrastructure failure and transportation disruption. Modelling ice formation over structures is challenging because it may occur at a very local scale, often far away from measuring stations. Experts in the field have empirically defined models of ice accretion under laboratory conditions. However, the significant number and complexity of the geophysical parameters included in these models, poses major hurdles for their application at the national level.
Crosswind is a potential source of road accidents, especially for unloaded trucks. We joined forces with colleagues from the ILT and RWS datalabs to assess how feasible it is to build predictive models capable of identifying risky locations for truck accidents. In this way, it is possible to proactively plan traffic inspections that might reduce the probability of accidents.
Data analysis We combined a given set of locations of truck accidents with road characteristics, traffic intensity, and weather variables.
Short-duration extreme rainfall events have the potential of causing problems with a high-impact for the road infrastructure and its correct operation. This is why national agencies maintaining the road networks are interested in getting a statistical description for severe downpours, so they can manage the current infrastructure and new constructions keeping the chances of being affected by extreme rainfall low. Previous works have tackled this type of analysis providing results for a single point.