Modeling hourly weather-related road traffic variations for different vehicle types in Germany

Abstract Weather has a substantial influence on people’s travel behavior. In this study we analyze if meteorological variables can improve predictions of hourly traffic counts at 1400 stations federal roads and highways in Germany. Motorbikes, cars, vans trucks are distinguished. It is evaluated how far the mean squared error Poisson regression models for reduced by using precipitation, temperature, cloud cover wind speed data. shown that particular motorbike strongly weather-dependent. On up to 60% with predictor variables, when compared without variables. A detailed analysis reveals non-linear relationships between counts. Car be specifically sensitive weather touristic regions like seaside resorts nature parks. The findings allow several potential applications improvements route planning navigation systems, implementations management day-ahead visitor numbers areas or usage road crash modelling.