Road network coverage models for cloud-based automotive applications: A case study in the city of munich

We propose a prediction model to forecast the coverage of road networks in vehicle-to-vehicle or vehicle-to-infrastructure (V2X) networks for cloud-based automotive applications. The model is derived from fleet tests in the City of Munich (Germany). It considers the fleet and the road network characteristics by splitting the network into sub-networks and using the fleet's relative mileage on the sub-networks. The correlation of the spatial coverage and the fleet's mileage is analyzed for each sub-network showing that the expected degressive correlation exists. The derived regression model also shows a comparable fit for a data series taking the driving direction into account. Finally, we validated the model's ability to predict the temporal coverage by reducing the considered time intervals and taking the number of observations into account. The results show that the model can be used to predict the availability, the up-to-dateness and the accuracy of extended floating car data (XFCD).