Weighted aggregation in the domain of crowd-based road condition monitoring

This paper focuses on crowd-based road condition monitoring using smart devices, such as smartphones and evaluates different strategies for aggregating multiple measurements (arithmetic mean and weighted means using R and RMSE) for predicting the longitudinal road roughness. The results con®rm that aggregating predictions from single drives leads to a higher model performance. This has been expected and con®rms the intuition. The overall R could be increased from 0.69 to 0.75 on average and the NRMSE could be decreased from 9% to 8% on average. However, contrary to the intuition, the results show that weighted aggregations of single predictions should be avoided, which is consistent with previous ®ndings in other domains, such as ®nancial forecasting.