Actionability is a key aspect of research advances achieved in diverse fields, as it determines whether new developments are useful in practice for expert users. Intelligent Transport Systems (ITS) are among such fields due to the highly applied set of knowledge areas lying at their core, with some of them subject to high user sensitiveness (e.g. autonomous driving, signaling or guiding systems, among others). In this context, certain ITS areas such as traffic forecasting have received so far little attention in regards to the actionability of the outcomes produced by data-based models. Indeed, most studies are devoted to performance assessment, thereby leaving the actionability and usability of traffic predictions as a rarely addressed matter. Likewise, long-term traffic estimation models have been very scarcely tackled in the literature, partly due to the lack of certainty of their estimations which, unless quantified and properly gauged for the application at hand, renders them far less useful than their short-term counterparts. It is well known that in general, uncertainty increases for a data-based model when the prediction horizon grows. It is precisely uncertainty what reduces most the usability of these models, which are designed to ultimately help taking traffic-related decisions. In this paper we propose a set of heuristic metrics that help reducing the uncertainty in long-term traffic estimations, yielding a more informed decision making process for a traffic manager. Our proposed methodology relies on the statistical analysis of the cluster space spanned by the available traffic data, and is intended to provide not only future traffic estimates, but also a set of quantitative measures reflecting their confidence. Results obtained with real traffic data will showcase the augmented information produced by our proposed methodology.