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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 …