## 8 Discussion and Future Directions
From the perspective of design research, it is beneficial to introduce the actual procedure for the solution by which other researchers may be inspired when using other algorithms, methods, or approaches to the solution [52]. Even in association with other views of using parametric or nonparametric approaches and their suitability for certain conditions introduced in Section 2, the authors would also like to emphasize the unique particularities associated with the performed research:  
* Available historical traffic data was based on data merger from various sensors (e.g., FCD data can be less accurate than data from the inductive-loop sensor in the same segment). * Available historical traffic data concerning traffic measurement covered 66% of all road segments in the Czech Republic, i.e., 37,002 km (excluding roads with low rates of vehicle passage). * The designed model focuses on a long-term perspective while predicting the speed within days and weeks and is thus a macro-spatial and temporal prediction model. * The designed model is suitable for (car) navigation application and is of a supplementary nature for route calculation, see Section 7. The stated contextual particularities differ significantly from the contextual particularities of models introduced in articles set forth in Section 2. Based on the aforementioned, we can say that, from a long-term perspective and considering the vastness of the territory, ensemble models based on data-driven statistical and machine learning approaches can reach very good results similarly to using deep learning approaches. The achieved good results of the prediction model can be explained by the following facts:  
* Average speed was predicted for a given hour of the day. * Night hours (from 10.00 p.m. to 5.00 a.m., see Section 5.2) were excluded,
* The model considered only segments with no reported traffic restrictions. * Data were available for the model just right before the start of the testing period. The inclusion of data concerning weather (forecast) can be considered as a logical step as the weather affects many aspects of road conditions and safety and is reflected in traffic speed and the values associated therewith (e.g., flow, density). The aforementioned is supported by the research conducted by the authors of [49] and [53], in which the designed models indicated the benefits of implementing weather data into traffic state prediction. The authors of [39] used a specific ensemble model based on deep belief networks for the improvement of traffic flow prediction, which indicated that this model, which used weather data from a short-term perspective, provided better results than models based solely on historical traffic data. However, this study did not provide a long-term prediction in days and within various categories of roads as in our case, and instead only focused on 47 selected freeways in California. Inductive-loop sensors serve as data sources rather than FCD primarily and secondarily merged data from other sensors. This is also associated with the number of measurement sessions for the individual segments, which may not be regular. All of this can affect the success rate of using weather data for the improvement of traffic state prediction. Similarly, the authors of [54] found that using deep learning-based approaches and ensemble learning while involving weather data enabled very good predictions, especially in hours. However, weather data has not been implemented successfully in the aforementioned prediction model. Similarly to the results found by the authors of [55], no positive impact on the improvement of parametric models prediction was detected with respect to the majority of weather events. Precipitation was as exception. However, due to the fact that, based on our data exploration, there was a significant dispersion between the minimum and maximum speed, there is still hope that there are actually other explanatory variables, such as weather data, and that the model can still be improved.