## 8 Discussion and Future Directions
In the future, besides the weather, it would be interesting to see how likely it is for traffic speed to be slowed down by fixed elements, such as public transport stops, pedestrian crossings, and traffic lights extracted from OSM. From the perspective of further development of our designed model and its planned implementation into the specific contextual conditions in the areas of car navigation and fleet and workflow management systems, acquiring longer data history for at least a whole year is a further option. Furthermore, we could try to implement the weather-related data to the longer history. Based on the analysis of data concerning the weather and traffic over a longer period of time, it is possible to find correction coefficients which are generally applicable (e.g., day of the year or weather event) and which could be used in the CBR submodel. Coefficient \(k_{F}\) can be increased or decreased this way (it will be possible to add up penalization and bonus awards):  
* Rain/snow: \(-0.1\)
* Fog: \(-0.1\)
* Black ice (temperature around 0 \({}^{\circ}\)C): \(-0.2\)
* As opposed to that, the period of school holidays can result in a bonus award for this period: \(+0.1\). Further development concerning the used prediction system will also be associated with the issue of obsolescent data history. In fact, it is possible that, in practice, the prediction system will predict speed only for a given hour based on data collected up to a year before the prediction. It is certainly appropriate to consider the possibility of updating data more frequently, ideally up to the form of its online upload. It would be more demanding on the system operation, but offers several advantages:  
* Detection of current changes (e.g., roadworks, opening of new segments). * Detection of traffic accidents. * Possibility to select better historical examples based on the initial development of the daily scheme (e.g., depending on the development of attained speed, the most similar day will be selected from the history). On the other hand, the first two points are ensured by the navigation systems alone as they already use real-time traffic information, so it is not necessary to duplicate this function. **Author Contributions:** Conceptualization, M.S. and Z.S. ; methodology, M.S. and Z.S. ; software, M.S. ; validation, M.S. ; formal analysis, M.S. ; investigation, M.S. and Z.S. ; resources, Z.S. ; data curation, Z.S. ; writing--original draft preparation, M.S. and Z.S. ; writing--review and editing, M.S. and Z.S. ; visualization, M.S. ; project administration, Z.S. ; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors received public financial support for this research from grant TH04010350 administered through the Technology Agency of the Czech Republic. The paper was processed with support from an institutional fund IP400040 for long-term conceptual development of science and research and internal grant F4/1/2019 at the Faculty of Informatics and Statistics, Prague University of Economics and Business. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data used in this study are available on reasonable request from the corresponding author only for research purpose. The data are not publicly available due to contracts between the providers of some datasets and our research team. The data supporting the validation presented in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.4372333](https://doi.org/10.5281/zenodo.4372333)]([https://doi.org/10.5281/zenodo.4372333](https://doi.org/10.5281/zenodo.4372333)), reference number [56]. **Conflicts of Interest:** The authors declare no conflict of interest.