## 8 Discussion and Future Directions  
Generally speaking, there is a number of specific conditions (e.g., social and technological aspects of the application environment, local availability, and quality of data inputs) which restrict the defined goals of the designed model focused on traffic prediction and related indicators. To a certain extent, contextual conditions related to research or application affect the success rate of models designed within a different task and a similar problem context where they may not reach as accurate results. This is also associated with the performed validation of the designed solutions. For instance, validation of the designed solution of testing data from a very limited temporal or spatial segment of the road network or in specific geographic conditions of the given region (e.g., region with low precipitation) only provides a framework perspective of suitability of using the specific prediction model in practice. This is how the artefact design issue is also approached by design science [42], which tries to offer paralogy of perspectives and approaches on handling a specific problem context. Experienced professionals then make the final selection of the most purposeful solution, which is not necessarily the most suitable one, for instance, from the perspective of attained prediction error rate or used technology (but the selected solution may be easier to implement in a sociotechnical context). Based on the validation results of the prediction model, we can state that traffic speed is predicted satisfactorily by the model in a long-term perspective. The achieved results may be compared with those found by the authors of [49], who tested the actual model (built-on machine learning) in a mid-sized city. This model achieved approximately 75%  
Figure 21: Conceptual design of integrating the prediction module into navigation. accuracy for a reasonable 10 km/h difference and up to 80% accuracy for a 20 km/h difference. Similarly to our case, historical data were primarily collected as FCD. The multiscale spatiotemporal feature learning network (MSTFLN), introduced by the authors of [19] and tested on three selected motorways and data collected from loop detectors, might serve as another model for framework comparison. The achieved average MAE per given hour is 5.72 km/h, which we consider as comparable with the prediction model introduced in this article. Similar results can also be achieved based on the MAE described by the authors of [50]. The CNN-LSTM model of the authors consists of convolutional neural networks and long short-term memory models, and achieved similar results with respect to real-world dataset of Hong Kong as MSTFLN (the MAE for hourly prediction was 5.4 km/h). Nevertheless, while the prediction of MSTFLN is in hours, the prediction of the CNN-LSTM model is restricted to dozens of minutes. Very good results for the short-term prediction were found by the authors of [51], who presented the attention-based LSTM (ATT-LSTM) model for short-term traffic speed forecasting. The average MAE of their ATT-LSTM model was 3.26 km/h for 15 min prediction. As for the stated comparison, the restriction with respect to the difference of the used datasets must be taken into consideration. Despite this restriction, based on validation, we consider the prediction model provided in this article as purposeful according to design science (i.e., applicable in practice and achieving reasonable results). Nevertheless, it should be emphasized that the solution presented in this article is for long-term prediction and serves mainly as a supplementary solution to enrich traffic information for better route calculation in car navigation systems. There were also some restrictions in the research introduced in this article, which were caused by the problem context in which the designed solution should be included within the applied research project.