## 6 Validation of Designed Model  
The current version of the prediction model (i.e., not including the meteorological values) was tested using all the data currently available. The period from 7 January to 14 June 2020 was used as a training set (data in the model history were only used for learning). Prediction was tested using the week from 15 June (Monday) to 21 June (Sunday) as the last week in June was affected by the upcoming start of summer holidays and by people going away. Data collected after 22 June have not been used so far. We need to emphasize that a proper testing of the model was undertaken with the testing period and did not include the history used for the model training. Furthermore, any nonrepresentative values were automatically eliminated in advance from the data used for model training. This mainly comprised various traffic restrictions (see Section 3.2) and the period of state of emergency, or the period of the strictest ordered quarantine in association with COVID-19, which caused a decrease in traffic density from 12 March to 26 April 2020. The testing methodology was stated as follows:  
* No testing was performed with respect to prediction for night from 10.00 p.m. to 5.00 a.m.
* Prediction could only be calculated for hours where there was at least one traffic measurement available for the given _TMC segment_. * The main metric was the absolute difference between the predicted speed and the measured average speed (prediction error) for a given hour. We used the mean absolute error (\(MAE\)) metric to evaluate the absolute error between the prediction and real values for a given road segment. \(MAE\) was defined as:  
\[MAE=\frac{\sum_{i=1}^{n_{p}}|S_{i}-P_{i}|}{n_{p}} \tag{7}\]  
where \(S_{i}\) means the real average speed for hour \(i\), \(P_{i}\) denotes the speed prediction for the same hour, and \(n_{p}\) is the number of samples in the test set. Summary results of testing of all 20,504 _TMC segments_ is as follows:  
* \(MAE\) of all the individual predictions (across all _TMC segments_) was 4.67 km/h with a standard deviation of 1.642 km/h. * Average of \(MAE\) of the individual _TMC segments_ (with at least one prediction) was 4.91 km/h, with a standard deviation of 1.996 km/h. * In 90% of cases, the average error per all _TMC segments_ was below 10.80 km/h. * In 95% of cases, the average error per all _TMC segments_ was below 14.01 km/h. * In 87.93% of all predictions, the average absolute prediction error was below 10 km/h. * The average maximum prediction error per all _TMC segments_ was below 24.02 km/h. Appendix B contains a text file with a complete overview of all segments and their testing results.Table 2 illustrates a selection of _TMC segments_ from Appendix B, which are introduced in more detail in Figures 13-20. In Table 2, column Count contains the number of hours for which testing could have been performed. The testing week spanned 168 h, but prediction was not tested at night from 10.00 p.m. to 5.00 a.m. (other factors than traffic density influence achieved speed). Thus, the maximum number of predictions was 119. A lower number indicated that, for some hours, there were no speed measurements available. Column MAE contains the mean absolute error (difference of the predicted speed from the average measured speed one). Columns Err90 pt and Err95 pt contain the maximum value of absolute difference in 90% and 95% of cases, respectively. Column ErrMax contains the maximum value of absolute difference. Column Low contains the number of cases where the prediction difference was below 10 km/h. In column Low %, this number of cases is expressed relatively to the value contained in column Count. Figures 13-20 contain graphic expressions of the average measured speed (blue), predicted speed (red), _FreeFlowSpeed_ (violet), and prediction error (green) for the selected road segments from Table 2 for each hour of the testing week from Monday, 15 June, to Sunday, 21 June. For illustrative purposes, prediction according to the CBR model is in grey.