## 7 Validation of Simulation Results
For percentages above 50 km/h (actual: 61.2%), sim_1 achieved perfect compliance (61.2%), while sim_2 significantly underestimated (48.0%, deviation 13.2%), sim_4 (52.6%), and sim_3 (57.2%) were also below the actual level. Percentage agreement, measured as the inverse of the weighted average of differences in metrics (mean, minimum, and maximum speed, and percentages), was highest for sim_4 (93.6%), followed by sim_2 (92.0%), sim_3 (90.6%), and lowest for sim_1 (89.3%), suggesting that sim_4 best represents the overall distribution of the data, despite larger errors in individual speed predictions. RMSE, which assesses prediction accuracy, was lowest for sim_1 (11.8), confirming its accuracy, while sim_4 had the highest RMSE (13.9), with sim_2 (13.7) and sim_3 (12.1) ranking in the middle. The result of sim_1 stands out in key metrics related to the propensity to exceed limits; however, the lower compliance may be due to deviations in less essential parameters, such as top speed. In contrast, sim_2, despite its high compliance, systematically underestimates speeds, indicating conservative model assumptions. The sim_3 score provides balanced results but does not lead in any category, and sim_4 accurately reproduces the overall traffic profile but loses precision in individual speeds. The analysis of vehicle the speed data from the SD_07 detector also showed varying performance among the four simulations (sim_1, sim_2, sim_3, and sim_4) in replicating actual traffic. The actual average speed was 56.6 km/h, and sim_3 was the closest (56.1 km/h, 0.5 km/h deviation), demonstrating high precision in this key metric. The sim_1 score slightly overestimated the average (57.2 km/h, deviation 0.6 km/h), while sim_2 (55.1 km/h, deviation 1.5 km/h) and sim_4 (55.6 km/h, deviation 1.0 km/h) underestimated it, with the most significant deviation in sim_2. For the percentage of vehicles above 40 km/h (actual: 95.2%), sim_3 achieved the best fit (96.4%, overestimation of 1.2%), while sim_1 overestimated (98.8%), sim_2, and sim_4 underestimated (91.5% for both). For the percentage above 50 km/h (actual: 80.6%), sim_1 came closest (78.2%, an underestimation of 2.4%), while sim_2 was significantly underestimated (67.9%, a deviation of 12.7%), and sim_3 (72.7%) and sim_4 (69.1%) also fell short of the actual level. Percentage agreement, measured as the inverse of the mean of the differences in metrics (mean, minimum, and maximum speed, and percentages), was highest for sim_2 (94.6%), slightly lower for sim_4 (94.5%) and sim_3 (94.4%), and lowest for sim_1 (93.9%), suggesting that sim_2 best represents the overall distribution of the data, despite significant errors in key metrics. RMSE, which assesses prediction accuracy, was lowest for sim_1 (12.2), confirming its accuracy, while sim_4 had the highest RMSE (14.1), and sim_2 (13.8) and sim_3 (12.5) ranked in the middle. Compared to the previous dataset (actual average of 52.1 km/h), the new set shows more dynamic movement (higher speeds and percentages above 40 km/h and 50 km/h). The sim_1 result stands out for its precision in key metrics, but the lower concordance may be due to deviations in less critical parameters. In contrast, sim_2, despite high concordance, systematically underestimates speeds, indicating conservative model assumptions. The sim_3 result offers balanced results, and sim_4 reproduces the overall traffic profile well but loses the precision of individual speeds.