# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 4 Results
### Validation Results  
Through multiple runs, the optimization process was able to determine the ideal calibration parameter values, greatly minimizing the differences between the simulation and the real world. To validate the calibrated parameter values across different traffic patterns and volumes, this study included a validation phase. The calibration was conducted using datasets collected during afternoon peak hours, from 1 PM to 4 PM. Validation was performed using separate datasets collected during morning peak hours, from 7 AM to 9 AM. The morning peak dataset was selected to test the model's robustness under different  
Figure 7: Average speed comparison of ground truth and simulation results before and after the calibration process. traffic conditions. Morning traffic typically includes higher commuter volumes, different congestion patterns, and potentially more variable speeds compared to afternoon peak hours. This variation provides a challenging scenario to evaluate the model's generalizability and validate its performance across diverse traffic conditions. The VISSIM model was first run with the calibration parameters set to their default values, and the total error corresponding to these values was computed. Subsequently, the parameters were adjusted to the calibrated values, and the total error for these calibrated parameters was recorded. The differences between the default model and the calibrated model in the validation process are shown in Table 5.  
In the validation phase, a significant reduction in mean error by 32.68% from 366.89 to 246.97 m-min was achieved, surpassing the improvement noted during calibration. This suggests that the calibrated parameters effectively enhanced the average accuracy of the model even under varying traffic conditions. However, the median error in the validation results increased by 15.79%, indicating that while some scenarios saw substantial improvements, typical cases represented by the median did not benefit uniformly. This tension between metrics indicates that while the calibration effectively addressed worst-case scenarios, it may have introduced trade-offs in handling typical or best-case scenarios. These findings highlight the potential need for future refinements to better balance improvements across the entire error distribution, ensuring consistent benefits for all traffic conditions. The reduction in the standard deviation during validation was profound, dropping by 62.74%, compared to a more modest 5.88% during calibration. This marked decrease underscores a significant enhancement in error consistency, suggesting that the variability in model performance was substantially narrowed after applying the calibrated parameters. The maximum error reduction during validation was also notable, decreasing by 71.56%, a substantial improvement that highlights the calibration's effectiveness in reducing worst-case scenario errors. Conversely, the minimum error and the 25 th percentile error showed deteriorations, increasing by 27.23% and 264.11%, respectively. These results indicate that while extreme errors were better controlled, the best-case scenario performance worsened, and lower quartile errors became more prevalent. Skewness and kurtosis changes during validation showed a trend toward a more symmetric and flatter error distribution, contrasting with the increases observed during calibration. This suggests that while the calibration process helped reduce outliers, it also shifted the overall distribution of errors. The validation results affirm the effectiveness of the calibration process in enhancing model accuracy and consistency across different testing conditions. However, the increase in median and 25 th percentile errors alongside the variability in skewness and kurtosis across different datasets point to areas where further calibration refinement could be beneficial. This highlights the importance of tailoring calibration strategies to accommodate variations in traffic patterns and conditions to ensure the model's robustness and reliability.