# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 5 Discussion and Conclusions  
This study effectively illustrated how a VISSIM microsimulation model can be calibrated and its ability to simulate a real-world highway segment (a section of the NJ-3 and NJ-495 eastbound highways in Hudson County, New Jersey) improved by using connected vehicle data from the Wejo dataset. Our goal was to apply a GA to connected vehicle data from the Wejo dataset in order to decrease trajectory inconsistencies in a VISSIM microsimulation model. The objective of this work was to increase simulation accuracy and model fidelity by utilizing high-resolution trajectory data and iterative optimization. The study's findings, which show notable error reductions across important measures, highlight the value of combining GAs for calibration with connected vehicle trajectory data. This study emphasizes the benefits of VISSIM's compliance with high-resolution trajectory data and distributed computing architecture when comparing the findings to other research, such as [PERSON] et al. [8], which also used GAs but concentrated on AIMSUN software (version not specified). Likewise, the strategy employed here provides more scalability and flexibility for a variety of traffic networks than approaches such as multi-point distribution calibration [29] or Bayesian neural networks [13]. This approach can be used to increase simulation fidelity in both urban and highway settings, as evidenced by the observed improvement in trajectory alignment, especially in the mean and median error reductions. The accuracy of the model was significantly improved by this calibration, as shown by the drop in mean and median errors as well as a general decrease in the variability of error metrics after calibration. The mean error decreased by 14.19%, while the median error saw an 18.27% reduction, showcasing the effectiveness of the calibration process in improving overall accuracy. The results underscore the importance of accurate parameter tuning in traffic simulation models and illustrate the benefit of leveraging connected vehicle data. By offering a unique calibration framework that goes beyond conventional techniques that rely on fixed parameter values or limited data sources, this study advances the discipline. This technique presents a dynamic and data-driven solution that may adjust to the particular features of any traffic network by utilizing connected vehicle trajectory data. By making traffic simulation models more realistic and applicable, this invention makes it possible to analyze and prepare for contemporary transportation systems with greater accuracy. Particularly notable was the rapid convergence to optimal parameter values within the first quarter of the iterations, highlighting the efficiency of the GA in finding practical solutions quickly. However, increased skewness and kurtosis post-calibration suggest that while the average errors were reduced, the variability, in some cases, increased. The increases in skewness and kurtosis indicate that while the overall error is reduced, certain extreme cases still exist, which could point to specific traffic behaviors not fully captured by the calibration. This suggests potential areas for further refinement, such as incorporating more detailed behavioral models or data sources. The suggested strategy has a few limitations despite its proven efficacy. First, even though the GA effectively reduced the number of discrepancies in this investigation,other optimization methods or hybrid approaches might produce even better outcomes. Furthermore, the framework does not yet take into consideration extremely specific driving behaviors or external variables that could affect calibration results, such as weather or incident impacts. By extending the calibration to multi-network settings, adding more data sources, and investigating sophisticated optimization strategies, future work will overcome these constraints. The calibration procedure might also be improved by using a multi-criteria objective function or a more complicated objective function.