# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
## 2 Literature Review  
In the field of traffic engineering, the fidelity of simulation models is paramount for predicting and enhancing real-world traffic scenarios. Calibration methodologies have evolved significantly [2], yet the integration of high-resolution data from connected vehicles represents a promising frontier that has yet to be fully exploited. This literature review explores the advances and challenges in the calibration of microscopic traffic simulations, underscoring the potential for dramatically improved accuracy and applicability. These models require accurate adjustments of parameters to reflect real-world conditions. Because of the complexity of these models and the variety of traffic situations and locations, it is not easy to find an optimum solution to calibrate the models. While researchers have made lots of efforts and studies to find a fast, accurate, and inexpensive solution [3], still, none of them can be generalized to all traffic and environmental situations. These studies vary from traditional techniques to advanced algorithms [4; 5], from small road segments [6] to large-scale traffic networks [7], and each has its advantages and challenges. Popular microsimulation software like SUMO and AIMSUN have been used for similar investigations because of their resilience and flexibility. However, VISSIM was selected for this study due to its compliance with automated optimization via the Component Object Model (COM) interface and its capacity to integrate data from connected vehicles. Despite various calibration approaches presented in the literature, a key challenge remains, which is integrating high-resolution, connected vehicle data to improve model accuracy across diverse traffic networks. Recent studies introduced various methods for calibrating traffic models. [PERSON] et al. [8] aimed to calibrate the AIMSUN simulator for a network in Brazil by utilizing a GA to minimize the mean absolute error of the delay time between the simulation and observed data, which were vehicle speed and volume measurement captured in different intersections of the networks. The results showed a significant improvement compared to the default values of parameters in the model. They did not, however, evaluate how well the recommended methods worked in practice. In a study conducted by [PERSON] et al. [9], VISSIM simulation software was calibrated for intersections in Miami, Florida, specifically regarding the degree of saturation. The data collected during the green times, along with queue lengths, were then compared with the real-world data. There were some limitations such as a small sample size and specific intersection geometry design, which can be considered for expanding the calibration applicability. The Bayesian methods for calibrating traffic models were tested in some studies [10; 11; 12]. The application of Bayesian neural networks with heuristic algorithms to predict simulation results without actually running simulations was tested recently [13]. This approach cap tured the optimal parameter values without repeated simulation runs. Physics-informed machine learning has been used to enhance the calibration process and results in some studies [5; 14]. They used a physics-informed machine-learning approach [5] and deep reinforcement learning [14] to calibrate the model. [PERSON] and [PERSON] [4] used the adjoint method to minimize the root mean square error between the simulations and measurements. Some recent studies considered a data-driven calibration approach. By continuously recording data from loop sensors inside the network, [PERSON] et al. [15] dynamically optimized the origin-destination matrix in microscopic traffic simulation. However, the availability and quality of the data heavily affect the performance of this method. While high-resolution data in the calibration of traffic simulators play a pivotal role, capturing accurate data may not be straightforward [16]. [PERSON] et al.