# Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm
###### Abstract  
This study introduces a data-driven approach to calibrate microscopic traffic simulation models like VISSIM using high-resolution trajectory data, aiming to improve simulation accuracy and fidelity. The study focuses on a highway segment of NJ-3 and NJ-495 in Hudson County, New Jersey, selected as a case study for its high traffic volume and strategic significance. Trajectory data from 338 connected vehicles, sourced from the Wejo dataset, a global provider of anonymized, high-resolution vehicle movement data, along with traffic volume data from Remote Traffic Microwave Sensors (RTMS), served as inputs. The trajectories produced by the simulation model were compared to the ground truth to measure discrepancies. By adjusting driving behavior parameters (e.g., car-following and lane-changing behaviors) and other factors (e.g., desire speed), a Genetic Algorithm was adopted to minimize these differences. Results showed significant improvements, including a 14.19% reduction in mean error, an 18.27% reduction in median error, and a 22.57% reduction in the 75 th percentile error during calibration. In the validation phase, the calibrated parameters yielded a 32.68% reduction in mean error, demonstrating the framework's robustness. This study presents a scalable calibration framework using connected vehicle data, providing tools for accurate simulation, real-time traffic management, and infrastructure planning. Article  
16 January 202529 January  
Numerous parameters must be calibrated in traffic microsimulation software to fine-tune traffic and driver behaviors. However, identifying optimal values for these parameters is a complex task. Additionally, traffic engineering simulations are usually confined to specific areas to save time and costs, even though the designated areas are sometimes influenced by external traffic. For instance, congestion on a highway may cause backups in downstream traffic, which is miles away. In such cases, simply expanding the simulation's spatial coverage may not always be the solution. The necessity to solve issues by calibrating traffic simulation models in scenarios with significant traffic variations and connected vehicle data served as the motivation for this research. Conventional methods for calibrating traffic simulation models often rely on giving predetermined parameter values or simple numerical ranges for particular driving behaviors or network configurations. Although these methods are straightforward, they are not adaptable enough to take into consideration the particularities of different networks, such as geometry, traffic patterns, and driver behavior. When used in a variety of real-world scenarios, this may result in inadequate calibration outcomes. A one-size-fits-all strategy is unlikely to produce the required degree of precision in various scenarios since the value of each calibration parameter is fundamentally dependent on the particular network design and driving behavior characteristics. In order to overcome these constraints, this paper presents a novel calibration framework that adapts parameter changes to the particular circumstances of the network being represented. This method allows traffic simulation models to be calibrated particularly for the target network. Although this study demonstrates the suggested framework on a particular highway segment, it is intended to be flexible and applicable to any network. Because of this, it is a flexible and reliable tool for improving the accuracy of traffic simulation models in a variety of applications, beyond the constraints of preset parameter values, and enabling context-sensitive optimization. To increase the accuracy of microscopic traffic simulation models like VISSIM, this study presents a calibration framework that combines high-resolution connected vehicle trajectory data with traditional traffic volume data. As VISSIM is a popular tool in traffic engineering and offers a stable platform for simulating sophisticated traffic systems, it was selected for this investigation.