Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm

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 75th 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.