Optimization for Case-Specific Traffic Simulation Study

Abstract: Accurate calibration of car-following models in microscopic traffic simulations is essential for reliable traffic analysis, forecasting, and transportation planning. This study presents an optimization framework for calibrating case-specific car-following parameters in microscopic simulations, ensuring that simulation outputs closely replicate observed field data. By leveraging metaheuristic algorithms, such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), this research systematically refines the car-following parameters to enhance the fidelity of simulated traffic dynamics. Metaheuristic algorithms offer the flexibility and adaptability needed to explore complex parameter spaces, efficiently converging on optimal solutions that reflect real-world traffic behavior.

The calibration process is designed to iteratively minimize discrepancies between simulated and observed traffic flows. By continually adjusting parameters based on observed field characteristics, this approach brings simulated results into alignment with actual traffic conditions, capturing subtle variations in driver behavior and roadway interactions. To ensure the robustness of the calibration, the study applies rigorous statistical tests to establish a benchmark for acceptable accuracy, defining thresholds for simulation reliability and validating the optimized model’s performance against field data.

This framework not only improves the precision of traffic simulations but also expands their applicability to real-world scenarios, enabling more accurate assessments of traffic interventions, infrastructure changes, and policy impacts. Through enhanced parameter calibration techniques, this work contributes to the field of traffic simulation by providing a replicable, adaptable model calibration approach that elevates simulation accuracy and applicability across diverse traffic conditions.