Defining accuracy benchmarks for freeway traffic simulations in support of highway operations and planning
Year: 2025
Type: article
Abstract: Accurate calibration of traffic simulation models is essential for replicating observed traffic conditions, and subsequent optimization of decision-making processes and targeted investments in transportation infrastructure. This study applies a genetic algorithm (GA) to optimize key parameters of the car-following model for a basic freeway segment in California, aiming to minimize the error between simulated and observed traffic data. Outputs generated during GA iterations were analyzed using paired T-tests and Wilcoxon signed-rank tests to compare simulated speed and flow against ground truth data. Accuracy for each sample was matched to its corresponding P-value, revealing a clear trend: when accuracy levels exceeded 80%, P-values for both speed and flow consistently rose above 0.05. This indicates that the simulated outputs became statistically indistinguishable from the observed field data after 80% accuracy. These findings demonstrate that combining statistical significance with accuracy metrics can effectively guide calibration processes and establish thresholds for acceptable simulation accuracy, contributing to a robust framework for traffic simulation studies.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7077082414