Calibration of PARAMICS Model: Application of Artificial Intelligence-Based Approach

The advent and significant improvement in computing technology in the last decades has led to immense popularity of traffic microscopic simulation models in addressing different transportation engineering issues. This paper focuses on the challenges of calibration of microscopic model incorporating the driving behavior for the local traffic conditions in the Kingdom of Saudi Arabia (KSA). One of the state-of-the-art microscopic simulation models, PARAMICS was used for the calibration study. This study proposes machine learning model-based calibration methodology for the PARAMICS model. The developed artificial neural network (ANN) model performs adequately in modeling the queue length as a function of mean target headway and mean reaction time. The selected values of the calibration parameters were finally obtained using the genetic algorithm, which ensures minimum difference with the measured values of queue lengths and the ANN output (i.e., queue lengths). The queue lengths obtained through the ANN- and GA-based approach were used as the input parameters for the PARAMICS model. The conformance of the PARAMICS and the ANN model outputs indicates the validity of the proposed calibration methodology.