Artificial Intelligence-Based Protocol for Macroscopic Traffic Simulation Model Development

This study proposes a machine learning-based protocol for developing a TRANSYT-7F model for an urban arterial network. The developed artificial neural network (ANN) method models the queue lengths of TRANSYT-7F using saturation flow, start-up lost time, and platoon dispersion as inputs. The queue lengths of the selected approaches of the study network can be obtained using the ANN model without running the TRANSYT-7F model. The optimum values of the selected parameters (i.e., saturation flow, start-up lost time, and platoon dispersion) were obtained using the genetic algorithm, which ensures minimum difference between the measured queue length and the ANN output (i.e., queue length). Finally, the comparison of the measured queue length and the simulated queue length with the calibrated TRANSYT-7F model revealed that the mean absolute percentage error was less than 2.5% for all approaches of the study network.