Simulation of Vehicles' Gap Acceptance Decision at Unsignalized Intersections Using SUMO

In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.