Integration of simulation and machine learning to predict the traffic in the event of disruptions
Date:
Decision-making in intelligent transportation systems is complex due to urban traffic’s unpredictable dynamics, particularly when assessing the impact of road works on congestion. To address this, a method combining simulation using SUMO and machine learning with GNN is proposed for traffic prediction in the event of road disruptions.