A data-driven traffic modeling for analyzing the impacts of a freight departure time shift policy

•We assess the impact of container transport demand on traffic using a data-driven traffic prediction model based on a graph neural network.•The model is implemented for the Port of Rotterdam using its container trip database combined with loop detector data for traffic flows.•The application of the model informs departure time shift policy, including the optimal shift volumes and patterns.•An optimized peak avoidance scheme can lead to significant congestion reduction while allowing to compensate the affected freight carriers.