A hybrid deep-learning-metaheuristic framework for bi-level network design problems. (arXiv:2303.06024v3 [cs.NE] UPDATED)

This study proposes a hybrid deep-learning-metaheuristic framework with a
bi-level architecture for road network design problems (NDPs). We train a graph
neural network (GNN) to approximate the solution of the user equilibrium (UE)
traffic assignment problem and use inferences made by the trained model to
calculate fitness function evaluations of a genetic algorithm (GA) to
approximate solutions for NDPs. Using three test networks, two NDP variants and
an exact solver as benchmark, we show that on average, our proposed framework …

architecture arxiv design equilibrium fitness framework function gnn graph graph neural network hybrid network neural network solution study traffic