Finding transport network configurations using supervised machine learning

We propose a machine learning-based approach using a random forest for the fast computation of optimized transport network routing configurations. An evaluation in a 7-node network shows that our approach achieves competitive results in terms of solution quality and computation time compared to an exact ILP solution.