Machine Learning Surrogates for Optimizing Transportation Policies with Agent-Based Models
Résumé
This study aims to evaluate the feasibility of using Graph Neural Networks as surrogates for agent-based models in transport planning, with the long-term goal of enabling simulation-based optimization through agent-based simulations. To the authors’ knowledge, only Narayanan et al. has provided a proof-of-concept, replicating the 4-step model with GNNs on networks of 15 to 80 nodes. Our study, by contrast, trains a GNN using MATSim simulation data for a Paris case study with 30,000 nodes. The results indicate that the trained model can successfully predict outcomes across different policy implementations.