3 papers
cs.LG2026
Metalearning traffic assignment for network disruptions with graph convolutional neural networks
Serio Agriesti, Guido Cantelmo, Francisco Camara Pereira
Building machine-learning models for estimating traffic flows from OD matrices requires an appropriate design of the training process and a training dataset spanning over multiple…
cs.LG2025
Climate Surrogates for Scalable Multi-Agent Reinforcement Learning: A Case Study with CICERO-SCM
Oskar Bohn Lassen, Serio Angelo Maria Agriesti, Filipe Rodrigues +1
Climate policy studies require models that capture the combined effects of multiple greenhouse gases on global temperature, but these models are computationally expensive and diffi…
cs.LG2025
Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment
Oskar Bohn Lassen, Serio Agriesti, Mohamed Eldafrawi +4
The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks. Traditional methods require it…