2 citations · 2 across the 1 of their papers we have counts for
4 papers
Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques
Amalie Roark, Serio Agriesti, Francisco Camara Pereira +1
The Macroscopic Fundamental Diagram is a popular tool used to describe traffic dynamics in an aggregated way, with applications ranging from traffic control to incident analysis. H…
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…
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…
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…