17 citations · 20 across the 6 of their papers we have counts for
5 papers · 1 filter
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 +2
Climate policy analysis requires models that capture multi-gas climate effects, but such models are too slow to embed in reinforcement learning loops at scale. In collaboration wit…
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…
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…
A Bayesian Optimization approach for calibrating large-scale activity-based transport models
Serio Agriesti, Vladimir Kuzmanovski, Jaakko Hollmén +2
The use of Agent-Based and Activity-Based modeling in transportation is rising due to the capability of addressing complex applications such as disruptive trends (e.g., remote work…