18 citations · 27 across the 7 of their papers we have counts for
7 papers
Learning and Generalizing Polynomials in Simulation Metamodeling
Jesper Hauch, Christoffer Riis, Francisco C. Pereira
The ability to learn polynomials and generalize out-of-distribution is essential for simulation metamodels in many disciplines of engineering, where the time step updates are descr…
Graph Reinforcement Learning for Network Control via Bi-Level Optimization
Daniele Gammelli, James Harrison, Kaidi Yang +3
Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional op…
Representation learning of rare temporal conditions for travel time prediction
Niklas Petersen, Filipe Rodrigues, Francisco Pereira
Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at…
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel ne…
Unboxing the graph: Neural Relational Inference for Mobility Prediction
Mathias Niemann Tygesen, Francisco C. Pereira, Filipe Rodrigues
Predicting the supply and demand of transport systems is vital for efficient traffic management, control, optimization, and planning. For example, predicting where from/to and when…
Deep Spatio-Temporal Forecasting of Electrical Vehicle Charging Demand
Frederik Boe Hüttel, Inon Peled, Filipe Rodrigues +1
Electric vehicles can offer a low carbon emission solution to reverse rising emission trends. However, this requires that the energy used to meet the demand is green. To meet this…