52 citations · 115 across the 13 of their papers we have counts for
25 papers
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…
Context-aware Bayesian Mixed Multinomial Logit Model
Mirosława Łukawska, Anders Fjendbo Jensen, Filipe Rodrigues
The mixed multinomial logit model assumes constant preference parameters of a decision-maker throughout different choice situations, which may be considered too strong for certain…
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…
On the importance of stationarity, strong baselines and benchmarks in transport prediction problems
Filipe Rodrigues
Over the last years, the transportation community has witnessed a tremendous amount of research contributions on new deep learning approaches for spatio-temporal forecasting. These…
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…