18 citations · 21 across the 6 of their papers we have counts for
6 papers
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…
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic…
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Filipe Rodrigues, Nicola Ortelli, Michel Bierlaire +1
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying t…