1 citations · 1 across the 7 of their papers we have counts for
8 papers
Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems
Emil Kragh Toft, Carolin Schmidt, Daniele Gammelli +1
Autonomous Mobility-on-Demand (AMoD) systems promise to revolutionize urban transportation by providing affordable on-demand services to meet growing travel demand. However, realis…
A Bayesian latent class reinforcement learning framework to capture adaptive, feedback-driven travel behaviour
Georges Sfeir, Stephane Hess, Thomas O. Hancock +5
Many travel decisions involve a degree of experience formation, where individuals learn their preferences over time. At the same time, there is extensive scope for heterogeneity ac…
On Predicting Sociodemographics from Mobility Signals
Ekin Uğurel, Cynthia Chen, Brian H. Y. Lee +1
Inferring sociodemographic attributes from mobility data could help transportation planners better leverage passively collected datasets, but this task remains difficult due to wea…
Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
Xinling Li, Meshal Alharbi, Daniele Gammelli +7
Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD…
Robo-taxi Fleet Coordination at Scale via Reinforcement Learning
Luigi Tresca, Carolin Schmidt, James Harrison +4
Fleets of robo-taxis offering on-demand transportation services, commonly known as Autonomous Mobility-on-Demand (AMoD) systems, hold significant promise for societal benefits, suc…
Diffusion-aware Censored Gaussian Processes for Demand Modelling
Filipe Rodrigues
Inferring the true demand for a product or a service from aggregate data is often challenging due to the limited available supply, thus resulting in observations that are censored…