The rapid deployment of streaming sensors have made spatiotemporal data increasingly common. In this project we develop probabilistic models for spatiotemporal data with applications in the field of transportation.
Photo credit iStock/Wenjie DongThe main focus is on developing computationally efficient Bayesian methods for learning, prediction and decision-making for spatiotemporal network-structured data.
Subproject 1. A Bayesian Dynamic Stochastic Block Model for Large-Scale Multilayered Networks with Applications to Airline Network Prediction.
Subproject 2. Traffic Flow Modeling and Prediction using Gaussian Processes with Road Topology Structure.
Héctor Rodriguez Déniz, Mattias Villani (2022)
Héctor Rodriguez Déniz, Mattias Villani, Augusto Voltes-Dorta (2022)
A multilayered block network model to forecast large dynamic transportation graphs: An application to US air transport
Héctor Rodriguez Déniz, Erik Jenelius, Mattias Villani (2017)