Bayesian Learning for Spatiotemporal Processes in Transportation

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.
Aerial View of Beijing Traffic Jam 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.
 

Researchers Show/Hide content

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Héctor Rodriguez Déniz, Mattias Villani (2022)

IEEE transactions on intelligent transportation systems (Print) Continue to DOI

Héctor Rodriguez Déniz, Mattias Villani, Augusto Voltes-Dorta (2022)

Transportation Research Part C: Emerging Technologies , Vol.137 Continue to DOI

Hector Rodriguez-Deniz, Erik Jenelius, Mattias Villani (2017)

2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC) Continue to DOI

Research Machine learning Show/Hide content

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