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.

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

External partner

Stockholms Lokaltrafik, SL

Publications

Héctor Rodriguez Déniz, Mattias Villani (2022)

Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses

IEEE transactions on intelligent transportation systems (Print) , Vol.23 , s.16304-16317 Continue to DOI

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

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

Héctor Rodriguez Déniz, Erik Jenelius, Mattias Villani (2017)

Urban Network Travel Time Prediction via Online Multi-Output Gaussian Process Regression

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

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