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

hector.rodriguez@liu.se

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

Research Machine learning

Machine learning for 5G System Control and Automation

There is a plethora of new applications envisioned for 5G, ranging from traditional efficient broadband communication links to more strictly reliable communication links or massive communication that is typically associated with Internet of Things.

Probabilistic models and deep learning – bridging the gap

Probabilistic models and deep learning are two successful branches of machine learning. In this project, we will develop theory and methods related to the interplay between these technologies, enabling us to take advantage of the strengths of both.

WASP research

WASP Machine learning

Division of Statistics and Machine Learning develops methods that autonomous systems use when learning from data. Our research focuses on probabilistic modeling and efficient algorithms for learning, prediction and decision making under uncertainty.

WASP - Wallenberg AI, Autonomous Systems and Software Program

The fourth industrial revolution is upon us, as automation becomes autonomy. LiU conducts outstanding research in several of the fields that are central to the Wallenberg AI Autonomous Systems and Software Program, WASP.