Abstract
In response to the current COVID-19 pandemic, policy makers at the Greater London Authority (GLA) and Transport for London (TfL) require prompt and accurate data sources to understand how the busyness of the city is changing. This poster presents Project Odysseus at the Alan Turing Institute which integrates multiple large-scale, heterogeneous datasets and develops machine learning models to infer mobility, transportation and footfall activity throughout the city of London.
Citation information
Patrick O'Hara, James Walsh, Chance Haycock, Edward Thorpe-Woords, Oluwafunmilola Kesa, Andrew Wang, Mihai Ilas, James Brandreth, Oscar Giles, Neil Dhir, Theodoros Damoulas, Computing Insight UK 2020.
Turing affiliated authors
James Walsh
Research Assistant
Chance Haycock
Research Assistant at University of Warwick
Edward Thorpe-Woods
Research Assistant at University of Warwick
Funmi Kesa
Feuer Scholar, University of Warwick
Andrew Wang
Undergraduate, University of Cambridge
Mihai Ilas
Undergraduate, University of Cambridge
James Brandreth
Research Assistant
Dr Neil Dhir
Visiting Researcher