Contemporary urban policy makers face a critical challenge over the next decade or so: transport infrastructures need to be able to cope with growing (and increasingly mobile) populations, yet central and local governments are operating in an era of austerity where major new investment in infrastructure is unlikely. Data science and data-centric engineering offer a potential solution to this problem. By offering ways of optimising the existing use of infrastructure by exploiting more data about the way the infrastructure is used, there is the possibility of improving the performance of existing traffic networks without making major new investments.
The availability of new data is transforming the way we study human behaviour as well as plan and operate infrastructure. Along with data from sensors, crowd-sourced data, social data, and administrative data have also all greatly increased. Millions of volunteers contribute geographic data to platforms like OpenStreetMap capturing details including points of interest, bus stops, restaurants, and walking trails. At the same time, huge swaths of social data (e.g., online reviews, posts on social networking sites, etc.) augment our physical space with additional data that can reveal commuting patterns and other human activity. Finally, administrative and government processes are digital, available in realtime and more easily linked and searched than ever before.
This project seeks to understand how these additional data sources complement sensor data to give us a deeper understanding of human behaviour and improve modelling and prediction tasks. We also seek to innovate in ways to visualise and analyse this data quickly, extending our work on generating tile maps algorithmically.
Associate Professor, Senior Research Fellow
Project role: Principal Investigator
Dr Scott A. Hale is an Associate Professor, Senior Research Fellow, and Turing Fellow. He develops and applies computer science techniques to the social sciences focusing on increasing equitable access to quality information.
Research Associate
Project role: Co-Investigator
In 2022 Jonathan Bright became the Head of AI for Public Services at the Turing Institute, having previously been a faculty member of the OII. A political scientist, he specialises in computational and ‘big data’ approaches to the social sciences.
Former
Project role: Researcher
Graham's research focuses on network science and visualization. He is particularly interested in geospatial and temporal networks.
Research Associate
Project role: Researcher
Dr Chico Camargo is an interdisciplinary researcher who develops and applies computational tools to study complex systems, collective human behaviour, public opinion dynamics and culture change.
With Dr Scott A. Hale, Dr Jonathan Bright, Dr Chico Camargo, and Private: Dr Graham McNeill
This presentation, hosted by the Alan Turing Institute focuses on using crowd-sourced data, such as OpenStreetMap and Waze, to improve traffic models and better understand the factors contributing to traffic jams and other traffic issues.
