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A new type of digital data helping us understand travel data patterns and improving public health


Travel can influence public health in a variety of ways, including air pollution, personal safety, and the spread of infectious diseases such as Covid-19. Work at LIDA is being undertaken to deploy collaborative multidisciplinary research using information and communications technology, and artificial intelligence with pervasive sensing systems, that generate a new type of digital data.

This helps us to understand travel data patterns, instances of close proximity and ultimately improve public health measures. Based around new and emerging data forms, mobility profiling and the role of web-based technologies in the transport sector, the work is looking at virus spread and transport use for a range of population groups including older people, young people and those with protected characteristics. This undertaking across multiple projects involves using micro-level, user-generated data acquired from downloadable apps and wearable bands.

Relatively little is known about the travel patterns of some parts of the population, including older people, younger people, those from “left behind” communities and those with protected characteristics. Historically, attention has focused more on the commuting behaviour of the working age population and the leisure time of this same demographic.

“As an example, retired people need to be mobile and also, for social sustainability, in touch with activities, health centres and the community,” explains Prof. Grant-Muller. “Travel data collected historically has been largely based on more traditional methods – questionnaires, writing travel diaries every day – and, for older people, it can be difficult to do this every day. Having an app that does it for them passively after downloading, means they don’t have to do anything else.”

This feature is being taken up by Prof. Grant-Mullers’ team as part of the Responsible Automation for Inclusive Mobility (RAIM) project, led by Prof. Ed Manley at the University of Leeds. The project includes collaboration with the University of Manitoba, Winnipeg Transit, UCL and Transport for West Midlands on a project funded by SRC and the Canadian Government, looking at the potential for on-demand, electric autonomous vehicles (EAVs) to meet the transport needs of older people in the UK and Canada.

The Transport Risk Assessment for COVID Knowledge (TRACK and TRACK II) projects, PI Prof. Cath Noakes at the University of Leeds, are funded by the Engineering and Physical Sciences Research Council (EPSRC). Prof. Grant-Mullers’ team have been involved in conducting fieldwork on buses and trains in London, Leeds and Newcastle, including the light-rail system in Tyne and Wear, using a mix of data technologies to look at a variety of data on travel patterns.

App tracking data allows detailed simulations of the way the COVID-19 virus could potentially spread and the data interfaces with other types of models, including Agent Based Models. This can lead to the creation of models quantifying the level of exposure faced by passengers, helping government and transport operators decide if additional mitigation measures are needed.

The Lifeband project is funded by Innovate UK, with the University of Leeds being part of a consortium developing sensing capabilities around understanding proximity and the spread of Covid-19. Lifeband is a low-tech, highly secure wristband alternative to a phone app, providing a more complete picture of the extent to which people are in close proximity to others and the risks to (and from) population groups.

At present, trials are being conducted on small samples of experimental data from people who have tried out the wristband. Location and proximity data from the trials will enable research analysis and modelling using large-scale data analytics, looking at how different groups are impacted upon by COVID-19 and how government responses can be improved. In the future, the technology can be used for other projects making use of movement data as well, with the Lifeband consortium currently working on the development of a smartphone app version as an alternative to the Lifeband.

“These technologies help us build up models of transport interactions and virus exposure, which are part of a bigger picture involving obesity, environmental and personal safety aspects, among other things, all of which have impact” says Prof Grant-Muller.

The KARMA project, funded by The Alan Turing Institute, takes account of all this work in examining the role new and emerging data can have in furthering our understanding of how transport interacts with health – the extent to which people are being active, their exposure to atmospheric pollutants and much more. The project is linking new data and models that work at different scales, including new data with an Agent Based Model, Spatial Microsimulation, and a System Dynamics Model.

Susan Grant-Muller is Professor of Technologies and Informatics at the University of Leeds Institute for Transport Studies and a Fellow of the Alan Turing Institute. She leads a programme of research into large scale data analytics and the role of new forms of technology enabled data in developing sustainable transport policy. She was recently invited to give evidence to a House of Lords Select Committee on the role of technologies and data needs to deliver better integration and connectivity across public transport. Co-Investigator for the ESRC Consumer Data Research Centre (CDRC), she is also a Co-Director of the LIDA: Societies community.