A good understanding of traveller behaviour underpins all policies which are effective in influencing travel behaviour to reduce congestion, increase activity levels, improve air quality and/or reduce carbon emissions. It is also key in planning economically efficient transport systems with high levels of customer satisfaction. To encourage a more substantial change in behaviour which can be maintained over a longer period of time, it is essential to focus on people's travel behaviour over weeks and months, rather than focusing on a particular trip or a particular day only. My research takes this more holistic approach, enabling me to examine transport costs charged weekly, monthly or annually, and travel patterns in the context of broader societal changes, such as changing working patterns, and changing family dynamics. As described in the UK's Industrial Strategy (HM Government, 2017, p48), fundamental changes in transport systems will occur over the coming decade, for example due to the introduction of autonomous vehicles and a shift away from individual vehicle ownership. It is more important than ever that people's transport needs and travel behaviour inform appropriate public policy and a vision for a more sustainable future, rather than changes being primarly driven by profit-making.

This research is timely due to the increasing importance of the data-driven economy, as emphasised within the Industrial Strategy (HM Government, 2017). For transport, large emerging data sources include smartcard and contactless bank card payments on local buses and data collected using mobile phone technologies. New methods are required to extract the relevant information from these data sources. During my PhD, I developed methodologies to gain insights into multi-day travel behaviour on the road network from large quantities of data collected continuously and passively.

During the fellowship, I will collaborate closely with potential users of the methodologies I develop. I will adapt the methodologies for different contexts and gain experience in handling a wider variety of transport data, including public transport and multi-modal data. By working closely with Reading Buses, Bristol Traffic Control Centre and Living Streets, I will provide insights into the multi-day travel behaviour of their customers, and gain a better understanding of their current priorities and barriers to changing practices. I will produce tailored reports for each of these collaborators. I will pursue connections I have made, including with a private sector body providing non-emergency patient transfers and a private sector consultant. I will use this experience to develop freely accessible tools which can be used to apply the methodologies developed.

The fellowship will enable me to take my research to the next level by examining the consequences of the insights learned from these new methodologies, for example the suitability of multi-day public transport fare structures. I have also identified that to apply my methods to data from large city regions in the future, I will need to develop skills for handling even larger datasets. I will therefore be undertaking structured training to fill this gap, namely modules on the Data Science and Analytics MSc at Cardiff University. During the year, I will also continue to learn from my colleagues at UWE, for example by developing skills in longitudinal data analysis with help from my mentor, Associate Professor Kiron Chatterjee, who is an expert in this field for transport.

I will communicate the methodologies I have developed, the findings and their implications to a range of audiences regionally, nationally and internationally. This will include presentations to academics and practitioners, writing academic journal papers, utilising contacts within the Centre for Transport and Society at UWE, and less traditional mediums such as a blog entry.