Using AI to leverage new forms of data in modelling cycling behaviours in the LCR

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The Problem:It is estimated that 22% of adults in England are physically inactive, and these rates are higher within each Local Authority within the Liverpool City Region (LCR) (PHE 2019). Physical activity is an important determinant of health, being associated with lower risk of cardiovascular diseases, as well as improved mental wellbeing. Designing cities and neighbourhoods to encourage physical activity is therefore an important policy priority. An increasingly adopted approach is to increase the uptake of active travel, particularly cycling. However, only 3.3% of adults in England cycle for travel at least 3 times per week, and rates are lower for the LCR. Targeted investment in cycling infrastructure can encourage more individuals to take up cycling, as well as reduce air pollution indirectly benefiting health.The Solution:This project will use state-of-the-art machine learning and AI techniques to leverage new forms of data to improve decision making around cycling investment. Such approaches are rarely applied within transport modelling, but offer novelty to process and model complex (big) data to inform cycling behaviours and infrastructure provision. A key advantage of the PhD will be the development of bespoke methods that enable to make the most out of data unexplored in the context of cycling. These methods will be designed so that they can be easily deployed within any local government to inform cycling provision. This project is designed to co-produce real-world solutions alongside the non-academic partner, the LCR, thus maximising impact.Outline of the PhD:The project will be structured as a publication-based PhD, and will include three main subprojects:1. Modelling volume of cycling traffic from pneumatic road tube countersThis project will use cycling counts from pneumatic road tube counters and ancillary data about the characteristics of the locations where they are placed to build a predictive model of cycling counts at the street segment that can be deployed to the entire network of the LCR. This will enhance the understanding of the distribution of cyclists to agencies related a range of domains, from public health to transport planning. Methodologically, this project will expand tree-based models (e.g. random forests, boosted trees) to explicitly incorporate spatial features and relationships.2. Understanding the drivers behind cycling flowsThis paper will unpack the driving factors behind the estimates obtained in the previous one. By combining traditional socio-economic sources of data (e.g. Census, Deprivation scores) with new approaches such as video footage or imagery data that recognise features of the environment (e.g. road quality, foliage, etc.), the study will identify how environmental factors interact with social conditions to determine the extent to which people cycle in different places. To be able to leverage these data sources, state-of-the-art AI techniques such as convolutional neural networks (CNNs) will be required.3. Predicting where to invest on urban cycling infrastructureIn this final paper, the student will use results from the previous two in order to build a decision-making system that informs policies on improvement of cycling infrastructure in the LCR. The system will fulfil two main functions: first, it will provide an intuitive way of visualising and interacting with the results of the predictive models generated and the measures of uncertainty associated with them; second, it will feature the capability of asking "what-if" type of questions around the improvement of infrastructure. In this context, the student will explore the suitability of spatial interaction and agent-based models. It is expected this system will enable the identification of policy priorities within the LCR.