The project:

Nature reserves and parks are often entered on foot. Understanding where people walk in these spaces is important, as it may determine maintenance needs, facility provision (bins, toilets...), and most importantly because human activity has been shown to displace wildlife, at least temporarily. How these spaces are used depends on the type of activity individuals pursue, which in turn may be coupled to seasonal changes or weather conditions. For example, walking for leisure may be less popular in winter and/or more confined to well-maintained trails. Thus, existing methodology for investigating pedestrian traffic cannot be applied directly to this context to make predictions about how many people walk where and when in nature reserves or parks.

To address the central question of this project, it is not necessary to track the movement of individuals. Therefore, the focus will be on developing macroscopic models that predict averaged usage patterns, such a pedestrian density, over time. The envisaged modelling approach will be based on hybrid models, that combine elements of physical or statistical models with black-box machine learning. Models will be trained using diverse data, drawing on geographic information system (GIS) techniques in urban planning, and combining methods such as park facility classification, point of interest (POI) data analysis, spatial analysis, and video and eye-tracking device recordings. There is substantial flexibility in how this problem can be modelled, and training for different techniques will be provided. This project will lead to a useful tool for managing nature reserves and parks, and for designing new ones. It will also enhance our fundamental understanding of pedestrian behaviour and the disturbances to wildlife caused by outdoor leisure activities.

Students can expect to be embedded in a vibrant international research community, and to learn a wealth of highly transferrable technical and soft skills.

Candidate requirements:

Applicants must hold/achieve a minimum of a merit at master’s degree level (or international equivalent) in a science, mathematics or engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first-class undergraduate degree. Please note, acceptance will also depend on evidence of readiness to pursue a research degree.

If English is not your first language, you need to meet this profile level: Profile E

Further information about English language requirements and profile levels .

Contacts:

For questions about the research topic, please contact the project supervisor.

For questions about eligibility and the application process please contact Engineering Postgraduate Research Admissions [email protected]

How to apply:

Prior to submitting an online application, you will need to contact the project supervisor to discuss.

Online applications are made at http://www.bris.ac.uk/pg-howtoapply . Please select Engineering Mathematics (PhD) on the Programme Choice page. You will be prompted to enter details of any studentship you would like to be considered for in the Funding and Research Details sections of the form.