EPSRC DLA: Simulating public transport environments: modelling behaviour and systems for safer, more resilient, and greener transport

One full scholarship is available in the Institute for Transport Studies in 2026/27. This scholarship is open to UK applicants and covers fees plus maintenance.<br /> <br /> The Institute for Transport Studies invites applications from prospective postgraduate researchers who wish to commence study for a PhD in the academic year 2026/27 Institute for Transport Studies EPSRC DLA Scholarship.

<p>Public transport is essential for providing access to jobs, healthcare, education, and social connection, especially for people without a car and the ageing populations. It is also expected to play an increasingly important role in meeting the UK’s net-zero goals, as policies encourage shared mobility and reduced private car use (DfT 2021). As more people shift from cars to buses and trains, understanding how passengers move through and experience transport spaces will be crucial for keeping these systems safe, resilient, and efficient. Yet key internal dynamics, such as passenger behaviour, movement, crowding, occupancy, and interactions with infrastructure remain poorly understood despite their influence on safety, comfort, and operational performance.</p> <p>Capturing these dynamics is challenging, as traditional surveys and manual observations fail to capture the high-resolution temporal and spatial variability required for modern infrastructure management. At the same time, national priorities in smart, sustainable, and resilient mobility highlight the need for advanced predictive tools such as digital twins. By integrating real-world data with simulation, these tools can support better decisions, improve operational efficiency, and accelerate the transition to safe, resilient, low-carbon transport. However, current digital twins are limited by insufficient empirical data on real passenger behaviour, limiting their ability to represent complex human-environment interactions. Closing this gap requires methods that capture fine-grained behavioural and environmental information and integrate both into simulation models that can support future transport digital twins.</p> <p>This research builds on earlier work (EPSRC: TRACK; EPSRC IAA: McMatcher) in which the UoL team developed a sensor-based method for capturing high-resolution, feature-rich passenger movement data in transport environments. Using wireless signals captured from pervasive smart devices (smartphone, smartwatch etc) carried by individuals and smart technology in the environment (IoT), the technology can capture fine-grained passenger movements, trajectories, dwell-times, and spatio-temporal patterns of crowding and occupancy.</p> <p>Unlike large-scale modelling effort such as EPSRC: TransiT, which focus on national transport decarbonisation through system-wide digital twins, this research targets the micro-level dynamics inside a public transport environment, such as a train station. It aims to generate finer-grained insights that can complement and improve TransiT’s models. Moreover, this research has a broader range of applications, beyond supporting decarbonisation, it can be used to study virus transmission, assess comfort or accessibility, and stress-test the transport environment under a range of operational scenarios.</p> <p>By combining sensor data, service timetables, meteorological information, and structural features of the built environment, this research aims to build a data-driven simulator capable of representing and forecasting conditions and dynamics within the transport environment, which can support future digital twin development. The simulator can be leveraged to study future and “what-if” scenarios, such as rising passenger demand from reduced car use, or the spread of airborne contaminants. These simulations will allow operators, planners, and policy makers to test interventions before implementing them in the real world.</p> <p>The objectives of the project will be to:</p> <p> 1. Analyse an existing database of sensor data collected within bus and train environments to identify passenger movement patterns, crowding levels, and interactions with key features of the built environment. This will require substantial data wrangling and pre-processing to extract reliable trajectories and flow patterns.</p> <p>2. Integrate sensor-derived behaviour with external datasets, such as service timetables and meteorological information, to explore how operational schedules and environmental</p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the research information section that the research degree you wish to be considered for is <em>Simulating public transport environments: modelling behaviour and systems for safer, more resilient, and greener transport</em> as well as <a href="https://environment.leeds.ac.uk/transport/staff/932/professor-susan-grant-muller">Prof. Susan Grant-Muller</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p><em>As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.</em></p>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The candidate should have a Masters degree in a quantative subject such as computer science, electrical engineering, or statistics, with knowledge of python, R or similar, and preferably a background of computation modelling (ABM) or analysis within the transport built environment.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<p>We are offering a fully funded scholarship to study the project <em>Simulating public transport environments: modelling behaviour and systems for safer: more resilient, and greener transport</em>, Institute for Transport Studies, University of Leeds for one UK status candidate. The funding covers UK tuition fees as well as a UKRI matched stipend (currently £20,780 in 2025/26) per year, subject to satisfactory progress.</p> <p><strong>Elegibility Criteria</strong></p> <p>Applicants must be elgigible for UK (Home) fees/funding.</p> <p>If you are unsure whether you are eligible for UK fees/funding, please see our <a href="https://www.leeds.ac.uk/undergraduate-fees/doc/fee-assessment">fee assessment page.</a></p>

<p>For further information please contact Prof. Susan Grant-Muller: <a href="mailto:S.M.Grant-Muller@its.leeds.ac.uk">S.M.Grant-Muller@its.leeds.ac.uk</a> or the Environment PGR Admissions team via email: ENV-PGR@leeds.ac.uk</p>