Scholarships & funding

EPSRC Doctoral Studentship - Plans vs Reality: AI-Enhanced Modelling of Disrupted Urban Mobility

Project details

Overview

Urban mobility models typically assume fixed daily activity schedules and stable transport networks. In reality, individuals continuously adapt their plans in response to disruptions such as delays, missed connections, time pressures, caring responsibilities, safety concerns, and changing priorities. This gap—between planned and realised behaviour—is poorly represented in current models, limiting their usefulness for designing robust, equitable, and sustainable transport systems.

This PhD will extend an existing agent-based and activity scheduling framework developed through the EPSRC AI4CI Hub. The framework integrates: (1) synthetic population generation grounded in demographic structures; (2) activity-based demand modelling; (3) multi-modal routing (walking, cycling, car, and bus); and (4) dynamic schedule adaptation under constraints. The project will advance this system by incorporating behavioural realism and disruption-aware modelling.

Two key innovations underpin the research. First, behavioural modelling using AI, including reinforcement learning (RL) and large language models (LLMs), will be explored to better represent decision-making under uncertainty, including preference evolution and context-aware adaptation. Second, disruption-aware scheduling and routing will be developed by embedding stochastic models of events such as delays, congestion, weather shocks, and safety perceptions, allowing analysis of how disruptions propagate through individual schedules and the wider system.

Aims and research questions

The overarching aim is to develop a framework for resilient urban mobility modelling that integrates machine learning with individual-based simulation. The project will:

1. Compare behavioural modelling approaches (rule-based, RL, and LLM-assisted) for dynamic decision-making.
2. Quantify how disruptions lead to plan breakdowns, rescheduling, and unequal impacts across populations.
3. Generate actionable insights for transport planning, including reliability, service design, ans support for vulnerable users.

Key research questions include:

• How do different modelling approaches perform in representing adaptive behaviour under uncertainty?
• How do disruptions affect activity completion, mode choice, and network performance?

Methodology

The project will build on and extend an existing modelling framework through four components:

• Synthetic populations and activity generation: A detailed population will be created using census and survey data (e.g. National Travel Survey), capturing household structure and care responsibilities.
• Network modelling: A unified, multi-modal, time-dependent network will be constructed, incorporating travel time, cost, comfort, and perceived safety, alongside dynamic routing capabilities.
• Dynamic activity scheduling: Individuals will follow baseline daily schedules subject to hard and soft constraints, with real-time adaptation triggered by disruptions such as delays or cancellations.
• Behavioural modelling: Agents will be implemented using rule-based approaches, RL policies, and LLM-guided decision-making, and evaluated based on realism, robustness, and task completion.

Model calibration and validation will be conducted throughout

Experiments and policy scenarios

The model will be applied to policy-relevant scenarios developed with stakeholders (e.g. Transport Scotland, local authorities, operators). Scenarios may include reliability improvements, safety interventions, and enhanced real-time information systems. The aim is to identify how interventions affect disruption propagation, accessibility, and equity.

Supervisory Team

Prinicipal Supervisor: Professor Alison Heppenstall

Secondary Supervisor/s: Dr David McArthur & Dr Yahya Gamal

About the School/Research Unit 

The student will be embedded within a highly interdisciplinary and collaborative research environment spanning artificial intelligence, urban analytics, and social science. The project is situated within the EPSRC-funded AI for Collective Intelligence (AI4CI) Hub (Smart City Design theme), where supervisors Heppenstall and Gamal are developing cutting-edge individual-based models, including housing and mobility systems with activity scheduling components. This ongoing work provides a strong methodological and technical foundation for the PhD. Through the Hub, the student will have access to a wide network of academic and non-academic stakeholders, offering opportunities for collaboration, data access, and real-world impact.

In addition, the student will be part of the Urban Analytics research group, led by the supervisors, which includes an active and growing community of national and international researchers. The student will also engage with the Urban Big Data Centre (UBDC) and the wider Urban Analytics group within the College of Social Sciences. This environment offers expertise in working with granular urban datasets and aligns closely with institutional research priorities, particularly around understanding social, economic, and health inequalities in cities, as reflected in initiatives such as Glasgow Changing Futures.

Eligibility

Applicants must meet the following eligibility criteria:

• Applicants will have a good Masters degree (or overseas equivalent)
• Applicants will have a demonstratable interest in the topic area under investigation
• Applicants must be able to study on a full-time basis only
• Applicants must meet the University's criteria to be considered 'Home' or 'Rest of UK' for fee status

Please note that all applicants must also meet the entry requirements for the Urban Studies, PhD. 

Number of Scholarships

1

Value

Award details

The scholarship is available as a full-time +3.5 (3.5 year) PhD programme only. The programme will commence in October 2026. The full funding package includes:

• An annual maintenance grant (stipend) at the UKRI rate
• Fees at the standard home tuition fee rate only
• Students can also draw on a Research Training Support Grant, usually up to a maximum of £940 per year 

How to apply

Applicants must apply via the Scholarships Application Portal (please see Scholarships Application Portal - Applicant Guide for more information).The funding opportunity is under 'College of Social Sciences Postgraduate Research Funding > COSS-26-024' - note the Portal will open for this opportunity shortly) uploading the following documentation:

• EPSRC Doctoral Studentship (Heppenstall) Application Form (in Word format)
• Academic transcripts (All relevant Undergraduate and Master’s level degree transcripts (and translations, if not originally in English) – provisional transcripts are sufficient if you are yet to complete your degree).
• Academic Prizes
• Contact details for two referees (where possible your referees should include an academic familiar with your work (within the last 5 years). Both referees can be academics but you may include a work referee, especially if you have been out of academia for more than 5 years). Please note, a CoSS PGR Funding Reference template will be sent to your referees for completion)*. Note that no member of the above supervisory team can act as your referee. Please see CoSS PGR Funding Reference request guide for further guidance
• Curriculum Vitae (CV) (academic where applicable)

*Please note that when you enter your referees contact details on the Scholarships Application Portal and send the reference request, your referees are expected to provide their references by the closing date of the Scholarship (below). It is strongly recommended you complete this as soon as possible, as late or incomplete applications will not be considered.

Application Closing Date: 29 May 2026

References due no later than 05 June 2026

Selection process

Applications will be assessed by the project team. Shortlisted applicants may be requested to attend an Interview.

All scholarship awards are subject to candidates successfully securing admission to a PhD programme in the School of Social & Political Sciences. Successful applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.

Contact Details

Questions on the Application Portal only: College of Social Sciences Graduate School

Questions on the Project: Professor Alison Heppenstall