TAKEN 5. Reinforcement Learning for Mobility Under Traffic Disruptions

Traffic networks can be significantly affected by unexpected events such as accidents, road closures or sudden congestion. Conventional route-planning approaches may struggle to adapt efficiently to these changes. This project investigates whether reinforcement learning can learn adaptive routing strategies that respond to unexpected disruptions. The learned approach will be evaluated in a simulated transport network … Read more

TAKEN 4. Robust Multi-Agent Reinforcement Learning for Cooperative Lane Changing

Vehicles approaching highway merges or lane-changing situations need to coordinate their actions to maintain safety and traffic efficiency. This project investigates whether multiple reinforcement learning agents can learn cooperative lane-changing strategies. Different training approaches will be compared with conventional rule-based strategies. The project will evaluate their impact on traffic safety, travel time and overall traffic … Read more

TAKEN 3. Reinforcement Learning for Sustainable Route Choice in Urban Mobility

Choosing the best route through a city is challenging because traffic conditions change continuously. Reinforcement learning (RL) provides a way for an agent to learn effective route-selection strategies by interacting with a simulated transport network. This project will investigate whether an RL-based approach can learn to select routes that reduce travel time and congestion compared … Read more

TAKEN 2 – Personalised Reinforcement Learning for Mobility Recommendations

Different travellers may have different preferences when choosing how and when to travel. While some may prioritise travel time, others may prefer cheaper, less crowded or more environmentally friendly options. This project investigates whether reinforcement learning can be used to learn personalised mobility recommendations based on individual preferences and feedback. The project will explore how … Read more

TAKEN 1 – Efficient Multi-agent Reinforcement Learning for Cooperative Route Planning

In cooperative multi-agent reinforcement learning, several agents have to learn how to work together. A challenge is that they can sometimes settle on a reasonably good way of cooperating, even though a much better joint solution exists. This project investigates whether  the way learning experiences are selected and used during training could influence which solution … Read more

TAKEN 7 – Cooperative CAV decision-making using Graph Neural Networks

Before fully autonomous driving is achieved, connected autonomous vehicles (CAVs) will operate for a certain period in mixed traffic, which includes both CAVs and human-driven vehicles (HDVs). The dynamic and interactive conditions in mixed traffic scenarios renders CAV decision making particularly challenging. This project will investigate the use of Graph Convolutional  Deep Reinforcement Learning for … Read more

Fast Conflict Onset Forecasting with Automatic Identification System Data

Forecasting the onset of conflict at a fine-grained temporal level has important implications for early-warning systems and can provide timely information to policymakers, humanitarian organisations, and peacekeeping actors. Typical conflict prediction methods rely on variables such as political instability, economic conditions, population characteristics, or historical conflict patterns. However, this data is often relatively coarse in … Read more

Machine learning for fibre disturbance detection via state of polarisation sensing – Taken, no longer available

Project Description: Optical fibre networks are critical to global connectivity, yet they are susceptible to accidental damage or deliberate tampering. While distributed acoustic sensing (DAS) has been widely investigated for disturbance detection, an alternative approach relies on monitoring the state of polarisation (SOP) of light propagating in the fibre. The SOP is highly sensitive to … Read more

Machine learning application to fibre sensing in maritime applications – Taken, no longer available

Project Description: Recent advances in distributed fibre sensing have shown that existing optical fibre cables, originally deployed for telecommunications, can also serve as dense sensor arrays. By sending probe signals through the fibre and analysing the backscattered light, it is possible to extract information about environmental changes such as vibrations, temperature, and strain along the … Read more