School of Computer Science and Statistics
Reinforcement learning (RL) and especially deep reinforcement learning have emerged as powerful paradigms for learning quasi-optimal sequential decision making strategies (in applications as diverse as game playing and urban traffic control). RL uses an approach based on’ trial and error’ in which agents learn by interacting with their environment. Most RL algorithms assume that the … Read more
Poor travel-time reliability, meaning that travel times for the same journey are highly variable and unpredictable, gives rise to similar negative impacts on the environment and the economy as does traffic congestion. Moreover, being able to offer a high degree of travel-time reliability will facilitate the uptake of sustainable road transportation including future public, shared, … Read more
Offering predictable journey times is important to the uptake of sustainable road transportation including future public, shared, and on-demand mobility services and to on-time delivery of goods. To achieve such predictability, the ClearWay [1] project at TCD is exploring ‘slot-based driving’ (SBD) as a strategy for active management of roads (especially highways). SBD abstracts traffic … Read more
Poor travel-time reliability, meaning that travel times for the same journey are highly variable and unpredictable, gives rise to similar negative impacts on the environment and the economy as does traffic congestion. Moreover, being able to offer a high degree of travel-time reliability will facilitate the uptake of sustainable road transportation including future public, shared, … Read more
An ideal bus route would offer highly predictable journey times to travellers so that the same journey taken at the same time of day on different occasions would take the same amount of time. While there are many sources of variability in journey times such as traffic conditions, passenger boarding and offloading time, and the … Read more
This project will investigate the use of reinforcement learning to develop a highway journey booking system incorporating a dynamic pricing strategy to allow traffic demand to be shaped in ways that will improve traffic efficiency and enhance sustainability by reducing emissions and fuel consumption. The goal will be to evaluate the potential benefit of the … Read more
This project aims at using sports analytics models to optimise decision-making in Fantasy Football. By integrating data from multiple sources, including player performance metrics, historical game data, and injury reports, etc., and using statistical analysis and machine learning algorithms you will predict player success and recommend optimal lineups.
A sparse matrix is a two-dimensional array where most of the elements are zero. If a very large sparse matrix is stored in normal array format, a huge amount of memory will be needed to store values that are almost all zero. Instead, large sparse matrices are normally stored in a compacted form, where only … Read more