Multi-agent Reinforcement Learning for Travel Time Reliability

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

Implementing Slot-based Driving on Mobile Robots – TAKEN

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

Swarm Intelligence for Travel Time Reliability

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

Reinforcement Learning for Motorway Demand Management

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