Motorcyle Helmet Selection
Helmet loss in collisions exposes the wearer to a high risk of long-term brain injury. Children and adolescents are at risk because they are provided with helmets that are wholly inadequate for the level of protection that they require.
While a large amount of research has been conducted on theoretically optimising helmet fit, there appears to be no work published on the consequence of poor helmet choice.
A large number of helmet users are wearing sub-optimal products because they are the wrong shape or not secure. This project will be modelling head shapes and analysing the difference between subjective and objective fit. Ultimately we aim to produce advice for all potential helmet wearers on how to select the best helmet for their protection.
Road traffic collisions (RTCs) are a leading cause of death and serious injury, with traumatic brain injuries (TBI) a leading injury type. The embedded sensors on a vehicle could be used to indicate trauma severity and rapidly direct the deployment of emergency service resources.
The purpose of this multi-disciplinary research is to therefore explore the feasibility of using such data, recorded via the event data recorder (EDR) of the vehicle, to instantaneously indicate the severity of TBI experienced in a collision.
AutoTRIAGE is a jointly funded PhD project with Imperial College London, at the end of which there will be a transfer of technology directly to the automotive/collision investigation industries.
This work will ultimately enable emergency services to provide better care to persons injured in a road traffic collision, reducing the impact to the individual, and reducing healthcare costs.
Developing a novel simulator to provide cost-benefit analysis for road and rail projects
TRL has a proven cost-benefit analysis tool called TRRIO, which was originally designed for the railway industry. This project will modify the tool as a macro-micro simulator for the road industry. Key changes to the tool will involve development of control variables which were previously fixed assumptions, such as operational demand, revenue fluctuations, energy usage and emissions. This level of granularity will mean that the simulator can calculate details required to assess the impact of policy decisions, or when evaluating new proposals.
Modelling tools like TRRIO support transport operators developing strategies to minimise energy consumption and emissions, and provide the evidence for investment decisions and technology choices.