Discovery Early Career Researcher Award - Grant ID: DE160101137
Funder
Australian Research Council
Funding Amount
$373,536.00
Summary
The whole is greater than its parts: Improving rail safety through teamwork. This project seeks to develop a train driving risk model that includes human factors, to enable rail organisations to better identify and mitigate safety risks. Train driving is a cognitively demanding task in which errors can quickly lead to catastrophic consequences. Signals passed at danger (SPADs) occur when a train goes past a red light. Despite significant investment in better signalling and communications infrast ....The whole is greater than its parts: Improving rail safety through teamwork. This project seeks to develop a train driving risk model that includes human factors, to enable rail organisations to better identify and mitigate safety risks. Train driving is a cognitively demanding task in which errors can quickly lead to catastrophic consequences. Signals passed at danger (SPADs) occur when a train goes past a red light. Despite significant investment in better signalling and communications infrastructure, SPAD rates remain unacceptably high and are projected to rise. SPAD risk is currently managed with a retrospective approach that fails to consider non-technical human factors such as time pressure, workload and team communications. By including non-technical dimensions, this project seeks to develop a comprehensive model to explain and prevent SPADs.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210100273
Funder
Australian Research Council
Funding Amount
$407,679.00
Summary
Supercomputing to understand track buckling and related train derailments. This project aims to understand the contributions of railway train forces to a dangerous and high-cost track dynamic behaviour called buckling; by developing a supercomputing method that unlocks the capability for large-scale 3D train-track interaction research for railway trains of up to 250 vehicles. This project expects to generate new knowledge regarding track buckling, train derailments and train-track dynamics. Expe ....Supercomputing to understand track buckling and related train derailments. This project aims to understand the contributions of railway train forces to a dangerous and high-cost track dynamic behaviour called buckling; by developing a supercomputing method that unlocks the capability for large-scale 3D train-track interaction research for railway trains of up to 250 vehicles. This project expects to generate new knowledge regarding track buckling, train derailments and train-track dynamics. Expected outcomes include a new supercomputing method for train-track dynamics and derailment research and a science-based technique to assess track buckling safety. This project should provide significant benefits to the rail industry including enhanced rail safety, lower maintenance costs and improved transport efficiency.Read moreRead less