Discovery Early Career Researcher Award - Grant ID: DE170101180
Funder
Australian Research Council
Funding Amount
$327,900.00
Summary
Understanding and preventing road deaths using coronial investigations. This project aims to study coronial death investigations of fatal road crashes in Australia using public health and road safety theoretical frameworks. Fatal road crashes are sudden, unexpected and violent. Each fatality has a lasting effect resulting in immeasurable emotional costs and a financial burden in excess of $3.8 billion per year. Intended outcomes will contribute to understanding of fatal road crashes including pr ....Understanding and preventing road deaths using coronial investigations. This project aims to study coronial death investigations of fatal road crashes in Australia using public health and road safety theoretical frameworks. Fatal road crashes are sudden, unexpected and violent. Each fatality has a lasting effect resulting in immeasurable emotional costs and a financial burden in excess of $3.8 billion per year. Intended outcomes will contribute to understanding of fatal road crashes including pre-crash social factors (e.g. alcohol/drug use and dependence, unemployment, age), the use and effect of coronial recommendations on road safety policy and practice, and preventing deaths on Australian roads.Read moreRead less
Sustainable mobility: city-wide exposure modelling to advance bicycling. This project aims to develop a world-leading platform for city-wide modelling of cycling exposure. This project will provide unparalleled insights into cycling exposure by combining multiple cycling data sources through the use of advanced spatial statistical and machine learning techniques. The expected outcomes of this project are a novel inventory of cycling infrastructure, a cycling route choice modelling system and rob ....Sustainable mobility: city-wide exposure modelling to advance bicycling. This project aims to develop a world-leading platform for city-wide modelling of cycling exposure. This project will provide unparalleled insights into cycling exposure by combining multiple cycling data sources through the use of advanced spatial statistical and machine learning techniques. The expected outcomes of this project are a novel inventory of cycling infrastructure, a cycling route choice modelling system and robust predictions of cycling volumes on individual streets. This project will deliver a step change in cycling that will lead to increased cycling participation, enhanced safety, and improved infrastructure planning, thereby resulting in substantial gains in population and environmental health.Read moreRead less