Unifying Traffic Modelling and Safety Management for Safer and Faster Roads. This project aims to balance road safety and efficiency as conflicting goals of transport systems mixed with connected and automated vehicles (CAVs). This project is expected to generate fundamental knowledge on operational algorithms and analytics for CAVs and develop innovative tools for operating them. Expected outcomes include ground-breaking models capable of the co-estimation of efficiency and safety impacts of CA ....Unifying Traffic Modelling and Safety Management for Safer and Faster Roads. This project aims to balance road safety and efficiency as conflicting goals of transport systems mixed with connected and automated vehicles (CAVs). This project is expected to generate fundamental knowledge on operational algorithms and analytics for CAVs and develop innovative tools for operating them. Expected outcomes include ground-breaking models capable of the co-estimation of efficiency and safety impacts of CAVs, and control strategies to safely and efficiently integrate CAVs into existing transport systems. This should provide significant safety and efficiency benefits that currently cost about 1160 lives and 1.25 billion hours of congestion per year, and make Australia better prepared for the connected and automated vehicle era.Read moreRead less
Predictive Analytics and Real-time Traffic Control for Urban Corridors. This project aims to develop predictive data analytics and real-time traffic control and safety models for multimodal management of urban corridors, serving two salient objectives: (1) optimising person-throughput of multimodal traffic; while (2) minimising safety risks for all modes. The outcome will be an automated, sensor-based platform to monitor traffic flows from all modes and make proactive and coordinated control dec ....Predictive Analytics and Real-time Traffic Control for Urban Corridors. This project aims to develop predictive data analytics and real-time traffic control and safety models for multimodal management of urban corridors, serving two salient objectives: (1) optimising person-throughput of multimodal traffic; while (2) minimising safety risks for all modes. The outcome will be an automated, sensor-based platform to monitor traffic flows from all modes and make proactive and coordinated control decisions in real-time. The expected benefits are profound; the developed algorithms and platform will significantly reduce traffic congestion, travel delays and safety risks for all modes of transport, especially for vulnerable road users (e.g. pedestrians and cyclists).Read moreRead less
A real-time traffic signal system for safe and efficient intersections . Road traffic crashes result in 1,200 fatalities and another 36,500 injuries on Australian roads each year. Signalised intersections represent a high-risk node in a transportation network, but the current signal designs only consider efficiency but not safety. This project aims to unleash the power of artificial intelligence (AI) and integrate with the advanced extreme value models for proactive and efficient detection of cr ....A real-time traffic signal system for safe and efficient intersections . Road traffic crashes result in 1,200 fatalities and another 36,500 injuries on Australian roads each year. Signalised intersections represent a high-risk node in a transportation network, but the current signal designs only consider efficiency but not safety. This project aims to unleash the power of artificial intelligence (AI) and integrate with the advanced extreme value models for proactive and efficient detection of crash risk in real-time. Its innovations lie on developing a novel traffic signal control system balancing safety and efficiency of signalised intersections. The proposed real-time traffic signal system will fundamentally transform the intersection operation and lead to reductions of road fatalities, injuries and emissions.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
Discovery Early Career Researcher Award - Grant ID: DE200101079
Funder
Australian Research Council
Funding Amount
$426,241.00
Summary
Safe distractions? Taking the danger out of competing activities. Distracted driving is an increasing safety concern in Australia and worldwide. Smartphones play key roles in today’s professional and social contexts and current road safety policies based on stopping their use while driving have shown little success. Distraction is predicted to be an even greater issue in new semi-automated vehicles. This project proposes an innovative approach that will enable safe engagement in competing tasks ....Safe distractions? Taking the danger out of competing activities. Distracted driving is an increasing safety concern in Australia and worldwide. Smartphones play key roles in today’s professional and social contexts and current road safety policies based on stopping their use while driving have shown little success. Distraction is predicted to be an even greater issue in new semi-automated vehicles. This project proposes an innovative approach that will enable safe engagement in competing tasks while driving non-automated and semi-automated vehicles. The outcomes will underpin the development of new technologies to reduce the potential adverse effects of these distractions and thus reduce deaths and serious injuries, representing significant cost savings to the health system and the community.Read moreRead less
Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project inclu ....Driving Towards Greener and Safer Roads using Big Spatiotemporal Data. This project aims to design novel techniques for using big spatiotemporal data to reduce the impact of road transport on the environment and improve road safety. This project expects to address key challenges and lay scientific foundations of using the big data for developing a next-generation eco-friendly navigation system and increasing situational awareness for road transport safety. Expected outcomes of this project include novel big data management and analytics techniques, and new edge computing models for vehicular networks. The success of this project should bring several key benefits including reducing greenhouse gas emissions on roads, facilitating urban planning, and improving road safety.Read moreRead less
Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of th ....Using visual science to reduce the dangers of night driving. This project aims to develop novel tests of visual function relevant to the modern night driving environment. Night driving is challenging for all drivers and has been linked to poor visibility under low light conditions. This project will characterise the visual challenges of the modern night driving environment, develop visual tests that incorporate the dynamic light levels typical of night-time roads and assess the association of these tests with night driving performance. The outcomes will contribute new knowledge regarding dynamic visual processing and the ageing visual system and will inform vision testing, potential interventions to improve visual function for night driving and reduce the dangers of night driving.Read moreRead less
A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driv ....A safety-preserving ecosystem for autonomous driving. In this project, Macquarie University will collaborate with UTS and SilverQuest to develop an innovative safety-preserving ecosystem for autonomous driving. This system will not only be adopted by SilverQuest’s customers (automotive companies) to secure their latest autonomous driving models, but also be commercialised as a toolset that can be plugged into existing autonomous vehicles to detect and prevent malicious attacks on autonomous driving models. The project will lead to two innovations: in theory design an attack detection and prevention ecosystem for autonomous driving and in application implement a safety analysis toolset for industry-scale autonomous systems.Read moreRead less
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
Understanding and improving sustained attention under vigilance conditions. This project aims to address a major global challenge caused by technological advances: human operators have to monitor computer-control (e.g., in autonomous vehicles, rail and airtraffic control) but sustaining attention is very difficult under these conditions. Developing innovative behavioural and neural methods, this internationally collaborative project bridges basic and applied science to understand lapses of atten ....Understanding and improving sustained attention under vigilance conditions. This project aims to address a major global challenge caused by technological advances: human operators have to monitor computer-control (e.g., in autonomous vehicles, rail and airtraffic control) but sustaining attention is very difficult under these conditions. Developing innovative behavioural and neural methods, this internationally collaborative project bridges basic and applied science to understand lapses of attention under monitoring conditions. It creates a novel intervention, based on brain activity patterns, to improve performance. Outcomes will increase our neural understanding of attention and lay a foundation for a novel system to detect lapses of attention in high-risk environments, preventing errors before they occur.Read moreRead less