The long-term effects of autonomous cars on land use, access and travel . Historically new transport technologies have significantly changed urban form in Australian cities with important business, economic, congestion, social and environmental impacts. Autonomous cars are said to revolutionise tomorrows transport but no research has yet considered long term impacts on land use and city structure. This project explores how land use and travel will change adopting innovative land use and transp ....The long-term effects of autonomous cars on land use, access and travel . Historically new transport technologies have significantly changed urban form in Australian cities with important business, economic, congestion, social and environmental impacts. Autonomous cars are said to revolutionise tomorrows transport but no research has yet considered long term impacts on land use and city structure. This project explores how land use and travel will change adopting innovative land use and transport models. Outcomes will better prepare Australia for an autonomous travel future.Read moreRead less
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
Scalable urban traffic control framework driven by distributed information. This project aims to develop a mathematical framework for investigating the role of information interactions between traffic signal settings and choices made by road users. Traffic control is one of the oldest and most cost-effective solutions for the worsening congestion problem in many metropolitan areas. However, through addressing fundamental mathematical challenges, further gains can be achieved to improve traffic ....Scalable urban traffic control framework driven by distributed information. This project aims to develop a mathematical framework for investigating the role of information interactions between traffic signal settings and choices made by road users. Traffic control is one of the oldest and most cost-effective solutions for the worsening congestion problem in many metropolitan areas. However, through addressing fundamental mathematical challenges, further gains can be achieved to improve traffic control and combat congestion. The expected outcome will be insights into the use of information and algorithms that can provide efficient, robust and safe traffic network management.Read moreRead less
Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to deve ....Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.Read moreRead less
Adaptive Stochastic Dynamic Traffic Assignment. This project aims to address some of the limitations of dynamic transport network modelling in the planning process particularly related to traffic uncertainty, driver adaptivity and information-provision. Previous advances facilitate the proposed methods to introduce; new network routing algorithms that account for numerous increasingly important problem characteristics such as driver route-choice response to real-time information and uncertainty; ....Adaptive Stochastic Dynamic Traffic Assignment. This project aims to address some of the limitations of dynamic transport network modelling in the planning process particularly related to traffic uncertainty, driver adaptivity and information-provision. Previous advances facilitate the proposed methods to introduce; new network routing algorithms that account for numerous increasingly important problem characteristics such as driver route-choice response to real-time information and uncertainty; new formulations for the stochastic dynamic traffic assignment problem which employ the novel routing algorithms as sub-problems; and new methods for relevant bi-level optimisation transport applications such as network design and incident management.Read moreRead less
Road safety and Aboriginal people. This study will involve an in-depth examination of factors underlying the high involvement in road crashes by Aboriginal people in Australia. Using mixed methods in six communities across NSW and South Australia it will inform development of new programs aimed at closing the gap in this important area.
Discovery Early Career Researcher Award - Grant ID: DE160100449
Funder
Australian Research Council
Funding Amount
$336,000.00
Summary
Modelling mixed traffic of traditional, connected, and automated vehicles. This project plans to address the challenge of efficiently operating mixed traffic flow of traditional, connected and automated vehicles. The rapid advancement of technologies is currently turning connected and automated vehicles from science fiction into science fact. However, there are no existing traffic flow models capable of reproducing features of mixed traffic flow consisting of traditional, connected and automated ....Modelling mixed traffic of traditional, connected, and automated vehicles. This project plans to address the challenge of efficiently operating mixed traffic flow of traditional, connected and automated vehicles. The rapid advancement of technologies is currently turning connected and automated vehicles from science fiction into science fact. However, there are no existing traffic flow models capable of reproducing features of mixed traffic flow consisting of traditional, connected and automated vehicles. This project aims to address this knowledge deficit and develop an analytical tool able to accurately model mixed traffic flow. This new knowledge and model are prerequisites to effective operation and control of traffic flow of traditional, connected and automated vehicles.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100205
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Optimisation of transit priority in a transportation network. This project is aimed at developing an optimised approach to combine various types of public transport priority in an urban network which can be used by transport planners to increase the efficiency of traffic movements and reduce traffic congestion. The case study is the network of Brisbane including all arterial and local roads.
Real-time Analytics on Urban Trajectory Data for Road Traffic Management. This project aims to develop real-time analytics and data management capabilities that leverage large-scale urban trajectory data to provide road operators with real-time insights into population movements and enable data-driven, customer-centric network operations. Current traffic management practices rely heavily on aggregate vehicle count data from fixed road sensors, which have limitations in accurately measuring traff ....Real-time Analytics on Urban Trajectory Data for Road Traffic Management. This project aims to develop real-time analytics and data management capabilities that leverage large-scale urban trajectory data to provide road operators with real-time insights into population movements and enable data-driven, customer-centric network operations. Current traffic management practices rely heavily on aggregate vehicle count data from fixed road sensors, which have limitations in accurately measuring traffic demand and network congestion propagation. This project seeks to develop innovative technologies to use a wide variety of data sources, especially massive trajectories of vehicles moving across the network, to better understand people's travel demands and road usage patterns and thus better manage the transport system.
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Discovery Early Career Researcher Award - Grant ID: DE190101020
Funder
Australian Research Council
Funding Amount
$357,000.00
Summary
Data-driven simulation of large traffic networks using trajectory data. This project aims to develop a low-cost, data-driven framework that builds a traffic simulation model automatically and directly from vehicle trajectory data to enable rapid and reliable analysis of large-scale traffic networks. The project expects to generate new knowledge in the area of transport engineering using an innovative approach to inferring travel behaviours, movement patterns and traffic dynamics from increasingl ....Data-driven simulation of large traffic networks using trajectory data. This project aims to develop a low-cost, data-driven framework that builds a traffic simulation model automatically and directly from vehicle trajectory data to enable rapid and reliable analysis of large-scale traffic networks. The project expects to generate new knowledge in the area of transport engineering using an innovative approach to inferring travel behaviours, movement patterns and traffic dynamics from increasingly available urban trajectory data. Expected outcomes include improved decision support for urban planners and traffic operators and enhanced traffic management and incident response capabilities, providing significant social, economic and environment benefits through optimised road use and urban flow.Read moreRead less