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
Discovery Early Career Researcher Award - Grant ID: DE220100909
Funder
Australian Research Council
Funding Amount
$350,000.00
Summary
Innovative Soft-computing for Condition Assessment of Large Infrastructure. Health conditions of large infrastructure, such as bridges, have been difficult to determine due to their large scales, associated incomplete data and high uncertainties in measurement and system identification. This project will develop an innovative condition assessment method based on the advancements in structural dynamics analysis, multi-objective topology and soft-computing techniques, for reliably evaluating the h ....Innovative Soft-computing for Condition Assessment of Large Infrastructure. Health conditions of large infrastructure, such as bridges, have been difficult to determine due to their large scales, associated incomplete data and high uncertainties in measurement and system identification. This project will develop an innovative condition assessment method based on the advancements in structural dynamics analysis, multi-objective topology and soft-computing techniques, for reliably evaluating the health conditions of large infrastructure. The outcomes will enhance the current practices in infrastructure asset management to deliver timely retrofitting and extended life cycle. The development will provide benefits to Australia by enhancing operational efficiency and preventing catastrophic failure of infrastructure.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101625
Funder
Australian Research Council
Funding Amount
$430,075.00
Summary
Developing an Advanced Drive-by Bridge Inspection Technology . 72% of bridges in Australia were constructed before 1976. Currently bridges are inspected by biennial visual inspection which is expensive, time consuming and subjective. Considering the large number of defective bridges in Australia and around the world and the limited budget of road authorities, this project aims to develop a low-cost and robust bridge monitoring framework by advanced data analytics, solely based on the response of ....Developing an Advanced Drive-by Bridge Inspection Technology . 72% of bridges in Australia were constructed before 1976. Currently bridges are inspected by biennial visual inspection which is expensive, time consuming and subjective. Considering the large number of defective bridges in Australia and around the world and the limited budget of road authorities, this project aims to develop a low-cost and robust bridge monitoring framework by advanced data analytics, solely based on the response of a moving vehicle passing over the bridge, with no equipment to be installed on the bridge. The project is significant because it opens a new direction for sustainable monitoring of such ageing infrastructure, consequently resulting in the lower costs of maintenance, enhanced safety and extended asset life.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.
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: DE220101320
Funder
Australian Research Council
Funding Amount
$425,970.00
Summary
Rethinking traffic modelling for next generation city-scale networks. This project aims to develop an efficient traffic simulation model that enables data-informed traffic monitoring and automated model development, streamlining the fundamental transformation that next-generation cities will undergo in the coming decades. The project expects to generate new knowledge in traffic modelling by developing an innovative approach to inferring traffic conditions and traveller behaviour from diverse dat ....Rethinking traffic modelling for next generation city-scale networks. This project aims to develop an efficient traffic simulation model that enables data-informed traffic monitoring and automated model development, streamlining the fundamental transformation that next-generation cities will undergo in the coming decades. The project expects to generate new knowledge in traffic modelling by developing an innovative approach to inferring traffic conditions and traveller behaviour from diverse data feeds, and automating model calibration through an optimisation formulation. Expected outcomes address the eventual transition to smart cities and connected and autonomous vehicle technologies, providing significant social, economic and environmental benefits through optimal planning and effective operation schemes.Read moreRead less