A novel integrated motorway management system for less congested, more reliable and safer motorways. Motorway traffic congestion poses major economic, social and safety problems, which this project seeks to address through intelligent traffic management solutions as an alternative to massive infrastructure expansion. The project’s innovative traffic analysis and control system will reduce periods of congestion and increase driver safety.
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.
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
Incentivised strategic traffic assignment: bi-level transport optimisation. This project aims to advance the fundamental knowledge base and methodological modelling capacity related to traffic network assignment representing complex incentive structures such as network pricing, behavioural shift inducement, dynamic speed control and information-provision. Expected outcomes include new equilibrium formulations characterising traveller responses to, and interactions with, incentive structures whil ....Incentivised strategic traffic assignment: bi-level transport optimisation. This project aims to advance the fundamental knowledge base and methodological modelling capacity related to traffic network assignment representing complex incentive structures such as network pricing, behavioural shift inducement, dynamic speed control and information-provision. Expected outcomes include new equilibrium formulations characterising traveller responses to, and interactions with, incentive structures while maintaining complex stochastic adaptive behaviours from previous research, new network routing algorithms, and a novel bi-level optimisation approach for seeking optimal incentive policies. The project will provide a scientific basis for the quantified network evaluation of incentivisation strategies that will support enhanced transport planning thereby improving mobility across society.Read moreRead less
Methodologies for the incorporation of congestion propagation and system reliability into transport network models for consistent multi-scale planning. This project will improve the capabilities of transport planning techniques. Specifically, new methods will be introduced, which improve the realism of regional congestion modelling, and the mathematical representation of traveller decision-making, thereby permitting an improved long-term transport plan.
Design of micro-decisions in automated transport. This project aims to design methods and market algorithms for vehicle control to tackle traffic congestion with interactive micro-auctions, micro-tolling and cooperative games. Specifically, this project develops and designs incentives, auctions and behavioural and pricing rules to manipulate micro traffic dynamics such as lane-changing, merging, energy-efficient driving, and driving at intersections, in roads without defined lanes and shared spa ....Design of micro-decisions in automated transport. This project aims to design methods and market algorithms for vehicle control to tackle traffic congestion with interactive micro-auctions, micro-tolling and cooperative games. Specifically, this project develops and designs incentives, auctions and behavioural and pricing rules to manipulate micro traffic dynamics such as lane-changing, merging, energy-efficient driving, and driving at intersections, in roads without defined lanes and shared spaces to achieve collective macro benefits. The project targets mixed traffic where AVs and conventional human-driven vehicles interact and share the road. The project expects to generate new knowledge of transport science to lessen social, economic and environmental impacts of private cars.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
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: 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