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
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.
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