Innovative urban traffic congestion solutions: optimising road space using networks of multi-class priority lanes. This project strengthens national approaches to a pervasive Australian problem; growing traffic congestion deteriorating liveability, environmental health and economic performance of the cities. This project improves approaches for traffic priority design to improve the efficiency of several class of vehicles and therefore, reducing traffic congestion.
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
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
Domain-driven information, quality assurance and interoperability for road transport systems. This project addresses the ever increasing need for improved information quality in the area of road transport and urban planning. Public bodies, like State and Federal authorities, require highly accurate sets of personal records as they are central authorities for the identification of individuals. This project will develop techniques for the enhancement of these records.
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.