Discovery Early Career Researcher Award - Grant ID: DE210100602
Funder
Australian Research Council
Funding Amount
$424,000.00
Summary
Market Design of Next Generation of Shared and Automated Transport Services. This project aims to develop novel quantitative models and market design methods to fundamentally transform the analysis, control and regulation of shared and automated point-to-point transport services in multimodal networks. The project offers an innovative non-equilibrium approach that models multiple competitive transport platforms, travellers, freelancer drivers and transport legislator entity to ensure achieving s ....Market Design of Next Generation of Shared and Automated Transport Services. This project aims to develop novel quantitative models and market design methods to fundamentally transform the analysis, control and regulation of shared and automated point-to-point transport services in multimodal networks. The project offers an innovative non-equilibrium approach that models multiple competitive transport platforms, travellers, freelancer drivers and transport legislator entity to ensure achieving social welfare. The project outcomes address the eventual transition towards automation where platforms own and utilise different proportions of AVs in their fleet. The project expects to generate new knowledge of transport science that can be used to lessen social, economic and environmental impacts of private car ownership.
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: 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