Discovery Early Career Researcher Award - Grant ID: DE220100052
Funder
Australian Research Council
Funding Amount
$437,020.00
Summary
Impacts of the apartment boom on public transport in Australian cities. This project aims to investigate the impacts of high density housing on public transport use and service provision to directly inform policy and practice. Recent growth in high density housing along public transport corridors is associated with overcrowded public transport services in Australian cities, yet this complex and interconnected relationship is not well understood. This project expects to generate new knowledge in ....Impacts of the apartment boom on public transport in Australian cities. This project aims to investigate the impacts of high density housing on public transport use and service provision to directly inform policy and practice. Recent growth in high density housing along public transport corridors is associated with overcrowded public transport services in Australian cities, yet this complex and interconnected relationship is not well understood. This project expects to generate new knowledge in the field of transport and land use integration and produce much needed cross-sectional and longitudinal evidence of the impacts of the apartment boom on public transport. Anticipated benefits include reduced overcrowding on public transport, improved travel choices and enhanced liveability in Australian cities.Read moreRead less
Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This pro ....Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This project aims to design an urban trip advisory system that could be followed by automated vehicles as well as human drivers, to reduce the financial and environmental cost of current urban congestion.Read moreRead less
Promoting active travel and public transport for a post-pandemic world. In many major cities, COVID-19 stimulated the provision of open streets, pop up bike lanes and widened pedestrian access, prompting unprecedented increases cycling and walking. While this type of infrastructure has always been supported by urban planners and designers, the pandemic has served as a vital inflection point, enabling cities to pursue long-term sustainable transport initiatives, including investment in Active Tra ....Promoting active travel and public transport for a post-pandemic world. In many major cities, COVID-19 stimulated the provision of open streets, pop up bike lanes and widened pedestrian access, prompting unprecedented increases cycling and walking. While this type of infrastructure has always been supported by urban planners and designers, the pandemic has served as a vital inflection point, enabling cities to pursue long-term sustainable transport initiatives, including investment in Active Travel (AT). There is an opportunity to promote AT as part of an integrated transport strategy, and to develop tools for the robust evaluation of AT impacts to inform future investment strategies. This proposal will provide our partner organisation Transport for New South Wales (with the knowledge required to achieve this.
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Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-s ....Robust and Explainable 3D Computer Vision. Computer vision is increasingly relying on deep learning which is fragile, opaque and fails catastrophically without warning. This project aims to address these problems by developing new theory in graph representation of 3D geometric and image data, hierarchical graph simplification and novel modules designed specifically for deep learning over geometric graphs. Using these modules, it aims to design graph convolutional network architectures for self-supervised learning that are robust to failures and provide explainable decisions for object detection and scene segmentation. The outcomes are expected to advance theory in robust deep learning and benefit 3D mapping, surveying, infrastructure monitoring, transport and robotics industries.Read moreRead less