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Scheme : Linkage Projects
Field of Research : Civil Engineering
Research Topic : Safety
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  • Researchers (41)
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  • Funded Activity

    Linkage Projects - Grant ID: LP0233238

    Funder
    Australian Research Council
    Funding Amount
    $112,725.00
    Summary
    Assessing the full impacts of high efficiency heavy vehicles in urban traffic networks using new analytical tools. Significant economic benefits result from new innovative heavy vehicles, which are able to carry up to twice the payload of standard semi-trailers. In addition, those vehicles have the potential to reduce overall accidents and evironmental degradation. Those benefits need to be traded-off against the impacts on other road users. This research will improve the operation of such ve .... Assessing the full impacts of high efficiency heavy vehicles in urban traffic networks using new analytical tools. Significant economic benefits result from new innovative heavy vehicles, which are able to carry up to twice the payload of standard semi-trailers. In addition, those vehicles have the potential to reduce overall accidents and evironmental degradation. Those benefits need to be traded-off against the impacts on other road users. This research will improve the operation of such vehicles in regional centres by producing: (1) an assessment methodology to deal with options for network efficiency gains which will capture the full impacts; and (2) a computer modelling tool based on fundamental research to quantify those impacts, in terms of delays and accident rates.
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    Funded Activity

    Linkage Projects - Grant ID: LP120100343

    Funder
    Australian Research Council
    Funding Amount
    $360,000.00
    Summary
    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.
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    Funded Activity

    Linkage Projects - Grant ID: LP0347911

    Funder
    Australian Research Council
    Funding Amount
    $195,000.00
    Summary
    Traffic microsimulation of ITS implementations in CBD road networks. This research project will investigate the advantages and disadvantages of using Intelligent Transport Systems (ITS) technologies in an Australian Central Business District (CBD) environment. It is often assumed that road networks and especially high activity areas such as CBDs can benefit significantly from ITS implementations. Traditionally ITS impacts have been difficult to quantify due to difficulties in isolating network .... Traffic microsimulation of ITS implementations in CBD road networks. This research project will investigate the advantages and disadvantages of using Intelligent Transport Systems (ITS) technologies in an Australian Central Business District (CBD) environment. It is often assumed that road networks and especially high activity areas such as CBDs can benefit significantly from ITS implementations. Traditionally ITS impacts have been difficult to quantify due to difficulties in isolating network effects and limitations with traditional traffic models. The project will overcome this difficulty by using a current state of the art traffic microsimulation model in order to test various ITS scenarios in a duplicate of a real world CBD.
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    Funded Activity

    Linkage Projects - Grant ID: LP0883807

    Funder
    Australian Research Council
    Funding Amount
    $423,585.00
    Summary
    Artificial Intelligence Based Deterioration Model for Development of Bridge Network Maintenance Strategy. The proposed AI-based methodology in conjunction with a Bridge Management System can tailor-make bridge deterioration models for a given bridge authority. The models so produced will enable effective BMS implementation which generates missing inspection records of past years, establishes optimal MR&R strategies and then reliably forecasts future bridge condition ratings. The methodology will .... Artificial Intelligence Based Deterioration Model for Development of Bridge Network Maintenance Strategy. The proposed AI-based methodology in conjunction with a Bridge Management System can tailor-make bridge deterioration models for a given bridge authority. The models so produced will enable effective BMS implementation which generates missing inspection records of past years, establishes optimal MR&R strategies and then reliably forecasts future bridge condition ratings. The methodology will be verified using available bridge datasets of QDMR and GCCC. The methodology is applicable to other bridge authorities throughout Australia and internationally to maintain ageing bridge stock.
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