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    An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution

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    Date
    2017-06
    Author
    Elizondo, David;
    Orun, A.
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    Abstract
    Traffic flow related air pollution is one of the major problems in urban areas, and is often difficult to avoid it if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our introduced work here, an artificial intelligence technique such as Bayesian networks are used for a robust traffic data analysis and modelling. The most common challenge in traditional data analysis is a lack of capability of unveiling the hidden links between the distant data attributes (e.g. pollution sources, dynamic traffic parameters, geographic location characteristics, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long term basis.
    Description
    CCI Group has contributed to the research
    Citation : Elizondo, D. and Orun, A. (2017) An Intelligent traffic network optimisation by use of Bayesian inference methods to combat air pollution, TPM- Transport Practitioner’s Meeting Conference, 28-29 June 2017, Nottingham
    URI
    http://hdl.handle.net/2086/14084
    Research Group : DIGITS
    Research Institute : Institute of Artificial Intelligence (IAI)
    Research Institute : Leicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)
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    • School of Computer Science and Informatics [2970]

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