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

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.