Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas

Date

2018-05-26

Advisors

Journal Title

Journal ISSN

ISSN

1361-9209

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Vehicular Air pollution, Artificial Intelligence, predictive modelling, GIS

Citation

Orun, A., Elizondo, D., Goodyer, E. and Paluszczyszyn, D. (2018) Use of Bayesian Inference Method to Model Vehicular Air Pollution in Local Urban Areas. Transportation Research Part D:Transport and Environment, 63, pp.236-243.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)
Leicester Institute for Pharmaceutical Innovation - From Molecules to Practice (LIPI)