Where Is the clean air? A bayesian decision framework for personalised cyclist route selection using R-INLA

Dawkins, Laura C., Williamson, Daniel B., Mengersen, Kerrie L., Morawska, Lidia, Jayaratne, Rohan and Shaddick, Gavin ORCID: https://orcid.org/0000-0002-4117-4264 2021. Where Is the clean air? A bayesian decision framework for personalised cyclist route selection using R-INLA. Bayesian Analysis 16 (1) , pp. 61-91. 10.1214/19-ba1193

Full text not available from this repository.

Abstract

Exposure to air pollution in the form of fine particulate matter (PM2.5) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of PM2.5 using mobile phone applications and websites. Often, these existing platforms provide one-size-fits-all guidance, not incorporating user specific personal preferences. This study demonstrates an innovative approach using Bayesian methods to support personalised decision making for air quality. We present a novel hierarchical spatio-temporal model for city air quality that includes buildings as barriers and captures covariate information. Detailed high resolution PM2.5 data from a single mobile air quality sensor is used to train the model, which is fit using R-INLA to facilitate computation at operational timescales. A method for eliciting multi-attribute utility for individual journeys within a city is then given, providing the user with Bayes-optimal journey decision support. As a proof-of-concept, the methodology is demonstrated using a set of journeys and air quality data collected in Brisbane city centre, Australia.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	?? VCO ??
Publisher:	International Society for Bayesian Analysis (ISBA)
ISSN:	1936-0975
Last Modified:	01 Aug 2024 13:30
URI:	https://orca.cardiff.ac.uk/id/eprint/170724

Actions (repository staff only)

Edit Item