Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA

Abstract

Exposure to air pollution in the form of fine particulate matter (${\mathrm{PM}}_{2.5}$) is known to cause diseases and cancers. Consequently, the public are increasingly seeking health warnings associated with levels of ${\mathrm{PM}}_{2.5}$ 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 ${\mathrm{PM}}_{2.5}$ 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.