'What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys. (23rd August 2019)

• Record Type: Journal Article
• Authors: Dawkins, Laura C.; Williamson, Daniel B.; Barr, Stewart W.; Lampkin, Sally R.
• Is Part Of: Journal of the Royal Statistical Society. Volume 183:Number 1(2020)
• Page Start: 251
• Page End: 280
• Publication Date: 2019-08-23
• Subjects: Bayesian modelling -- Smart cities -- Subjective priors -- Survey analysis -- Transport; Social sciences -- Statistical methods -- Periodicals; Statistics -- Periodicals; 300.15195
• DOI: 10.1111/rssa.12499
• Languages: English
• ISSNs: 0964-1998

Abstract

Summary: The city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large on‐line survey, and similar individuals have been grouped to explore distinct behaviour profiles to inform intervention design to reduce commuter congestion. Statistical analysis within societal applications benefit from incorporating available social scientist expert knowledge. Current clustering approaches for the analysis of social surveys assume that the number of groups and the within‐group narratives are unknown a priori . Here, however, informed by valuable expert knowledge, we develop a novel Bayesian approach for creating a clear opposing transport mode group narrative within survey respondents, simplifying communication with project partners and the general public. Our methodology establishes groups characterizing opposing behaviours based on a key multinomial survey question by constraining parts of our prior judgement within a Bayesian finite mixture model. Drivers of group membership and within‐group behavioural differences are modelled hierarchically by using further information from the survey. In applying the methodology we demonstrate how it can be used to understand the key drivers of opposing behaviours in any wider application.