"What drives commuter behaviour?": A Bayesian clustering approach for understanding opposing behaviours in social surveys

Abstract

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 online 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 the number of groups and the within group narratives to be 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 characterising opposing behaviours based on a key multinomial survey question by constraining parts of our prior judgement within a Abbreviations: EST, Engaged Smart Transport; GI, Group Identifier; BI, Behavioural Influencer; MV, Motor Vehicle; PT, Public Transport; Markov chain Monte Carlo, MCMC. 1 2 DAWKINS ET AL. Bayesian finite mixture model. Drivers of group membership and within-group behavioural differences are modelled hierarchically 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.