Abstract
SummaryThe 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.
Highlights
High levels of commuter congestion negatively impact both the quality of the environment and societal health and wellbeing
Exeter has become the test bed for a new ‘smart cities’ methodology which aims to reduce commuter congestion. This methodology is being developed within the Innovate UK ‘Engaged smart transport’ (EST) project, which is a collaboration between statisticians and social scientists at the University of Exeter, the City and County Councils, and a large consortium of industrial partners
Posterior samples of α quantify the relationship between group identifiers’ (GIs) and the probability of group allocation, identifying the key characteristics that differ between the five groups
Summary
High levels of commuter congestion negatively impact both the quality of the environment and societal health and wellbeing. The city of Exeter, UK, is experiencing unprecedented economic and physical growth, with the population of greater Exeter set to increase by as much as 50% by 2026 (Exeter City Council, 2015) This growth will put further pressure on current infrastructure and presents a significant challenge in meeting and maintaining air quality standards (Exeter City Council, 2015). Exeter has become the test bed for a new ‘smart cities’ methodology which aims to reduce commuter congestion This methodology is being developed within the Innovate UK ‘Engaged smart transport’ (EST) project, which is a collaboration between statisticians and social scientists at the University of Exeter, the City and County Councils, and a large consortium of industrial partners
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