Influencing transport behaviour: a Bayesian modelling approach for segmentation of social surveys
Dawkins, L; Williamson, D; Barr, S; et al.Lampkin, S
Date: 7 June 2018
Journal
Journal of Transport Geography
Publisher
Elsevier
Publisher DOI
Abstract
Current approaches for understanding and influencing transport behaviour often involve
creating fixed, homogenous groups of similar surveyed individuals in order to explore specific
behavioural profiles, an approach known as segmentation. Most commonly, segmentation
is not based on a formal statistical model, but either a simple ‘a ...
Current approaches for understanding and influencing transport behaviour often involve
creating fixed, homogenous groups of similar surveyed individuals in order to explore specific
behavioural profiles, an approach known as segmentation. Most commonly, segmentation
is not based on a formal statistical model, but either a simple ‘a priori’ defined group classification
narrative, failing to capture the complexity of varying group characteristics, or
a ‘post hoc’ heuristic cluster analysis, applied to multi-dimensional behavioural variables,
creating complex descriptive group narratives. Here we present an alternative, Bayesian
finite mixture-modelling approach. A clear group narrative is created by constraining the
Bayesian prior to group survey respondents based on the predominance of a single apposing
transport behaviour, while a detailed insight into the behavioural complexity of each group
is achieved using regression on multiple additional survey questions. Rather than assuming
within group homogeneity, this creates a dynamic group structure, representing individual
level probabilities of group membership and within group apposing travel behaviours. This
approach also allows for numerical and graphical representation of the characteristics of
these dynamic, clearly defined groups, providing detailed quantitative insight that would
be unachievable using existing segmentation approaches. We present an application of this
methodology to a large online commuting behaviour survey undertaken in the city of Exeter,
UK. Survey respondents are grouped based on which transport mode type they predominantly
commute by, and the key drivers of these predominant behaviours are modelled to
inform the design of behavioural interventions to reduce commuter congestion in Exeter.
Our approach allows us to prioritise the most effective intervention themes, and quantify
their potential effect on motor vehicle usage. For example, we identify that individuals that
predominantly commute by public transport, but also sometimes motor vehicle, do so on
average up to one day per week less often, if they are strongly concerned about the environment,
demonstrating how an intervention to promote environmental awareness could greatly
reduce motor vehicle usage within this group.
Geography - old structure
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Except where otherwise noted, this item's licence is described as © 2018 The Authors. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).