Browsing by Author "Dawkins L"

Now showing 1 - 4 of 4
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    Engaging publics in the transition to smart mobilities
    (Springer, 2023-06-09) Lampkin S; Barr S; Williamson D; Dawkins L
    Commercial and public sector interests surrounding technological developments are promoting a widespread transition to autonomous vehicles, intelligent transportation systems and smart phone communications in everyday life, as part of the smart mobility agenda. There is, however, inadequate understanding about the impact of such a shift on potential users, their readiness to engage and their vision of transportation systems for the future. This paper presents the findings from a series of citizen panels, as part of a 2-year project based in south-west England, focusing on in-depth discussions regarding the future of commuting, the flow of the daily commute and the inclusion of publics in smart mobility planning. The paper makes three key propositions for researchers: enabling publics should lead to a visionary evolution in the development of sustainable transportation systems; commercial interests, public bodies and IT innovators must employ a holistic approach to mobility flows; and, processes engaging publics need to be inclusive when co-creating solutions in the transition to smart mobilities.
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    ‘I feel the weather and you just know’. Narrating the dynamics of commuter mobility choices
    (Elsevier B.V., 2022-07-18) Barr S; Lampkin S; Dawkins L; Williamson D
    Efforts to promote travel behaviour change have frequently deployed social marketing strategies that are based on characterising populations into discrete target groups through quantitative segmentation techniques. Such techniques provide an important basis for understanding behavioural choices and motivations, frequently using psychological constructs that can be used for planning interventions. However, there are limitations to what a solely quantitative approach can offer practitioners in terms of understanding the dynamics of travel behaviour and the meanings associated with personal mobility that can be used to design appropriate interventions. In this paper we provide evidence to argue for a mixed-methods approach, where insights from quantitative segmentation and qualitative data can be used to reveal the experiential nature of factors that influence travel decision making. To pursue this argument we present findings from research with commuters in the city of Exeter, South West England. Using data from five workshops, we illustrate the ways in which participants articulated and gave meaning to a series of travel mode influences identified using quantitative segmentation techniques for specific commuter groups (private car, public transport, walking, cycling and a combination of modes). We demonstrate how both understanding the dynamism of travel behaviour and revealing its meanings can present opportunities for designing interventions, offering pathways to promote shifts away from carbon intensive transport.
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    Influencing transport behaviour: A Bayesian modelling approach for segmentation of social surveys
    (Elsevier B.V., 2018-06-07) Dawkins L; Williamson DB; Barr SW; Lampkin SR
    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 multidimensional 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.
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    ‘What drives commuter behaviour?': a Bayesian clustering approach for understanding opposing behaviours in social surveys
    (Oxford University Press, 2019-08-23) Dawkins L; Williamson DB; Barr S; Lampkin SR
    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.