'What Drives Commuter Behaviour?': A Bayesian Clustering Approach for Understanding Opposing Behaviours in Social Surveys
'What Drives Commuter Behaviour?': A Bayesian Clustering Approach for Understanding Opposing Behaviours in Social Surveys
复制标题
“什么驱动通勤者行为？”：用于理解社会调查中反对行为的贝叶斯聚类方法
DOI：
10.1111/rssa.12499
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Dawkins L
中科院分区：
文献类型：
--
作者：
Dawkins L
英国埃克塞特市正经历着前所未有的增长，给交通基础设施带来了压力。与交通网络管理一样，了解和影响通勤者的行为对减少拥堵也很重要。通过一项大型在线调查收集了有关当前通勤者行为的信息，并将类似的个人分组，以探索不同的行为特征，从而为减少通勤拥堵的干预设计提供信息。社会应用中的统计分析受益于整合可用的社会科学家专家知识。目前用于分析社会调查的聚类方法假定群体的数目和群体内的叙述是先验未知的。然而，在这里，通过宝贵的专家知识，我们开发了一种新的贝叶斯方法，用于在调查受访者中创建一个明确的反对运输模式组的叙述，简化与项目合作伙伴和公众的沟通。我们的方法建立了一个关键的多项调查问题的基础上，通过限制我们的先验判断的贝叶斯有限混合模型的部分特征的对立行为的群体。组成员和组内行为差异的驱动程序的层次模型，通过使用进一步的信息，从调查。在应用该方法时，我们展示了如何在更广泛的应用中使用它来理解对立行为的关键驱动因素。
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 unknowna 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.