Personalizing travel behaviour change interventions using the trans-theoretical model and multimodality data

IF 5.1 3区 工程技术 Q1 TRANSPORTATION
Warnakulasooriya Umesh Ashen Lowe, Leonhard Lades, Páraic Carroll
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引用次数: 0

Abstract

Behaviourally informed soft policies, such as nudges, have become popular in areas like health, environment, and energy use as cost-effective instruments to change behaviour and decision-making. However, the effectiveness of soft policies in the transport sector is modest at best. One reason for this relative ineffectiveness might be their one-size-fits-all nature, and personalizing soft interventions has been suggested to increase their effectiveness. The Trans-theoretical Model (TTM) suggests that people progress through five stages of behavioural change, from pre-contemplating a behaviour to maintaining the behaviour, and behavioural interventions could be designed for specific stages. However, it is not always feasible to conduct surveys to place people at different stages of the TTM. This paper explores whether it is possible to use multimodality data taken from a travel diary to place people at different stages of the TTM. The analysis uses an existing dataset from 826 respondents that includes self-reported TTM stages regarding cycling and data on multimodality. In the analysis, the multimodality data are used to allocate respondents to categories and assign them to TTM stages. The performances of the stage assignment approaches are evaluated using the self-reported TTM data and confusion matrices. The accuracy of the allocation of participants to TTM stages using multimodality data is approximately 75%. The accuracy is higher for early stages (pre-contemplation) and later stages (maintenance) of the TTM. A data-driven approach to dealing with multimodality data performs slightly better than an approach that relies on pre-defined categorization. The paper suggests that it will be possible in the future to personalise behavioural interventions according to the stages of the TTM even in the absence of self-reported survey data that classifies people to TTM stages if objective multimodality data are available.
利用跨理论模型和多模态数据,个性化改变出行行为的干预措施
在健康、环境和能源使用等领域,以行为为导向的软性政策,如 "劝导",作为改变行为和决策的成本效益工具,已变得十分流行。然而,软性政策在交通领域的效果充其量只能算一般。造成这种相对低效的原因之一可能是其 "一刀切 "的性质,而个性化的软干预措施被认为可以提高其有效性。跨理论模型(TTM)认为,人们的行为变化会经历五个阶段,从预先考虑行为到保持行为,行为干预措施可针对特定阶段进行设计。然而,将人们置于 TTM 的不同阶段进行调查并不总是可行的。本文探讨了是否有可能利用旅行日记中的多模态数据将人们置于 TTM 的不同阶段。分析使用了 826 名受访者的现有数据集,其中包括有关骑自行车的自我报告 TTM 阶段和有关多模式的数据。在分析中,多模态数据用于将受访者划分为不同类别,并将他们归入 TTM 阶段。使用自我报告的 TTM 数据和混淆矩阵对阶段分配方法的性能进行了评估。使用多模态数据将参与者分配到 TTM 阶段的准确率约为 75%。对于 TTM 的早期阶段(前思索)和后期阶段(维持),准确率更高。数据驱动的多模态数据处理方法比依赖预定义分类的方法略胜一筹。本文表明,如果有客观的多模态数据,即使没有将人们划分到 TTM 阶段的自我报告调查数据,将来也有可能根据 TTM 阶段进行个性化的行为干预。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European Transport Research Review
European Transport Research Review Engineering-Mechanical Engineering
CiteScore
8.60
自引率
4.70%
发文量
49
审稿时长
13 weeks
期刊介绍: European Transport Research Review (ETRR) is a peer-reviewed open access journal publishing original high-quality scholarly research and developments in areas related to transportation science, technologies, policy and practice. Established in 2008 by the European Conference of Transport Research Institutes (ECTRI), the Journal provides researchers and practitioners around the world with an authoritative forum for the dissemination and critical discussion of new ideas and methodologies that originate in, or are of special interest to, the European transport research community. The journal is unique in its field, as it covers all modes of transport and addresses both the engineering and the social science perspective, offering a truly multidisciplinary platform for researchers, practitioners, engineers and policymakers. ETRR is aimed at a readership including researchers, practitioners in the design and operation of transportation systems, and policymakers at the international, national, regional and local levels.
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