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研究旅行者在行程规划情境下对旅行产品类别和激励措施的偏好。

Study of travellers' preferences towards travel offer categories and incentives in the journey planning context.

机构信息

Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia.

Cefriel, Milano, Italy.

出版信息

PLoS One. 2023 Apr 26;18(4):e0284844. doi: 10.1371/journal.pone.0284844. eCollection 2023.

Abstract

Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers' expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners.

摘要

如今,鼓励人们改变出行行为,选择环保和积极的交通方式的努力正在加强。一个有前途的解决方案是增加可持续公共交通方式的使用。目前,这一解决方案面临的一个重大挑战是实施行程规划器,以便向旅行者提供可用的出行解决方案,并通过个性化技术帮助他们做出决策。本文为行程规划器开发人员提供了一些有价值的提示,说明如何定义和优先考虑出行方案类别和激励措施,以满足旅行者的期望。分析的数据是从 H2020 RIDE2RAIL 项目在几个欧洲国家进行的调查中获得的。结果证实,旅行者更愿意尽量减少旅行时间并按时到达。此外,价格折扣或舱位升级等激励措施可能在影响出行方案选择方面发挥关键作用。通过应用回归分析,发现旅行方案类别和激励措施的偏好与一些人口统计或旅行相关因素有关。结果还表明,对于特定的出行方案类别和激励措施,显著因素的子集差异很大,这强调了个性化推荐在行程规划器中的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb55/10132637/1bf8077cc348/pone.0284844.g001.jpg

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