驾驶自动驾驶汽车时如何处理通勤时间?陈述意图实验的结果

IF 6.3 1区 工程技术 Q1 ECONOMICS
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引用次数: 0

摘要

自动驾驶技术的快速发展和自动驾驶汽车(AVs)的普及有望改变人们的习惯出行模式。完全自动驾驶车辆(FAVs)不需要用户操控,这意味着用户可以在自动驾驶车辆将其送达目的地时参与一些非驾驶的车内活动(IVAs)。因此,人们可以利用旅行时间工作、放松、娱乐、交流,还可能进行其他活动。由于 FAV 提供了与火车和公共汽车等传统旅行方式不同的环境,人们在 FAV 旅行中进行车内活动的偏好已成为交通研究中的一个新问题。了解人们在 FAV 旅行中进行 IVA 的偏好将为未来的车辆内饰设计和交通政策制定提供重要信息。因此,本文介绍了一项研究的成果,该研究旨在加深我们对个人在乘坐汽车旅行时进行 IVA 的意向以及影响这些意向的内生和外生因素和变量的理解。我们设计了一个实验,并使用同步方程模型分析了响应数据,以研究乘坐固定翼飞机旅行时进行 IVA 的意向以及不同 IVA 之间可能存在的潜在相关性。结果显示,IVA 意愿和 IVA 之间的相关性存在明显的异质性。年轻人、高学历群体和在职者参与大多数 IVA 的意愿较高。此外,性别、家庭收入、晕车和拥有驾照也会影响人们的意愿。估计结果表明,进行 IVA 的意愿取决于旅行时间的长短。此外,个人游之间的潜在相关性也得到了证实。例如,有睡觉意向的受访者对吃喝和玩游戏感兴趣,但不倾向于用电脑工作。与此相反,打算在 FAV 旅行期间使用社交媒体的受访者在 FAV 旅行期间不太可能睡觉。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What to do with commuting time when driving autonomous vehicles? Results of a stated intention experiment

Rapid improvements in autonomous driving technology and the availability of autonomous vehicles (AVs) are expected to change people’s habitual travel patterns. Fully autonomous vehicles (FAVs) do not need to be maneuvered by their users, implying users are allowed to participate in a number of non-driving in-vehicle activities (IVAs) when their FAV is bringing them to their destination. People can therefore use their travel time for working, relaxation, entertainment, communication and possibly other activities. Since FAVs provide a different environment than traditional travel modes, such as trains and busses, people’s preferences for conducting IVAs in FAV travel has become an emerging issue in transportation research. Understanding people’s preferences for conducting IVAs during FAV travel will generate important information for future vehicle interior design and the development of transportation policies. Hence, this paper presents the outcomes of a research study that aims at increasing our understanding of the intentions of individuals to conduct IVAs when travelling by FAV’s and the endogenous and exogenous factors and variables influencing these intentions. We designed an experiment and analyzed the response data using simultaneous equation modeling to examine the intentions to conduct IVAs during FAV travel and potential correlations that may exist across IVAs. The results show significant heterogeneity in IVA intentions and correlations between IVAs. Youngsters, high-education-level groups, and employed show a higher intention to engage in most IVAs. In addition, gender, household income, motion sickness, and license ownership affect people’s intentions. The estimated results suggest that the intentions to conduct IVAs depend on trip length. Moreover, the potential correlation between IVAs is confirmed. For example, respondents who have intentions to conduct to sleep show interest in eating or drinking and play games, but are not inclined to work with a computer. In contrast, respondents who intend to use social media during FAV travel are less likely to sleep when travelling by FAV.

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来源期刊
CiteScore
13.20
自引率
7.80%
发文量
257
审稿时长
9.8 months
期刊介绍: Transportation Research: Part A contains papers of general interest in all passenger and freight transportation modes: policy analysis, formulation and evaluation; planning; interaction with the political, socioeconomic and physical environment; design, management and evaluation of transportation systems. Topics are approached from any discipline or perspective: economics, engineering, sociology, psychology, etc. Case studies, survey and expository papers are included, as are articles which contribute to unification of the field, or to an understanding of the comparative aspects of different systems. Papers which assess the scope for technological innovation within a social or political framework are also published. The journal is international, and places equal emphasis on the problems of industrialized and non-industrialized regions. Part A''s aims and scope are complementary to Transportation Research Part B: Methodological, Part C: Emerging Technologies and Part D: Transport and Environment. Part E: Logistics and Transportation Review. Part F: Traffic Psychology and Behaviour. The complete set forms the most cohesive and comprehensive reference of current research in transportation science.
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