Autonomous driving is expected to strongly influence the value of travel time savings (VTTS) which is crucial for the assessment of the impact of automated vehicles (AVs). However, no consensus has been reached yet about the size and direction of the effect of AVs on VTTS. This high uncertainty around the VTTS is most likely due to high heterogeneity in preferences for travel time. Our hypothesis is that a key role in the heterogeneity of the VTTS for AVs is played by psychological factors. We focus in particular on Travel Experiences (besides individual preference for activities conducted during travelling) and Trust in travel time preferences for AVs. For this purpose, an online survey with a stated choice experiment and psychometric scales was conducted. Besides currently available transport modes, also a privately-owned AV (PAV) and a shared AV (SAV) were included in the choice sets. The data was analysed using a hybrid choice model. T-test and confidence intervals of the estimated VTTS are computed to assess if the VTTS are statistically significant and statistically different among user groups. Results confirm that both psychological factors have significant positive effect on VTTS for AVs. Gender, age, level of education and experience with similar systems were found to affect the VTTS directly and indirectly through their impact on the individual attitudes. Significant differences are found among some potential user groups, in particular in terms of trust to technology and anticipated travel experiences. For example, men are found to trust the technology more than women and also to have potentially higher technology affinity. However, our results show that they also perceive higher marginal disutility for travel time in both PAV and SAV. A comparison between a mixed logit model and the hybrid choice model reveals that capturing the indirect effect of the socio-economic characteristics of individuals through the effect of these factors on attitudes allows differentiating between the VTTS for different user groups. Lastly, implications for policy and technology deployment strategies are discussed based on the findings.

预计自动驾驶将极大地影响旅行时间节省 (VTTS) 的价值，这对于评估自动驾驶汽车 (AV) 的影响至关重要。然而，关于 AV 对 VTTS 影响的大小和方向尚未达成共识。VTTS 的这种高度不确定性很可能是由于旅行时间偏好的高度异质性。我们的假设是，心理因素在 AV 的 VTTS 异质性中起关键作用。我们特别关注旅行体验（除了个人对旅行期间进行的活动的偏好）和信任在 AV 的旅行时间偏好方面。为此，我们进行了一项带有明确选择实验和心理测量量表的在线调查。除了当前可用的运输方式外，选择集中还包括私有 AV (PAV) 和共享 AV (SAV)。使用混合选择模型分析数据。计算估计 VTTS 的 T 检验和置信区间，以评估 VTTS 在用户组之间是否具有统计显着性和统计差异。结果证实，这两种心理因素对 AV 的 VTTS 都有显着的积极影响。发现性别、年龄、教育水平和类似系统的经验通过对个人态度的影响直接或间接影响 VTTS。在一些潜在的用户群体中发现了显着的差异，特别是在对技术和预期旅行体验的信任方面。例如，发现男性比女性更信任技术，并且可能具有更高的技术亲和力。然而，我们的结果表明，他们也认为 PAV 和 SAV 的旅行时间具有更高的边际负效用。混合 logit 模型和混合选择模型之间的比较表明，通过这些因素对态度的影响来捕捉个人社会经济特征的间接影响，可以区分不同用户群体的 VTTS。最后，根据研究结果讨论了对政策和技术部署战略的影响。发现男性比女性更信任技术，并且可能具有更高的技术亲和力。然而，我们的结果表明，他们也认为 PAV 和 SAV 的旅行时间具有更高的边际负效用。混合 logit 模型和混合选择模型之间的比较表明，通过这些因素对态度的影响来捕捉个人社会经济特征的间接影响，可以区分不同用户群体的 VTTS。最后，根据研究结果讨论了对政策和技术部署战略的影响。发现男性比女性更信任技术，并且可能具有更高的技术亲和力。然而，我们的结果表明，他们也认为 PAV 和 SAV 的旅行时间具有更高的边际负效用。混合 logit 模型和混合选择模型之间的比较表明，通过这些因素对态度的影响来捕捉个人社会经济特征的间接影响，可以区分不同用户群体的 VTTS。最后，根据研究结果讨论了对政策和技术部署战略的影响。混合 logit 模型和混合选择模型之间的比较表明，通过这些因素对态度的影响来捕捉个人社会经济特征的间接影响，可以区分不同用户群体的 VTTS。最后，根据研究结果讨论了对政策和技术部署战略的影响。混合 logit 模型和混合选择模型之间的比较表明，通过这些因素对态度的影响来捕捉个人社会经济特征的间接影响，可以区分不同用户群体的 VTTS。最后，根据研究结果讨论了对政策和技术部署战略的影响。