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Personalized incentives for promoting sustainable travel behaviors
Transportation Research Part C: Emerging Technologies ( IF 7.6 ) Pub Date : 2019-06-21 , DOI: 10.1016/j.trc.2019.05.015
Xi Zhu , Feilong Wang , Cynthia Chen , Derek D. Reed

We develop a personalized system to modify individual travel behaviors by offering personalized incentives. Individual preferences are learned to provide personalized incentives so that the promoted alternative is likely accepted. Using knowledge from control theories and state estimation, we model travelers’ choice-making behaviors with the random utility theory and responses from the individuals are mined by a particle filter for learning individual preferences to promote sustainable behaviors. The discrete nature of travel behavior naturally leads to limited observability. We overcome this problem by designing a measurement function from which additional information can be solicited. Additionally, the inherent trade-offs between factors that affect travel choices result in an infinite set of solutions. We thus propose two solutions: (1) the divide and conquer strategy in which a multi-dimensional conditional probability function is proposed; and (2) use of domain knowledge to restrict that preference values fall in certain ranges and are consistent with certain distributions. The performance of preference learning with these two solutions applied is shown via simulation tests and an online experiment involving human participants. For departure time choices, we show an average acceptance ratio of 0.68 for all participants when being promoted with alternatives with personalized incentives. We also show that changes in individual departure time choices will lead to 48% reduction in total travel time on a simple transportation network.



中文翻译:

促进可持续旅行行为的个性化奖励

我们开发了个性化系统,通过提供个性化奖励措施来修改个人旅行行为。学会个人喜好来提供个性化的激励,以便提升的选择可能被接受。利用控制理论和状态估计中的知识,我们使用随机效用理论对旅行者的选择行为进行建模,并通过粒子过滤器挖掘个人的反应,以学习个人偏好以促进可持续行为。旅行行为的离散性质自然导致可观察性有限。我们通过设计一种测量功能来克服此问题,可以从中获取更多信息。此外,影响出行选择的因素之间的内在取舍导致了无数种解决方案。因此,我们提出了两种解决方案:(1)提出了多维条件概率函数的分治策略;(2)利用领域知识来限制偏好值在一定范围内并与某些分布相一致。通过模拟测试和涉及人类参与者的在线实验,展示了应用这两种解决方案的偏好学习的表现。对于出发时间的选择,当通过个性化激励的替代品进行推广时,所有参与者的平均接受率为0.68。我们还表明,单个出发时间选择的更改将使简单的运输网络上的总旅行时间减少48%。(2)利用领域知识来限制偏好值在一定范围内并与某些分布相一致。通过模拟测试和涉及人类参与者的在线实验,展示了应用这两种解决方案的偏好学习的表现。对于出发时间的选择,当通过个性化激励的替代品进行推广时,所有参与者的平均接受率为0.68。我们还表明,单个出发时间选择的更改将使简单的运输网络上的总旅行时间减少48%。(2)利用领域知识来限制偏好值在一定范围内并与某些分布相一致。通过模拟测试和涉及人类参与者的在线实验,展示了应用这两种解决方案的偏好学习的表现。对于出发时间的选择,当通过个性化激励的替代品进行推广时,所有参与者的平均接受率为0.68。我们还显示,单个出发时间选择的更改将使简单的运输网络上的总旅行时间减少48%。对于出发时间的选择,当通过个性化激励的替代品进行推广时,所有参与者的平均接受率为0.68。我们还表明,单个出发时间选择的更改将使简单的运输网络上的总旅行时间减少48%。对于出发时间的选择,当通过个性化激励的替代品进行推广时,所有参与者的平均接受率为0.68。我们还表明,单个出发时间选择的更改将使简单的运输网络上的总旅行时间减少48%。

更新日期:2020-02-21
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