There has been an increasing effort to improve the behavioural realism of mathematical models of choice, resulting in efforts to move away from random utility maximisation (RUM) models. Some new insights have been generated with, for example, models based on random regret minimisation (RRM, μ-RRM). Notwithstanding work using for example Decision Field Theory (DFT), many of the alternatives to RUM tested on real-world data have however only looked at only modest departures from RUM, and differences in results have consequently been small. In the present study, we address this research gap again by investigating the applicability of models based on quantum theory. These models, which are substantially different from the state-of-the-art choice modelling techniques, emphasise the importance of contextual effects, state dependence, interferences and the impact of choice or question order. As a result, quantum probability models have had some success in better explaining several phenomena in cognitive psychology. In this paper, we consider how best to operationalise quantum probability into a choice model. Additionally, we test the quantum model frameworks on a best/worst route choice dataset and demonstrate that they find useful transformations to capture differences between the attributes important in a most favoured alternative compared to that of the least favoured alternative. Similar transformations can also be used to efficiently capture contextual effects in a dataset where the order of the attributes and alternatives are manipulated. Overall, it appears that models incorporating quantum concepts hold significant promise in improving the state-of-the-art travel choice modelling paradigm through their adaptability and efficient modelling of contextual changes.

为了改善所选择的数学模型的行为现实性，已经进行了越来越多的努力，从而导致人们努力摆脱随机效用最大化（RUM）模型。例如，基于随机后悔最小化的模型（RRM，μ-RRM）。尽管使用了例如决策场理论（DFT）进行了工作，但是在实际数据上测试的RUM的许多替代方案仅着眼于与RUM的适度偏离，因此结果差异很小。在本研究中，我们通过研究基于量子理论的模型的适用性，再次解决了这一研究空白。这些模型与最新的选择建模技术有很大不同，它们强调了上下文效果，状态依赖，干扰以及选择或问题顺序的影响的重要性。结果，量子概率模型已经成功地更好地解释了认知心理学中的几种现象。在本文中，我们考虑如何最好地将量子概率运用于选择模型。另外，我们在最佳/最差路线选择数据集上测试了量子模型框架，并证明了它们发现了有用的变换，可以捕获最受宠的替代品与最不受欢迎的替代品之间重要的属性之间的差异。类似的转换也可以用于有效地捕获数据集中的上下文效果，在该数据集中，属性和替代项的顺序得到了控制。总体而言，似乎具有量子概念的模型通过其适应性和对上下文变化的高效建模，有望在改善最新的出行选择建模范式方面发挥巨大作用。