Ordinal measures are frequently encountered in travel behavior research. This paper presents a new method for combining them when a hierarchical structure of the data can be presumed. This method is applied to study the subjective assessment of the amount of travel by different transportation modes among a group of French clerical workers, along with the desire to increase or decrease the use of such modes. Some advantages of this approach over traditional data reduction technique such as factor analysis when applied to ordinal data are then illustrated. In this study, combining evidence from several variables sheds light on the observed moderately negative relationship between the personal assessment of the amount of travel and the desire to increase or decrease it, thus integrating previous partial (univariate) results. We find a latent demand for travel, thus contributing to clarify the behavioral mechanisms behind the induced traffic phenomenon. Categorizing the above relationship by transportation mode shows a desire for a less environmental-friendly mix of modes (i.e., a greater desire to use heavy motorized modes and a lower desire to use two-wheeled modes), whenever the respondents do not feel to travel extensively. This result, combined with previous theoretical investigations concerning the determinants of the desire to alter trips consumption levels, shows the importance of making people aware of how much they travel.

中文翻译：

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通过结合分层结构的有序变量研究与旅行相关的个人评估和愿望

在出行行为研究中经常会遇到序数度量。当可以假设数据的层次结构时，本文提出了一种将它们组合的新方法。该方法用于研究一组法国文职人员对不同交通方式出行量的主观评估，以及增加或减少使用此类方式的愿望。然后说明了这种方法在应用于有序数据时相对于传统数据简化技术（例如因子分析）的一些优点。在这项研究中，结合来自多个变量的证据揭示了个人对旅行量的评估与增加或减少旅行量的愿望之间观察到的适度负相关，从而整合了先前的部分（单变量）结果。我们发现了对旅行的潜在需求，从而有助于阐明诱导交通现象背后的行为机制。按交通方式对上述关系进行分类表明，每当受访者不想旅行时，他们都希望采用不那么环保的方式组合（即，更愿意使用重型机动模式，而不愿意使用两轮模式）广泛。这一结果与之前关于改变旅行消费水平的愿望决定因素的理论研究相结合，表明了让人们意识到他们旅行多少的重要性。使用重型机动模式的愿望更大，而使用两轮模式的愿望更低），只要受访者不觉得要长途旅行。这一结果与之前关于改变旅行消费水平的愿望决定因素的理论研究相结合，表明了让人们意识到他们旅行多少的重要性。使用重型机动模式的愿望更大，而使用两轮模式的愿望更低），只要受访者不觉得要长途旅行。这一结果与之前关于改变旅行消费水平的愿望决定因素的理论研究相结合，表明了让人们意识到他们旅行多少的重要性。