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首页> 外文会议>Annual meeting of the transportation research board;Transportation Research Board >Constructing an Optimal Orthogonal Choice Design with Alternative-specific Attributes for Stated Choice Experiments
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Constructing an Optimal Orthogonal Choice Design with Alternative-specific Attributes for Stated Choice Experiments

机译：构造具有替代特定属性的最优正交选择设计用于状态选择实验

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摘要

The stated choice (SC) experiment is generally regarded as an effective way to obtain data for discrete choice analysis. The SC experimental design method, which determines the rule to allocate different levels for each attribute in choice situations, will have a great impact on parameter estimation. The optimal orthogonal choice (OOC) design is one of the most efficient SC designs, by which more reliable parameter estimates can be achieved with an equal or lower sample size. However, OOC design can only be applied to utility models with generic attributes; using it to assign alternative-specific attribute levels is not fully discussed in literature. This paper provides a method to extend the use of OOC design to alternative-specific attributes. Column vectors for alternative-specific attributes are introduced and the value of each vector is forced to be orthogonal with other generic attributes in a same alternative. In this way, orthogonality of OOC design is kept within individual alternative but not necessarily across alternatives. The proposed method was compared with traditional orthogonal design and D-efficient design (another state-of-the-art efficient design method). Three experiments using field data on mode choice were conducted. The result shows that both proposed method and D-efficient design have a higher efficiency than the orthogonal design. In addition, under the complex experiment setting environment in real world, the proposed method outperforms D-efficient design in the sense that almost the same efficiency can be obtained while avoiding multiple iterations for optimal solution.
机译：陈述选择（SC）实验通常被视为获取数据的有效方法 离散选择分析。 SC实验设计方法，它确定要分配的规则 选择情况下每个属性的级别不同，将对参数产生很大影响 估计。最佳正交选择（OOC）设计是最有效的SC设计之一， 相等或较小的样本量可以实现更可靠的参数估计。 但是，OOC设计只能应用于具有通用属性的实用新型。用它来分配 文献中没有充分讨论替代特定的属性级别。本文提供了一种方法 将OOC设计的使用范围扩展到特定于替代的属性。的列向量 引入了特定于替代的属性，并且每个向量的值都被强制为正交 与其他通用属性在相同的替代方案中。这样，可以保持OOC设计的正交性 在单个替代方案内，但不一定跨替代方案提议的方法是 与传统的正交设计和D高效设计相比（另一种最新的高效 设计方法）。使用现场数据进行模式选择进行了三个实验。结果 表明所提出的方法和D效率设计均具有较高的效率。 正交设计。此外，在现实世界中复杂的实验设置环境下， 在几乎可以达到相同效率的意义上，建议的方法优于D效率设计 避免多次迭代以获得最佳解决方案。

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