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离散选择模型研究进展

DOI: 10.18306/dlkxjz.2015.10.008, PP. 1275-1287

Keywords: 离散选择模型,精细化,适用性,新动向

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Abstract:

本文从离散选择模型(discretechoicemodel,DCM)体系的一般原理和应用价值出发,总结了各经典模型的基本理论和典型应用,并概括了近来年一些重要的研究新动向。多项Logit模型(multinomiallogitmodel,MNL)是离散选择模型体系的基础,具有简洁、可靠、易实现等优点,但也存在固有的理论缺陷,由此产生了对更加精细化模型的需求。替代的精细化模型中,嵌套Logit模型(nestedlogitmodel,NL)常用于处理备选项相关、“都不选”备选项、数据合并等问题,一般极值模型(generalizedextremevaluemodel,GEV)体系是其更一般的形式;混合Logit模型(mixedlogitmodel,MXL)可用于解决随机偏好问题和多种相关问题,包括备选项相关、面版数据相关、随机系数相关、数据合并等,与之类似的潜在类别模型也有着广泛应用;多项Probit模型(multinomialprobitmodel,MNP)具有极高的灵活性,但其复杂的模型设定与庞大的运算量大大制约了其应用范围。本文在研究新动向上介绍了4个重要的研究关注点由多种经典模型形式相结合而成的复杂模型;面向RP/SP数据、定序、排序、多选等不同数据类型的适宜模型;基于各种受限理性选择策略的更为真实的模型;以及考虑选择的时空背景的模型。

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