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原文传递 Application of a Rank Ordered Probit Modeling Approach to Understanding Level of Interest in Autonomous Vehicles.

题名：	Application of a Rank Ordered Probit Modeling Approach to Understanding Level of Interest in Autonomous Vehicles.
作者：	Nair, G. S.; Astroza, S.; Bhat, C. R.; Khoeini, S.; Pendyala, R. M.
关键词：	Travel demand modeling, Autonomous vehicle adoption and usage, Application, Education, Rank ordered probit model, Rank ordered data, Socio-economic concerns, Methodology, Employment
摘要：	Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.
报告类型：	科技报告