PECASE: Taking Attitudes Seriously: A Multi-Contextual Approach to Behavioral-Modeling

PECASE：认真对待态度：行为建模的多情境方法

• 批准号: 0958110
• 负责人: Joan Walker
• 金额: $ 37.45万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0958110&HistoricalAwards=false
• 关键词: PECASE; Taking; Attitudes; Seriously; Multi

Behavioral models of transport and land use are used to inform planning and policy making and forecast future scenarios. Discrete choice models are a key component and explain the choice of interest (spatial location of people and firms, mode, activities, etc.) as a function of observable attributes of the alternatives such as time and cost and observable characteristics of the decision maker such as household size and income. Behavioral scientists have long emphasized the importance of attitudes as the "emotional core" driving many decision-making processes. Despite their importance, such behavioral constructs have not been well represented in the discrete choice paradigm. The objective of this research is to effectively integrate attitudinal constructs into discrete choice analysis by building on the findings of behavioral science and incorporating the methods of structural equation modeling and psychometric data. The model system uses multiple equations and maximum likelihood estimation to simultaneously estimate discrete choice and latent variable models. This framework provides several advantages over existing approaches. First, it can incorporate the latest developments in discrete choice analysis (for example, flexible error structures and complex choice sets) as well as linear and non-linear structural equation models. Second, it produces consistent and efficient estimators of the influences of attitudes on choice. Third, it provides a means of simultaneously inferring both a set of intrinsic attitudes for each individual and how these attitudes affect a number of interrelated decisions. This methodology has been employed in limited cases and will be further developed and extended in this research program. Extensions include (i) modeling the set of intrinsic attitudes of a person and their influence on a number of interrelated travel, activity, and land use decisions, and (ii) modeling the trending nature of these attitudes over time. The research goals are to prove the feasibility of the approach, demonstrate its added value, and make it accessible to researchers.The educational program uses the domain of attitudes and behavior as the focus for hands-on, group research activities aimed at teaching the scientific method and re-enforcing basic principles taught in class. Educational modules will be developed for graduate, undergraduate, and secondary school classes that focus on exploration of the students own behavior and that of their family and colleagues. The research and educational programs are integrated through the domain of attitude and behavior research and a common dataset, thereby allowing for interaction through presentations and mentoring across all levels of the research and education. The education program contributes to the development of students excited about scientific exploration and with skills in the scientific method, thereby increasing their probability of careers in science and engineering. The research program contributes to better understanding of the development of transportation infrastructure and urban planning, providing insight into issues of urban sprawl, traffic congestion, environmental quality, and emergency evacuation. The proposed methods promise enriched quantitative models of behavior, thereby improving forecasts and better informing planning, management, and policy making. Estimation software that makes use of this approach will be developed and made freely accessible, opening the door for researchers in a wide variety of fields to enrich behavioral models with attitudinal constructs.

交通和土地使用的行为模型被用来为规划和政策制定提供信息，并预测未来的情景。离散选择模型是一个关键组成部分，它解释了利益选择（人和企业的空间位置、模式、活动等）作为可观察的替代属性（如时间和成本）和决策者的可观察特征（如家庭规模和收入）的函数。行为科学家长期以来一直强调态度的重要性，认为态度是驱动许多决策过程的“情感核心”。尽管它们很重要，但这种行为结构在离散选择范式中并没有得到很好的体现。本研究的目的是在行为科学研究成果的基础上，结合结构方程模型和心理测量数据的方法，将态度构念有效地整合到离散选择分析中。模型系统采用多方程和最大似然估计来同时估计离散选择和潜在变量模型。与现有方法相比，该框架提供了几个优点。首先，它可以结合离散选择分析的最新发展（例如，灵活的误差结构和复杂的选择集）以及线性和非线性结构方程模型。其次，它产生一致和有效的估计态度对选择的影响。第三，它提供了一种方法，可以同时推断出每个人的一套内在态度，以及这些态度如何影响一些相互关联的决策。这种方法已经在有限的情况下使用，并将在本研究计划中进一步发展和扩展。扩展范围包括：(i)对一个人的一套内在态度及其对若干相互关联的旅行、活动和土地使用决策的影响进行建模，以及（ii）对这些态度随时间的趋势性质进行建模。研究目标是证明该方法的可行性，展示其附加价值，并使其可供研究人员使用。该教育项目将态度和行为领域作为实践和小组研究活动的重点，旨在教授科学方法并加强课堂上教授的基本原则。教育模块将为研究生、本科生和中学班级开发，重点是探索学生自己及其家人和同事的行为。研究和教育项目通过态度和行为研究领域和一个公共数据集整合在一起，从而允许在研究和教育的各个层面通过演示和指导进行互动。该教育计划有助于培养对科学探索感兴趣并具有科学方法技能的学生，从而增加他们从事科学和工程职业的可能性。该研究项目有助于更好地理解交通基础设施和城市规划的发展，为城市蔓延、交通拥堵、环境质量和紧急疏散等问题提供见解。所提出的方法有望丰富行为的定量模型，从而改进预测，更好地为规划、管理和政策制定提供信息。使用这种方法的评估软件将被开发出来并免费提供，为研究人员在各种各样的领域打开大门，用态度结构丰富行为模型。