SCC-Planning: Caution: Heavy Load Ahead

SCC-Planning：警告：前面有重载

• 批准号: 1737492
• 负责人: Kris Wernstedt
• 金额: $ 9.94万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737492&HistoricalAwards=false
• 关键词: SCC; Planning; Caution; Heavy; Load

Waze, Twitter, variable message signs, Google Transit, transportation agency live camera feeds, and scores of mobile apps and other sources provide high quality, dynamic information to travelers in cities around the country. This information, much of it crowdsourced, epitomizes the allure of big data and smart and connected communities. Readily available to almost any traveler with a smart phone, it holds the promise of enabling better decisions to reduce traffic congestion, improve road safety, facilitate transit use, guide infrastructure investments, and otherwise improve transport outcomes. However, two stumbling blocks cloud this vision. These relate to, first, the challenges digital overload and other human cognitive limitations impose on decision-making; and, second, the reluctance of many transportation agencies to rely heavily on crowdsourced data in their operational (e.g., traffic management) and longer-term (e.g., infrastructure planning) decisions. This planning project addresses these two challenges. More specifically, PIs are working with public transportation managers in two inner-ring, Washington, DC metro area counties with highly diverse national- and foreign-born populations to develop a longer-term research effort to understand: (1) personal and institutional factors that influence the generation and use of information from crowdsourcing apps and other digital technologies in transportation; (2) impacts of perceived information overload on drivers, public managers, and planners (consumers and producers of information); 3) effect of overload on transportation incidents and patterns, and 4) effects on transport system performance of different types, levels, and quality of crowd-sourced transport information. Ultimately, PIs seek to yield better transportation outcomes for travelers in our study area, and to provide transferable lessons for communities around the country. The work integrates across social psychology, public administration, decision sciences, transportation engineering, and computer sciences. Its focus on digital overload, co-production of information by individuals and institutions, and model-based informatics uniquely captures decision dynamics widely distributed across space and individual actors. As such, it seeks to advance understanding of smart transportation system performance by incorporating a neglected element of information use (overload) critical to traveler behavior. By expanding a model-based informatics perspective to consider overload and aggregating decision-making distributed across a large number of individuals, it augments social psychology efforts to capture the collective effects of individual overload and stress as well. It also fosters new synergies between public administration -- which rarely considers uncertainty and risk as central parts of a decision situation -- and the behavioral decision sciences, which rarely consider the public interest nature of managers' responsibilities and their collaborative decision environment.

Waze、Twitter、可变消息标志、Google Transit、交通机构实时摄像头馈送以及数十个移动的应用程序和其他来源为全国各地城市的旅行者提供高质量的动态信息。 这些信息，其中大部分是众包的，集中体现了大数据和智能互联社区的吸引力。 几乎所有拥有智能手机的旅行者都可以随时使用它，它有望实现更好的决策，以减少交通拥堵，改善道路安全，促进交通使用，指导基础设施投资，并以其他方式改善交通成果。 然而，有两个绊脚石阻碍了这一愿景。 这些问题涉及：第一，数字过载和其他人类认知限制对决策带来的挑战;第二，许多运输机构不愿意在其运营中严重依赖众包数据（例如，业务管理）和长期（例如，基础设施规划）决策。 本规划项目应对这两个挑战。 更具体地说，PI正在与两个内环，华盛顿，DC都市区县的公共交通管理人员合作，这些县具有高度多样化的国内和外国出生的人口，以开展长期研究工作，以了解：（1）影响众包应用程序和其他交通数字技术信息生成和使用的个人和机构因素;（2）感知信息超载对驾驶员、公共管理者和规划者（信息消费者和生产者）的影响;（3）超载对交通事故和模式的影响;（4）不同类型、水平和质量的众包交通信息对交通系统性能的影响。 最终，PI寻求为我们研究区域的旅行者提供更好的交通成果，并为全国各地的社区提供可转移的经验教训。这项工作整合了社会心理学，公共管理，决策科学，交通工程和计算机科学。 它专注于数字过载，个人和机构共同制作信息，以及基于模型的信息学，独特地捕捉了广泛分布在空间和个人行为者中的决策动态。 因此，它试图通过将一个被忽视的信息使用（超载）的关键旅客行为的元素，以提高对智能交通系统性能的理解。 通过扩展基于模型的信息学视角来考虑分布在大量个体中的过载和聚合决策，它增强了社会心理学的努力，以捕捉个体过载和压力的集体效应。 它还促进公共行政-很少将不确定性和风险视为决策情况的核心部分-与行为决策科学-很少考虑管理人员责任的公共利益性质及其协作决策环境-之间新的协同作用。