RI: Dynamic Discrete Choice Networks -- An Artificial Intelligence Approach to Modeling Dynamic Travel Behavior

RI：动态离散选择网络——动态出行行为建模的人工智能方法

• 批准号: 0705898
• 负责人: Alan Borning
• 金额: $ 90万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-10-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0705898&HistoricalAwards=false
• 关键词: RI; Dynamic; Discrete; Choice; Networks

Project SummaryThe goals of the proposed research are twofold: first, to advance the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques; and second, to use these techniques in building better transportation models, which can then be used to help inform public deliberation regarding major infrastructure decisions. Problems of maintaining or replacing aging infrastructure, or adding new infrastructure to meet the needs of population growth and urban expansion of metropolitan areas, are becoming increasingly difficult to solve, in part because the cost is extremely large, and in part because the political discourse over alternative solutions is contentious and reflects divergent assumptions and values. Often, a major source of disagreement is cost; but another is rooted in differing assumptions about how people would adjust their travel in response to changed circumstances in both the short and long term, and how much congestion would result. Current transportation models used in operational analysis and planning are too behaviorally simple to be very useful in addressing these questions. Recent research advances have provided improvements in behavioral representation in these kinds of choice situations, but to date these nnovations are not integrated and are computationally not feasible for large-scale application. During the last decade, the artificial intelligence community has developed a set of techniques that enable fine-grained activity recognition from sensor data; among the most advanced and successful are approaches based on Dynamic Bayesian networks and statistical relational learning. The research team will build on this foundation, integrating these AI techniques with the Discrete Choice Models used in econometric approaches, to yield a new, hybrid reasoning system: Dynamic Discrete Choice Networks. This technique will be applied to the challenging domain of modeling dynamic travel choices of individuals, such as the number of trips, scheduled time of departure, destinations, modes, and routes and to predict how these choices change under dynamically updated travel conditions. Intellectual MeritThe merit of this proposal is grounded in the research challenges in the artificial intelligence and urban modeling areas. This project advances the state of the art in artificial intelligence and cognitive sciences by developing novel probabilistic reasoning techniques that are well suited for modeling the complex combinations of factors involved in human decision making in the commonsense domain of daily travel. By integrating this modeling power into probabilistic temporal models, Dynamic Discrete Choice Networks will provide an extremely general and flexible framework for learning and recognizing human activities from sensor data and for understanding how everyday human decision making adapts to a constantly changing environment.Broader ImpactsUrbanSim has the potential to significantly aid in public deliberation over major decisions regarding transportation replacement or expansion of transportation infrastructure, managing urban development, planning for response to mitigate the effects of events such as hurricane Katrina or a major earthquake, and other issues. UrbanSim is Open Source and freely available, and has already attracted considerable interest and use. Because of their improved ability to recognize and analyze human activities from raw sensor data, Dynamic Discrete Choice Networks will have applications to other significant domains as well, such as eldercare and long term health monitoring.

项目概述拟议研究的目标有两个：第一，通过开发新的概率推理技术来促进人工智能和认知科学的最新发展；第二，使用这些技术来建立更好的交通模型，然后使用这些模型来帮助公众就重大基础设施决策进行审议。维护或更换老化的基础设施，或增加新的基础设施以满足大都市地区人口增长和城市扩张的需求，这些问题正变得越来越难以解决，部分原因是成本极其高昂，部分原因是关于替代解决方案的政治讨论存在争议，反映了不同的假设和价值观。通常，分歧的一个主要来源是成本；但另一个分歧源于对人们将如何调整旅行以应对短期和长期情况变化的不同假设，以及会导致多大程度的拥堵。目前用于业务分析和规划的运输模型在行为上过于简单，不能很好地解决这些问题。最近的研究进展为这类选择情景下的行为表征提供了改进，但到目前为止，这些创新还没有整合在一起，在计算上不适用于大规模应用。在过去的十年里，人工智能社区开发了一套技术，能够从传感器数据中进行细粒度的活动识别；其中最先进和成功的方法是基于动态贝叶斯网络和统计关系学习的方法。研究团队将在此基础上，将这些人工智能技术与计量经济学方法中使用的离散选择模型相结合，产生一个新的混合推理系统：动态离散选择网络。这项技术将被应用于模拟个人动态出行选择的挑战性领域，例如出行次数、预定出发时间、目的地、方式和路线，并预测这些选择在动态更新的出行条件下如何变化。智力价值这一提议的优点在于人工智能和城市建模领域的研究挑战。该项目通过开发新的概率推理技术来推动人工智能和认知科学的发展，这些技术非常适合在日常旅行的常识领域对人类决策所涉及的因素的复杂组合进行建模。通过将这种建模能力集成到概率时间模型中，动态离散选择网络将提供一个极其通用和灵活的框架，用于从传感器数据学习和识别人类活动，并了解日常人类决策如何适应不断变化的环境。Broader ImpactsUrbanSim具有显著帮助公众审议有关交通基础设施的更换或扩展、管理城市发展、规划响应以减轻卡特里娜飓风或大地震等事件的影响等重大决策的潜力。UrbanSim是开源的，并且可以免费获得，并且已经吸引了相当大的兴趣和使用。由于其提高了从原始传感器数据中识别和分析人类活动的能力，动态离散选择网络还将应用于其他重要领域，如老年护理和长期健康监测。