Predicting Disrupted Network Behavior

预测中断的网络行为

• 批准号: 0927315
• 负责人: S. Travis Waller
• 金额: $ 32万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2012-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0927315&HistoricalAwards=false
• 关键词: Predicting; Disrupted; Network; Behavior

As people interact at a large-scale within infrastructure systems (such as roadway systems), the area of network modeling is often employed to characterize the impact of the resulting behavior. Traditionally, the concept of network equilibrium has been employed which models the long-term steady state behavior of many individuals each acting in their own self interest. While equilibrium has been critical for infrastructure planning and management, it requires several key assumptions such as familiarity and rationality which may not hold true in high stress disruptive situations. This research project addresses new models for network behavior when significant disruptions occur which upset the expected network state. The primary hypotheses of this research is that individuals can transform and adapt previous expectations based on their perception of the disruption as well as information learned en-route and that in unfamiliar cases network users place greater weight on system and context-specific characteristics such as route and road geometry, risk preference, and travel constraints (e.g., when unfamiliar with the true expected cost, users may select a longer path simply because it moves them closer to the destination initially). This research will discern these new individual behaviors through psychological experiments and then develop novel mathematical formulations for the resulting network impacts.By adopting the new problem characteristics noted in the previous paragraph, fundamentally new mathematical system descriptions and predictions can be developed for large-scale networks subjected to disruptions. By achieving superior prediction capabilities, substantial societal improvements are achievable by being able to better prepare for disaster and evacuation possibilities. Furthermore, by better understanding non-equilibrium behavior even substantial near-daily non-extreme improvements are achievable such as mitigating the impact of non-recurrent congestion and traffic incidents (both areas which have long complicated transportation planning). Numerous broader benefits will also be seen beyond transportation systems. As this research addresses the fundamental problem of network behavior, numerous fields which employ network models can adopt aspects of the new behavioral models. Educationally, substantial benefits will result from the closer consideration of network modeling with psychological behavior. Further, outreach efforts will be conducted by both Co-PIs and in conjunction with programs sponsored by UT Austin to introduce students to research and practice, with an emphasis on recruiting a diverse mix of undergraduate and graduate students. Through such programs (including the Advanced Institute and US Intern program at UT-Austin combined with the NSF REU program) the PIs have been repeatedly successful in the past in recruiting such a diverse mix of students and are committed to forming a closely cooperative interdisciplinary research effort.

当人们在基础设施系统（例如道路系统）内进行大规模交互时，网络建模领域通常用于描述由此产生的行为的影响。传统上，网络均衡的概念被用来模拟许多个人的长期稳态行为，每个人都按照自己的利益行事。 虽然平衡对于基础设施规划和管理至关重要，但它需要几个关键假设，例如熟悉性和合理性，而这些假设在高压力破坏性情况下可能不成立。 该研究项目解决了当发生重大中断而扰乱预期网络状态时网络行为的新模型。这项研究的主要假设是，个人可以根据他们对中断的感知以及途中学到的信息来改变和调整以前的期望，并且在不熟悉的情况下，网络用户更重视系统和特定环境的特征，例如路线和道路几何形状、风险偏好和旅行限制（例如，当不熟悉真正的预期成本时，用户可能会选择更长的路径，因为它最初使他们更接近目的地）。 这项研究将通过心理实验来辨别这些新的个体行为，然后针对由此产生的网络影响开发新的数学公式。通过采用上一段中提到的新问题特征，可以为遭受破坏的大规模网络开发全新的数学系统描述和预测。 通过实现卓越的预测能力，可以更好地为灾难和疏散做好准备，从而实现社会的重大进步。 此外，通过更好地理解非平衡行为，甚至可以实现近乎日常的非极端改进，例如减轻非经常性拥堵和交通事故的影响（这两个领域长期以来交通规划都很复杂）。 除了交通系统之外，还将看到许多更广泛的好处。 由于这项研究解决了网络行为的基本问题，因此许多采用网络模型的领域都可以采用新行为模型的各个方面。 从教育角度来说，更仔细地考虑网络建模与心理行为将会带来巨大的好处。 此外，两位联合PI将与德克萨斯大学奥斯汀分校赞助的项目一起开展外展工作，向学生介绍研究和实践，重点是招收多样化的本科生和研究生。通过此类项目（包括 UT-Austin 的高级学院和美国实习生项目以及 NSF REU 项目），PI 过去多次成功地招募了如此多样化的学生，并致力于形成紧密合作的跨学科研究工作。