NeTS: Small: Investigation of Human Mobility: Measurement, Modeling, Analysis, Application and Protocols

NeTS：小型：人类流动性调查：测量、建模、分析、应用和协议

• 批准号: 1016216
• 负责人: Mladen Vouk
• 金额: $ 29.84万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016216&HistoricalAwards=false
• 关键词: NeTS; Small; Investigation; Human; Mobility

This project studies the fundamental statistical laws governing the mobility patterns of humans. The prior NSF-funded studies conducted by the PI, which are based on GPS traces of 100 people in five different settings including university campuses, New York City, Disney World, and State Fair, revealed that many important fundamental statistical properties of human mobility, namely heavy-tail flight distributions, self-similar dispersion of visit points, and least-action principle for trip planning. Most of all, they find that peo-ple tend to optimize their trips in a way to minimize their discomfort or cost of trips (e.g., distance). The current project is extending this work by considering the effect of temporal constraints. When two persons meet, they have to be at the same location, and also at the same time. A realistic human mobility model must capture these spatial and temporal constraints and dependencies. The major goal of the project is to statistically capture the fundamental laws of these properties which are invariant of specifics in mobility scenarios, and represent them realistically in diverse mobility scenarios including user-created virtual en-vironments. The results from this project include (1) understandings of the fundamental human mobility characteristics and (2) statistical representation of them in synthetic mobility traces. Realistic human mobility models can improve our current practice of evaluating the performance of mobile networks. The models can also be used for other disciplines such as civil engineering for city planning and escape planning, critical disease control for studying the patterns of virus spread, and biology and sociology.

该项目研究支配人类流动模式的基本统计规律。PI之前进行的NSF资助的研究基于在五个不同环境中的100人的GPS跟踪，包括大学校园，纽约市，迪士尼世界和州博览会，揭示了人类流动性的许多重要的基本统计特性，即重尾飞行分布，访问点的自相似分散和旅行规划的最小行动原则。 最重要的是，他们发现人们倾向于以一种最小化他们的不适或旅行成本的方式来优化他们的旅行（例如，距离）。目前的项目正在通过考虑时间限制的影响来扩展这项工作。当两个人相遇时，他们必须在同一地点，也必须在同一时间。一个现实的人类移动模型必须捕捉这些空间和时间的约束和依赖关系。该项目的主要目标是统计捕获这些属性的基本规律，这些属性在移动场景中是不变的，并在不同的移动场景中真实地表示它们，包括用户创建的虚拟环境。该项目的结果包括（1）对人类基本流动特征的理解和（2）在合成流动轨迹中对它们的统计表示。现实的人类移动模型可以改善我们目前的做法，评估移动的网络的性能。这些模型也可用于其他学科，如用于城市规划和逃生规划的土木工程，用于研究病毒传播模式的重大疾病控制，以及生物学和社会学。