CAREER: Exploring the Dynamics of Individual Pedestrian and Crowd Behavior in Dense Urban Settings: A Computational Approach

职业：探索密集城市环境中个体行人和人群行为的动态：一种计算方法

• 批准号: 0643322
• 负责人: Paul Torrens
• 金额: $ 33.31万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0643322&HistoricalAwards=false
• 关键词: CAREER; Exploring; Dynamics; Individual; Pedestrian

Crowds are vital to the lifeblood of cities. Crowd behavior has largely been veiled from traditional academic inquiry, however. For example, it is impractical to establish live experiments with hundreds or thousands of people along busy streetscapes, to reproduce mob behavior during riots for the purposes of academic experimentation, or to expect to replicate the life-and-death behavior under emergency situations in a fabricated fashion. Modeling and simulation occupy a pivotal role in the research of crowd behavior as synthetic laboratories for exploring ideas and hypotheses that are simply not amenable to investigation by other means. Major advances have been made in modeling crowd dynamics, but challenges remain. The goal of this Faculty Early-Career Development (CAREER) award is to support research, education, and related activities that will develop a reusable and behaviorally founded computer model of pedestrian movement and crowd behavior amid dense urban environments. The investigator intends for this work to serve as a test-bed for experimentation with ideas, hypotheses, and plans that would otherwise lie beyond the reach of academic inquiry. The research will seek to advance the state-of-the-art in crowd modeling by representing individuals, crowds, and the ambient city with rich detail. Models will be built with theory-informed algorithms that capture the intricacies of human behavior. The model will be realized as a fully immersive three-dimensional environment that engages both the public and students, and it will convey intuitively complicated ideas about human movement and crowd behavior. A robust calibration and validation scheme will be employed to facilitate evaluation of policies and plans in simulation and mapping of models to real-world scenarios in public health, downtown revitalization, public safety, defense, large-scale event-planning, escape, evacuation, and emergencies.The project will be innovative in areas of methodological and substantive interest in many ways. It will push the current state-of-the-art in spatial modeling in the geographical sciences. The work will broaden the behavioral base for computational modeling of human movement. The project will contribute to the development of dynamic geographic information science. The work also will produce a novel validation scheme that combines GIS analytics based on time geography with spatial analysis, landscape metrics, and spatial statistics. Substantively, the model will be used to build theory in areas of human and urban geography that are traditionally ill-equipped for investigation and examination at the micro-scale and in massively dynamic contexts. Moreover, the model will serve as an experimental but wholly realistic environment for exploring "what-if" and unforeseen scenarios of relevance to cities and their citizens.

人群对城市的命脉至关重要。然而，传统的学术调查在很大程度上掩盖了人群行为。例如，为了学术实验的目的，在繁忙的街道上建立成百上千人的现场实验，复制暴乱期间的暴徒行为，或者期望以捏造的方式复制紧急情况下的生死行为，是不切实际的。建模和仿真在人群行为研究中扮演着关键角色，因为它们是探索想法和假设的综合实验室，而这些想法和假设根本不适合用其他方法进行研究。在人群动力学建模方面取得了重大进展，但挑战依然存在。这一学院早期职业发展(职业)奖的目标是支持研究、教育和相关活动，这些活动将开发一个可重复使用的、基于行为的计算机模型，用于在密集的城市环境中研究行人的运动和人群行为。研究人员打算将这项工作作为一个试验台，用其他方式超出学术调查范围的想法、假设和计划进行实验。这项研究将寻求通过丰富的细节来表现个人、人群和周围城市，从而推动人群建模的最先进水平。模型将用捕捉人类行为错综复杂的理论知识的算法建立。该模型将被实现为一个完全身临其境的三维环境，公众和学生都参与其中，它将传达关于人类运动和人群行为的直观复杂的想法。在公共卫生、市中心振兴、公共安全、国防、大型活动规划、逃生、疏散和紧急情况的模拟和模型映射中，将采用稳健的校准和验证方案来促进政策和计划的评估。该项目将在许多方面具有方法学和实质性的创新意义。它将推动地理科学中目前最先进的空间建模。这项工作将为人体运动的计算建模拓宽行为学基础。该项目将为动态地理信息科学的发展做出贡献。这项工作还将产生一种新的验证方案，将基于时间地理的地理信息系统分析与空间分析、景观度量和空间统计相结合。实质上，该模型将用于在人文和城市地理领域建立理论，这些领域传统上不适合在微观和大规模动态背景下进行调查和审查。此外，该模型将作为一个试验性但完全现实的环境，用于探索与城市及其公民相关的“假设”和不可预见的情景。