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: 1231873
• 负责人: Paul Torrens
• 金额: $ 8.79万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-31 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1231873&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，群体行为在很大程度上被传统的学术研究所掩盖。 例如，在忙碌的街道上，让沿着成百上千的人进行现场实验，为了学术实验的目的而重现骚乱期间的暴民行为，或者期望以虚构的方式复制紧急情况下的生死行为，这些都是不切实际的。 建模和仿真在群体行为研究中扮演着关键的角色，作为探索那些无法通过其他手段进行研究的想法和假设的综合实验室。 群体动力学建模已经取得了重大进展，但挑战依然存在。 这个教师早期职业发展（CAREER）奖的目标是支持研究，教育和相关活动，将开发一个可重复使用的和行为建立的行人运动和人群行为的计算机模型在密集的城市环境。 研究者打算将这项工作作为一个试验台，用于实验那些超出学术研究范围的想法、假设和计划。 该研究将寻求通过以丰富的细节代表个人，人群和周围城市来推进人群建模的最新技术。 模型将建立与理论知情的算法，捕捉人类行为的复杂性。 该模型将被实现为一个完全沉浸式的三维环境，吸引公众和学生，它将直观地传达关于人类运动和人群行为的复杂想法。 将采用一个强大的校准和验证方案，以促进政策和计划的评估，并将模型映射到公共卫生，市中心复兴，公共安全，国防，大规模事件规划，逃生，疏散和紧急情况的真实场景。该项目将在方法和实质性兴趣领域的许多方面进行创新。 它将推动当前地理科学中空间建模的最新发展。 这项工作将拓宽人类运动计算建模的行为基础。 该项目将有助于发展动态地理信息科学。 这项工作还将产生一个新的验证方案，将基于时间地理的GIS分析与空间分析，景观指标和空间统计相结合。 实质上，该模型将被用来建立在传统上装备不良的调查和检查在微观尺度和大规模动态背景下的人文和城市地理学领域的理论。 此外，该模型将作为一个实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。