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.
This project has pursued a threefold mission. The first is in developing new computational technologies and platforms for modeling, in detail, the intricate and complex actions, interactions, and activities of individuals in crowds. The second task involved using those models to explore how individuals behave in crowds, and how their collective behavior gives rise to crowd phenomena. The third endeavor was to simulate the genesis and dynamics of significant crowd processes and phenomena. This has included modeling everyday trip-making and movement of pedestrians along streets, through cities, and among popultaions, and the rhythms and motifs of daya-to-day life that they produce. It has also allowed us to explore what-if questions for extraordinary and even crictical scenarios that individuals and crowds may find themselves exposed to, including crowd behavior, egress, and evacuation in emergency situations, as well as the genesis and dynamics of anti-social crowds. Over the course of the project, we have developed a series of models, many with graphically-immersive interfaces that allow users to walk among virtual crowds, and to setup their own cities and emergency scenarios to explore. These models are accessible to general audiences (with much of the advanced computing and algorithm resolution going on behind the scenes), with the result that we have been able to use the models as synthetic, virtual laboratories for exploring, collaborating, and playing with ideas in the classroom and conference room.
