CS Events

Qualifying Exam

Predicting Long-Term Crowd Flow in Built Environments

 

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Thursday, December 16, 2021, 09:00am

 

Speaker: Samuel S. Sohn

Location : Via Zoom

Committee

Prof. Mubbasir Kapadia (advisor)

Prof. Vladimir Pavlovic

Prof. Dimitris Metaxas

Prof. David Pennock

Event Type: Qualifying Exam

Abstract: Predicting the concerted movements of crowds in built environments, whether at the scale of a house or a campus with several buildings, is a key requirement for crowd and disaster management, architectural design, and urban planning. It is particularly important to study this movement with large crowds and environments over long time frames to understand the influence of the environment on the crowd, e.g. in terms of congestion. However, this comes at a prohibitive logistical cost when studying real humans and a prohibitive computational cost when studying simulations, neither of which meet the demands of practitioners. We therefore propose the first framework for instantaneously forecasting the full movement of a human/agent crowd using solely the initial state of the crowd and environment. Since the spatial dimensions of environments can vary widely, we have developed fixed-size crowd scenario representations to effectively direct initial state information into convolutional neural network architectures. Experimental results indicate that after training models exclusively on synthetic data, the models generalize to never-before-seen real environments and crowds.

Organization

Rutgers University School of Arts and Sciences

Contact  Prof. Mubbasir Kapadia

Zoom Link: https://rutgers.zoom.us/j/93638756660?pwd=d3d4SmFKTnFJTWxxSk9MT0RPMFp0UT09