When GeoAI Meets the Crowd

Estimating a moving crowd, such as the head count of a presidential inauguration or a football game, presents a practical and intellectual challenge that is often politically and emotionally charged. The objectives of this paper are to discuss the integration of artificial intelligence and agent-based model (ABM) to simulate and estimate a moving crowd and outline some key issues and research agenda. To simulate individual movements of a moving crowd, Genetic Algorithm (GA) can be employed to fine-tune agent parameters in wayfinding (e.g. direction, speed, etc.) through mutation, crossover, elitism and extinction. Besides individual-based wayfinding parameters, GA can also be employed to optimize population-wide model parameters as well, such as the maximum walking speed, maximum crowd capacity, early departure and late arrival rates. These individual and global model parameters present different bottom-up and top-down forces in shaping and precipitating diverse crowd behaviors and movements to match empirical pattern. Besides spatial optimization, convolutional NN can also be trained to derive snapshots of crowd count and crowd density from still-frame pictures and videos to better provide feedbacks to the fitness function of GA. However, more researches are needed to better understand and overcome various technical issues in crowd simulation, including but not limited to overtraining in optimization, feature extraction of objects moving in multi- and random directions, ontological separation of protesters from pedestrians and spectators, reconciliation of a single/multiple crowds over time and space.