Crowd flow forecasting via agent-based simulations with sequential latent parameter estimation from aggregate observation.

ABSTRACT: Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management; however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd de

SUBMITTER: Makinoshima F 

PROVIDER: S-EPMC9249888 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature