Predictive Analysis for Crowd Flow

Crowd flow prediction has become a critical area of research, especially for event managers who need to ety, smooth mobility, and effective planning in large gatherings. Unlike traditional crowd monitoring, flow prediction focuses on understanding how groups of people move collectively, which can help prevent congestion and support realtime decision-making. In this review, I examine recent advances in deep learning–based techniques for crowd flow prediction, highlighting their ability to catch complex spatial and temporal patterns in crowded environments. I discuss key approaches such as density forecasting, flow estimation, and trajectory prediction, while also comparing widely used datasets and evaluation practices. Beyond summarizing existing methods, this review provides understanding into the highlights and challenges of current models, identifies gaps in the literature, also suggests possible ideas for future studies. The goal is to offer a comprehensive yet practical perspective on how crowd flow prediction can better support event management and public safety.