Simulation of High Density Pedestrian Flow: Microscopic Model

TL;DR

This paper develops a microscopic pedestrian simulation model based on empirical image data to better understand and control high-density crowd evacuation scenarios, emphasizing individual interactions and data-driven approaches.

Contribution

It introduces a new microscopic pedestrian model derived from empirical video data, enhancing crowd simulation accuracy for evacuation planning.

Findings

Model effectively simulates high-density pedestrian flows.

Uses empirical data from videos for realistic behavior modeling.

Supports development of evacuation control strategies.

Abstract

In recent years modelling crowd and evacuation dynamics has become very important, with increasing huge numbers of people gathering around the world for many reasons and events. The fact that our global population grows dramatically every year and the current public transport systems are able to transport large amounts of people, heightens the risk of crowd panic or crush. Pedestrian models are based on macroscopic or microscopic behaviour. In this paper, we are interested in developing models that can be used for evacuation control strategies. This model will be based on microscopic pedestrian simulation models, and its evolution and design requires a lot of information and data. The people stream will be simulated, based on mathematical models derived from empirical data about pedestrian flows. This model is developed from image data bases, so called empirical data, taken from a video camera or data obtained using human detectors. We consider the individuals as autonomous particles interacting through social and physical forces, which is one approach that has been used to simulate crowd behaviour.