Time-continuous microscopic pedestrian models: an overview

TL;DR

This paper reviews the evolution of time-continuous microscopic pedestrian models, emphasizing data-driven approaches, calibration methods, and future development prospects in the field.

Contribution

It provides a comprehensive overview of mathematical models, calibration techniques, hybrid neural network approaches, and data-based models in pedestrian dynamics.

Findings

Modern models incorporate sophisticated collision-avoidance and anticipation.

Data-driven calibration and deep learning enhance model accuracy.

The field is expected to grow with new development perspectives.

Abstract

We give an overview of time-continuous pedestrian models with a focus on data-driven modelling. Starting from pioneer, reactive force-based models we move forward to modern, active pedestrian models with sophisticated collision-avoidance and anticipation techniques through optimisation problems. The overview focuses on the mathematical aspects of the models and their different components. We include methods used for data-based calibration of model parameters, hybrid approaches incorporating neural networks, and purely data-based models fitted by deep learning. The conclusion outlines some development perspectives that we expect to grow in the coming years.