The trouble with 2nd order models or how to generate stop-and-go traffic in a 1st order model

TL;DR

This paper demonstrates that stop-and-go pedestrian traffic can be effectively modeled using a stochastic first order approach, avoiding the unrealistic behaviors inherent in classical second order models like the social-force model.

Contribution

The authors introduce a novel stochastic first order pedestrian model that reproduces stop-and-go behavior without the artifacts caused by inertia in second order models.

Findings

First order stochastic model reproduces stop-and-go traffic.

Second order models exhibit unrealistic backward motion and oscillations.

Correlated noise drives realistic pedestrian dynamics.

Abstract

Classical second order models of pedestrian dynamics, like the social-force model, suffer from various unrealistic behaviors in the dynamics, e.g. backward motion, oscillations and overlapping of pedestrians. These effects are not related to the discretization of the equations of motion, but intrinsic to the dynamics. They are the consequence of strong inertia effects that usually appear in second order models. We show that the experimentally observed stop-and-go behavior, which is an important test for any pedestrian model, can be reproduced with a stochastic first order model that does not suffer from the dynamical artefacts resulting from strong inertia. The model provides a new mechanism for stop-and-go behavior which is based on correlated noise.