  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.
