  In human crowds as well as in many animal societies, local interactions among
individuals often give rise to self-organized collective organizations that
offer functional benefits to the group. For instance, flows of pedestrians
moving in opposite directions spontaneously segregate into lanes of uniform
walking directions. This phenomenon is often referred to as a smart collective
pattern, as it increases the traffic efficiency with no need of external
control. However, the functional benefits of this emergent organization have
never been experimentally measured, and the underlying behavioral mechanisms
are poorly understood. In this work, we have studied this phenomenon under
controlled laboratory conditions. We found that the traffic segregation
exhibits structural instabilities characterized by the alternation of organized
and disorganized states, where the lifetime of well-organized clusters of
pedestrians follow a stretched exponential relaxation process. Further analysis
show that the inter-pedestrian variability of comfortable walking speeds is a
key variable at the origin of the observed traffic perturbations. We show that
the collective benefit of the emerging pattern is maximized when all
pedestrians walk at the average speed of the group. In practice, however, local
interactions between slow- and fast-walking pedestrians trigger global
breakdowns of organization, which reduce the collective and the individual
payoff provided by the traffic segregation. This work is a step ahead toward
the understanding of traffic self-organization in crowds, which turns out to be
modulated by complex behavioral mechanisms that do not always maximize the
group's benefits. The quantitative understanding of crowd behaviors opens the
way for designing bottom-up management strategies bound to promote the
emergence of efficient collective behaviors in crowds.
