Pedestrian flow in two dimensions: Optimal psychological stress leads to less evacuation time and decongestion.

Collective motion is an innate ability of all living systems, which depends on physiological and psychosocial factors in the case of humans. Such a collective organization is becoming of great interest in collective motion in human crowds. Using a cellular automaton (CA) simulation model, we demonstrate that emergency egress from a two-dimensional corridor with optimal stress leads to less evacuation time and efficient mass evacuations. We study how three types of stress (i.e., mild stress, optimal stress, and anxiety) described in the literature have a significant impact on the collective dynamics. We found that low-stress levels could decrease the evacuation time in an entire occupied room since agents choose alternative routes rather than the shortest path to the exit and display cooperative behavior. Therefore, the combination of mild and optimal stress can lead to efficient evacuations. Also CA simulations may be used to find safer and more efficient ways to conduct mass evacuation procedures.

Wavelet-based multifractal analysis of nonlinear time series: the earthquake-driven tsunami of 27 February 2010 in Chile.

We study general multifractal properties of tidal gauge and long-wave time series which show a well defined transition between two states, as is the case of sea level when a tsunami arrives. We adopt a method based on discrete wavelets, called wavelet leaders, which has been successfully used in a wide range of applications from image analysis to biomedical signals. First, we analyze an empirical time series of tidal gauge from the tsunami event of 27 February 2010 in Chile. Then, we study a numerical solution of the driven-damped regularized long-wave equation (RLWE) which displays on-off intermittency. Both time series are characterized by a sudden change between two sharply distinct dynamical states. Our analysis suggests a correspondence between the pre- and post-tsunami states (ocean background) and the on state in the RLWE, and also between the tsunami state (disturbed ocean) and the off state in the RLWE. A qualitative similarity in their singularity spectra is observed, and since the RLWE is used to model shallow water dynamics, this result could imply an underlying dynamical similarity.

Characterization of the nontrivial and chaotic behavior that occurs in a simple city traffic model.

We explore in detail the nontrivial and chaotic behavior of the traffic model proposed by Toledo et al. [Phys. Rev. E 70, 016107 (2004)] due to the richness of behavior present in the model, in spite of the fact that it is a minimalistic representation of basic city traffic dynamics. The chaotic behavior, previously shown for a given lower bound in acceleration/brake ratio, is examined more carefully and the region in parameter space for which we observe this nontrivial behavior is found. This parameter region may be related to the high sensitivity of traffic flow that eventually leads to traffic jams. Approximate scaling laws are proposed.

Resonance, criticality, and emergence in city traffic investigated in cellular automaton models.

The complex behavior that occurs when traffic lights are synchronized is studied for a row of interacting cars. The system is modeled through a cellular automaton. Two strategies are considered: all lights in phase and a "green wave" with a propagating green signal. It is found that the mean velocity near the resonant condition follows a critical scaling law. For the green wave, it is shown that the mean velocity scaling law holds even for random separation between traffic lights and is not dependent on the density. This independence on car density is broken when random perturbations are considered in the car velocity. Random velocity perturbations also have the effect of leading the system to an emergent state, where cars move in clusters, but with an average velocity which is independent of traffic light switching for large injection rates.

Optimal control in a noisy system.

We describe a simple method to control a known unstable periodic orbit (UPO) in the presence of noise. The strategy is based on regarding the control method as an optimization problem, which allows us to calculate a control matrix A. We illustrate the idea with the Rossler system, the Lorenz system, and a hyperchaotic system that has two exponents with positive real parts. Initially, a UPO and the corresponding control matrix are found in the absence of noise in these systems. It is shown that the strategy is useful even if noise is added as control is applied. For low noise, it is enough to find a control matrix such that the maximum Lyapunov exponent lambda(max)<0, and with a single non-null entry. If noise is increased, however, this is not the case, and the full control matrix A may be required to keep the UPO under control. Besides the Lyapunov spectrum, a characterization of the control strategies is given in terms of the average distance to the UPO and the control effort required to keep the orbit under control. Finally, particular attention is given to the problem of handling noise, which can affect considerably the estimation of the UPO itself and its exponents, and a cleaning strategy based on singular value decomposition was developed. This strategy gives a consistent manner to approach noisy systems, and may be easily adapted as a parametric control strategy, and to experimental situations, where noise is unavoidable.

Universal and nonuniversal features in a model of city traffic.

The complex behavior that occurs when traffic lights are synchronized is studied. Two strategies are considered: all lights in phase, and a "green wave" with a propagating green signal. It is found that traffic variables such as traveling time, velocity, and fuel consumption, near resonance, follow critical scaling laws. For the green wave, it is shown that time and velocity scaling laws hold even for random separation between traffic lights. These results suggest the concept of transient resonances, which can be induced by adaptively changing the phase of traffic lights. This may be important to consider when designing strategies for traffic control in cities, where short trajectories, and thus transient solutions, are likely to be relevant.

Modeling traffic through a sequence of traffic lights.

We introduce a microscopic traffic model, based on kinematic behavior, which consists of a single vehicle traveling through a sequence of traffic lights that turn on and off with a specific frequency. The reconstructed function that maps the state of the vehicle from light to light displays complex behavior for certain conditions. This chaotic behavior, which arises by the discontinuous nature of the map, displays an essential ingredient in traffic patterns and could be of relevance in studying traffic situations.