Modeling rationality to control self-organization of crowds: An environmental approach

In this paper we propose a classification of crowd models in built environments based on the assumed pedestrian ability to foresee the movements of other walkers. At the same time, we introduce a new family of macroscopic models, which make it possible to tune the degree of predictiveness (i.e., rationality) of the individuals. By means of these models we describe both the natural behavior of pedestrians, i.e., their expected behavior according to their real limited predictive ability, and a target behavior, i.e., a particularly efficient behavior one would like them to assume (for, e.g., logistic or safety reasons). Then we tackle a challenging shape optimization problem, which consists in controlling the environment in such a way that the natural behavior is as close as possible to the target one, thereby inducing pedestrians to behave more rationally than what they would naturally do. We present numerical tests which elucidate the role of rational/predictive abilities and show some promising results about the shape optimization problem

CellEVAC: an adaptive guidance system for crowd evacuation through behavioral optimization

A critical aspect of crowds' evacuation processes is the dynamism of individual decision making. Identifying optimal strategies at an individual level may improve both evacuation time and safety, which is essential for developing efficient evacuation systems. Here, we investigate how to favor a coordinated group dynamic through optimal exit-choice instructions using behavioral strategy optimization. We propose and evaluate an adaptive guidance system (Cell-based Crowd Evacuation, CellEVAC) that dynamically allocates colors to cells in a cellbased pedestrian positioning infrastructure, to provide efficient exit-choice indications. The operational module of CellEVAC implements an optimized discrete-choice model that integrates the influential factors that would make evacuees adapt their exit choice. To optimize the model, we used a simulation?optimization modeling framework that integrates microscopic pedestrian simulation based on the classical Social Force Model. In the majority of studies, the objective has been to optimize evacuation time. In contrast, we paid particular attention to safety by using Pedestrian Fundamental Diagrams that model the dynamics of the exit gates. CellEVAC has been tested in a simulated real scenario (Madrid Arena) under different external pedestrian flow patterns that simulate complex pedestrian interactions. Results showed that CellEVAC outperforms evacuation processes in which the system is not used, with an exponential improvement as interactions become complex. We compared our system with an existing approach based on Cartesian Genetic Programming. Our system exhibited a better overall performance in terms of safety, evacuation time, and the number of revisions of exit-choice decisions. Further analyses also revealed that Cartesian Genetic Programming generates less natural pedestrian reactions and movements than CellEVAC. The fact that the decision logic module is built upon a behavioral model seems to favor a more natural and effective response. We also found that our proposal has a positive influence on evacuations even for a low compliance rate (40%).Ministerio de Economía y Competitivida

Adaptive cell-based evacuation systems for leader-follower crowd evacuation

The challenge of controlling crowd movement at large events expands not only to the realm of emergency evacuations but also to improving non-critical conditions related to operational efficiency and comfort. In both cases, it becomes necessary to develop adaptive crowd motion control systems. In particular, adaptive cell-based crowd evacuation systems dynamically generate exit-choice recommendations favoring a coordinated group dynamic that improves safety and evacuation time. We investigate the viability of using this mechanism to develop a ‘‘leader-follower’’ evacuation system in which a trained evacuation staff guides evacuees safely to the exit gates. To validate the proposal, we use a simulation–optimization framework integrating microscopic simulation. Evacuees’ behavior has been modeled using a three-layered architecture that includes eligibility, exit-choice changing, and exit-choice models, calibrated with hypothetical-choice experiments. As a significant contribution of this work, the proposed behavior models capture the influence of leaders on evacuees, which is translated into exitchoice decisions and the adaptation of speed. This influence can be easily modulated to evaluate the evacuation efficiency under different evacuation scenarios and evacuees’ behavior profiles. When measuring the efficiency of the evacuation processes, particular attention has been paid to safety by using pedestrian Macroscopic Fundamental Diagrams (p-MFD), which model the crowd movement dynamics from a macroscopic perspective. The spatiotemporal view of the evacuation performance in the form of crowd-pressure vs. density values allowed us to evaluate and compare safety in different evacuation scenarios reasonably and consistently. Experimental results confirm the viability of using adaptive cell-based crowd evacuation systems as a guidance tool to be used by evacuation staff to guide evacuees. Interestingly, we found that evacuation staff motion speed plays a crucial role in balancing egress time and safety. Thus, it is expected that by instructing evacuation staff to move at a predefined speed, we can reach the desired balance between evacuation time, accident probability, and comfort