Quickest Paths in Simulations of Pedestrians

This contribution proposes a method to make agents in a microscopic simulation of pedestrian traffic walk approximately along a path of estimated minimal remaining travel time to their destination. Usually models of pedestrian dynamics are (implicitly) built on the assumption that pedestrians walk along the shortest path. Model elements formulated to make pedestrians locally avoid collisions and intrusion into personal space do not produce motion on quickest paths. Therefore a special model element is needed, if one wants to model and simulate pedestrians for whom travel time matters most (e.g. travelers in a station hall who are late for a train). Here such a model element is proposed, discussed and used within the Social Force Model.Comment: revised version submitte

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

Redefining the role of obstacles in pedestrian evacuation

The placement of obstacles in front of doors is believed to be an effective strategy to increase the flow of pedestrians, hence improving the evacuation process. Since it was first suggested, this counterintuitive feature is considered a hallmark of pedestrian flows through bottlenecks. Indeed, despite the little experimental evidence, the placement of an obstacle has been hailed as the panacea for solving evacuation problems. In this work, we challenge this idea and experimentally demonstrate that the pedestrians flow rate is not necessarily altered by the presence of an obstacle. This result - which is at odds with recent demonstrations on its suitability for the cases of granular media, sheep and mice - differs from the outcomes of most of existing numerical models, and warns about the risks of carelessly extrapolating animal behaviour to humans. Our experimental findings also reveal an unnoticed phenomenon in relation with the crowd movement in front of the exit: in competitive evacuations, an obstacle attenuates the development of collective transversal rushes, which are hazardous as they might cause falls.Fil: Garcimartín, A.. Universidad de Navarra; EspañaFil: Maza, D.. Universidad de Navarra; EspañaFil: Pastor, J. M.. Focke Meler Gluing Solutions S.A.; EspañaFil: Parisi, Daniel Ricardo. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martín Gómez, C.. Universidad de Navarra; EspañaFil: Zuriguel, I.. Universidad de Navarra; Españ

LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework for generating crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior, flow, lighting conditions, viewpoint, noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by improving the accuracy of pedestrian detection and crowd behavior classification algorithms. LCrowdV would be released on the WWW

Guidelines for assessing pedestrian evacuation software applications

This paper serves to clearly identify and explain criteria to consider when evaluating the suitability of a pedestrian evacuation software application to assess the evacuation process of a building. Guidelines in the form of nine topic areas identify different modelling approaches adopted, as well as features / functionality provided by applications designed specifically for simulating the egress of pedestrians from inside a building. The paper concludes with a synopsis of these guidelines, identifying key questions (by topic area) to found an evaluation