3D Indoor Environment Abstraction for Crowd Simulations in Complex Buildings

This paper presents an approach for the automatic abstraction of built environments needed for pedestrian dynamics from any building configuration. The approach assesses the usability of navigation mesh to perform realistically pedestrian simulation considering the physical structure and pedestrian abilities for it. Several steps are examined including the creation of a navigation mesh, space subdivision, border extraction, height map identification, stairs classification and parametrisation, as well as pedestrian simulation. A social-force model is utilised to simulate the interactions between pedestrians and an environment. To perform quickly different 2D/3D geometrical queries various spatial indexing techniques are used, allowing fast identification of navigable spaces and proximity checks related to avoidance of people and obstacles in built environments. For example, for a moderate size building having eight floors and a net area of 13,000 m2, it takes only 104 s to extract the required building information to run a simulation. This approach can be used for any building configuration extracting automatically needed features to run pedestrian simulations. In this way, architects, urban planners, fire safety engineers, transport modellers and many other users without the need to manually interact with a building model can perform immediately crowd simulations.

Introducing Interactivity in Disaster Recovery Simulations

Crowd simulations are widely used to study and predict the human behavior in disaster scenarios. In this paper, we introduce real-time user interactivity into the simulation process of virtual environments (e.g., buildings with rooms and doors between them). We develop a new tactical path-planning model that translates the interactive virtual environment into an abstract graph in order to calculate the shortest paths in real time. Our extension of the Vadere simulation framework with interactivity features allows the users to better understand the actual problem situations and to analyze them. Our experiments demonstrate the effectiveness of the approach by simulating the evacuation of students in groups and as individuals from the Schloss Muenster (the administrative building of the University of Muenster) in Germany. During simulation run time, the user can interact with the virtual environment spontaneously (e.g., by opening and closing doors) while our model recalculates the shortest paths for agents in real time.

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.

Empirical Findings from an Ascending Stair Evacuation Exercise in a Subway Station

Crowd simulations have proven to be a valuable numerical tool for evacuation analysis. There is series of research and empirical evacuation studies for infrastructures and buildings. In contrast to research on evacuation via descending stairs, little attention has been given to ascending stairs, but they are an important criterion, especially in subway stations with high passenger frequencies. In this paper, we present the findings from an evacuation exercise in a subway station with long ascending stairs. The empirical findings showed an increasing walking time on the ascending stairs during evacuation. Also, the flow rate differs with higher flow rates at the beginning of the stairs and lower values at the end of the stairs. The mechanism behind these results has still to be investigated, but the findings already provide an interesting basis for modelling and validating evacuation simulations over long ascending stairs.

A Turing test for crowds

The accuracy and believability of crowd simulations underpins computational studies of human collective behaviour, with implications for urban design, policing, security and many other areas. Accuracy concerns the closeness of the fit between a simulation and observed data, and believability concerns the human perception of plausibility. In this paper, we address both issues via a so-called ‘Turing test’ for crowds, using movies generated from both accurate simulations and observations of real crowds. The fundamental question we ask is ‘Can human observers distinguish between real and simulated crowds?’ In two studies with student volunteers ( n = 384 and n = 156), we find that non-specialist individuals are able to reliably distinguish between real and simulated crowds when they are presented side-by-side, but they are unable to accurately classify them. Classification performance improves slightly when crowds are presented individually, but not enough to out-perform random guessing. We find that untrained individuals have an idealized view of human crowd behaviour which is inconsistent with observations of real crowds. Our results suggest a possible framework for establishing a minimal set of collective behaviours that should be integrated into the next generation of crowd simulation models.

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

Movement behavior models of pedestrian agents form the basis of computational crowd simulations. In contemporary research, a large number of models exist. However, there is still no walking behavior model that can address the various influence factors of movement behavior holistically. Thus, we endorse the use of artificial neural networks to develop walking behavior models because machine learning methods can integrate behavioral factors efficiently, automatically, and data-driven. In this paper, we support this approach by providing a framework that describes how to include artificial neural networks into a pedestrian research context. The framework comprises 5 phases: data, replay, training, simulation, and validation. Furthermore, we describe and discuss a prototype of the framework.