Civil Safety Research (IAS-7)

Overview

Overview of projects with IAS-7 contributing

LoStInCrowds

Locomotion strategies and injury potential in dense pedestrian crowds

CroMa-PRO

Simulation-supported traffic and crowd management at major events

BESKID

Fair2Ped+

More FAIR to Pedestrian Dynamics Data

SISAME 2.0

Simulations for Safety at Major Events

CrowdDNA

CrowdDNA is a joint project of 7 organizations from 4 European countries, funded by the European Commission (EU H2020). It aims to enable a new generation of "crowd technologies"; a system that avoids fatalities, minimizes inconvenience and maximizes efficiency in crowd management.

CrowdDNA

Technologies for Computer Assisted Crowd Management

Fair2Ped

FAIR to Pedestrian Dynamics Data

Multi-Agent modeling of dense crowd dynamics: Predict & Understand

Crowd Management in Transport Infrastructures

Simulations for Safety at Major Events

KapaKrit

Safety for people with physical, mental or age-related impairments

Optimization of smoke extraction and passenger guidance in subway stations: experiments and simulations

Quantitative Description of Jamming in Pedestrian Streams

Building blocks for the security of major events

HERMES

Boundary Effects and Non-Equilibrium States in Pedestrian Dynamics - Experiments and Modelling

CroMa

The aim of the CroMa project is to achieve greater robustness, safety and performance of stations during peak loads through improved structural arrangements, appropriate crowd management and adapted cross-organizational instructions for action.

Optical Head Tracking: Software PeTrack

With PeTrack, extensive test series with a large number of people can be analyzed. Individual codes allow personalized trajectories with static information about each participant (e.g. age, gender).

3D Motion in Crowds

For the understanding of the dynamics inside crowds, reliable empirical data are needed, which could increase safety and comfort for pedestrians and the design of models reflecting the real dynamics. To analyze effects like clogging, overtaking or the threading into a bottleneck the orientation of body parts like shoulder, pelvis, arms and feet are of high interest. In dense crowds a well-calibrated camera system can extract absolute head positions with high accuracy. The inclusion of inertial sensors or even self-contained full-body motion capturing (mocap) systems allows the relative tracking of body parts or even capturing the locomotion of the whole body.

Pushing in Crowds

In many dangerous crowd situations, the combination of high density (more than 8 persons per m2) and movement within the crowd results in strong pressure on the human body and increases the likelihood of life-threatening falls. The research group studies a. the collective dynamics resulting from pushing; b. under which circumstances do people in a crowd start to push intentionally; c. develops AI tools to automatic detect pushing in crowds and d. develops agent based models to model spatio-temporal dynamics of pushing propagation in crowds.

Visibility in Fires

In case of fire, one of the threads is the inhabited perception of evacuation signs. With our research we aim to understand the involved physical phenomena (light-smoke interaction) and the human perception of signs. For this, we conduct real-scale fire experiments and develop new theoretical models. All this leads to a better, i.e. more realistic, numerical prediction in CFD-based simulation models and smoke representation in VR.

Flame Spread

The prediction of the cause of a fire, thus the spread of it, is one of the major challenges in fire safety science. It allows to determine the dynamics of the fire under consideration of the relevant physical and chemical processes, and especially the behaviour of the burning material. We conduct experiments on the micro scale to understand the pyrolysis of the material, i.e. the thermal decomposition, to determine the relevant material parameter. With these, we utilise computation fluid dynamics models to predict the flame spread in large scale experiments.