Helmut Schrom-Feiertag

Bridging the gap between visual exploration and agent-based pedestrian simulation in a virtual environment

We present a system to evaluate and improve visual guidance systems and signage for pedestrians inside large buildings. Given a 3D model of an actual building we perform agent-based simulations mimicking the decision making process and navigation patterns of pedestrians trying to find their way to predefined locations. Our main contribution is to enable agents to base their decisions on realistic threedimensional visibility and occlusion cues computed from the actual building geometry with added semantic annotations (e.g. meaning of signs, or purpose of inventory), as well as an interactive visualization of simulated movement trajectories and accompanying visibility data tied to the underlying 3D model. This enables users of the system to quickly pinpoint and solve problems within the simulation by watching, exploring and understanding emergent behavior inside the building. This insight gained from introspection can in turn inform planning and thus improve the effectiveness of guidance systems.

Evaluation of indoor guidance systems using eye tracking in an immersive virtual environment

In this article, we present a novel method for evaluating guidance systems using an immersive virtual environment in combination with a mobile eye tracking system. Accurate measurements of position, locomotion, viewing frustum, and gaze are captured in the virtual environment. They are applied to the projection of an attention map onto the virtual 3D environment for visualizing the fixation in the environment as well as the amount of time objects were fixated. To demonstrate the methods applicability, we conducted an experiment with 24 participants evaluating a guidance system of a large public infrastructure. The results show that our method allows for the creation of attention maps as well as for the identification of objects of interest based on eye tracking.

Simulation-Based Forecasts of Crowd Flows at Major Events Using Real-Time Measurements

The complexity and dynamic nature of large events arise the need for decision makers to assess the current situation and to derive multi-temporal forecasts in order to identify critical situations in a timely manner and to initiate appropriate countermeasures. In this work, we present a fast mesoscopic simulation model which incorporates measurements from counting and Bluetooth sensors, thus providing real-time forecasts of crowd flows at major events. With this approach already a sparse placement of sensors at strategic points on an event area is sufficient to achieve the necessary spatial and temporal resolution for a complete characterization of the current crowd flows. For model verification and validation, we investigated case studies from two music festivals in Austria in 2012 and 2013 where extensive measurements on human motion data were obtained to evaluate the deviations of the simulation results from the measured walking times.

Creating a richer data source for 3D pedestrian flow simulations in public transport

The distribution of passenger flows in public transport environments can be predicted through simulations of flow characteristics. The flow characteristics are usually based on average values, which do not include behavioural characteristics of people with mobility impairments such as wheelchair users, individuals with prams and small children, elderly people or people with sensory impairments. In the project at hand we gather data on orientation behaviour of these passenger groups through experience and task-related research. In a combination of methods -- video and audio recording, user centered scenario and task development, questionnaires, interviews and time-motion-event (TME) recording -- qualitative and quantitative data are generated. By linking qualitative information to quantitative measures a multi-dimensional description of the behaviour of individuals and user groups in a transport interchange emerges. It is expected that by comparing and contrasting the resulting behavioural measures to the average values currently used to represent passenger flows, the quality of simulations can be considerably enhanced.

A Mobile LBS for Geo-Content Generation Facilitating Users to Share, Rate and Access Information in a Novel Manner

While mobile applications typically offer access to standardized ‘basic’ geo-content, there is evidence in the human sciences that people ac-tually prefer subjective information sources for decision making, e.g. personal stories about experiences by family and friends. The success story of content communities in web applications confirms the wide acceptance of innovative information systems that offer the potential to consume, to produce and to rate personalized data. Therefore in this paper an approach for a mobile, interactive and integrated system is presented that do not only deliver basic geo-referenced information, but also allow the users to create location aware information for themselves and other users: information like hints and personal experience will be geo-referenced, time stamped and annotated with text and keyword information. It is stored and exchanged between community members. This individual information creates information content which is continuously growing and updated. Image based and text based information retrieval in combination with location information are used to provide easy access to relevant information. In this paper we outline the system architecture and components that enable these new approaches also providing augmented reality navigation. The approach was tested in a field test study and results and open issues are given. The system worked well and could be applied to future experiments in order to gain more insight in the mobile users’ behavior in real contexts.

Informationsvermittlung, Besucheranalyse und nachhaltige Angebotsplanung in Schutzgebieten auf der Basis eines mobilen Guides

Auf Grund standig steigender Besucherzahlen in Grosschutzgebieten ist bei der Angebotsplanung fur Besucher eine langfristige Sicherung kultureller und naturlicher Gegebenheiten wichtig. Im Rahmen des Projekts BALANCE wurden dazu eine GPS-basierte mobile Applikation zur Informationsvermittlung und zur Datenaufzeichnung, ein Datenmanagementtool zur Angebotserstellung und ein Tool zur Besucheranalyse entwickelt. Die gesammelten Daten wurden mit Hilfe des entwickelten Analysetools ausgewertet. Fur das Schutzgebietsmanagement wurden dadurch wichtige Informationen uber das Verhalten der Besucher (z.B. bevorzugte Routen, Anzahl und Dauer von Stopps, etc.) ersichtlich. Die Entwicklung erfolgte in Zusammenarbeit mit zwei Nationalparks und wurde abschliesend im Rahmen eines Pilottests durch Besucher und Nationalparkbetreiber getestet und evaluiert.

On extracting commuter information from GPS motion data

Commuters rely on realistic and real-time information in order to optimize the time spent on commuting between home and work. Delays in (urban) transport and congestion for individual motorized transport are a major issue for unnecessary long travel times. While some of these delays occur randomly, there is also a systematic component. In this paper we describe a data-driven approach to analyze positions of an individual collected using GPS to obtain information on the individuals typical routes, typical schedules and the used mode of transport. Furthermore, we propose an approach to model the probability of an event like missing a train as a function of time. This allows to optimize the expected commuting time based solely on the commuters motion history. Suitability of the approach is demonstrated in a real world application based on a dataset comprising six weeks of GPS tracks.

Simulation of Handicapped People Finding Their Way Through Transport Infrastructures

This paper presents a research effort put into enhancing existing simulation models by including models for the motion and orientation behavior of handicapped people being unfamiliar with a transport infrastructure. On the tactical level the perception of guidance systems is modeled and makes it possible to simulate agent navigation through an unknown infrastructure using the present signage. The guidance information is determined against relevant influencing factors in a simulated virtual 3D environment. For the proof of concept the applicability of the wayfinding algorithm is demonstrated in three different scenarios. Results show that the proposed simulation model facilitates an agent to find its way autonomously through a transport infrastructure based on signage information only. This makes it possible to evaluate the visibility of the guidance system and can reveal areas lacking guidance information for people unfamiliar with the infrastructure especially for elderly and handicapped people with reduced reception capabilities.

Route-Choice Modeling for Pedestrian Evacuation Based on Infrastructure Knowledge and Personal Preferences

In recent years, pedestrian simulation has been a valuable tool for the quantitative assessment of egress performance in various environments during emergency evacuation. For a high level of realism, an evacuation simulation requires a behavioral model that takes into account behavioral aspects of real pedestrians. In many studies, however, it is assumed that simulated pedestrians have a global knowledge of the infrastructure and choose either a predefined or the shortest route. It is questionable whether this simplification provides realistic results. This study addresses the problem of human-like route-choice behavior for microscopic pedestrian simulations. A route-choice model is presented that considers two concepts: first, the modeling of infrastructure knowledge to represent the variations in the decision-making processes of pedestrians with different degrees of familiarity with the infrastructure (e.g., regular commuters versus first-time visitors). Second, for each pedestrian the internal preference for selecting a certain path can be calibrated to allow the choice for the fastest routes or the ones that are most convenient for the agent (e.g., by avoiding stairs). The approach here uses a hybrid route-choice behavior model composed of a graph-based macrolevel representation of the environment, which is augmented with local information to avoid obstacles and dense crowds in the vicinity. This method was applied with different parameter sets in an evacuation study of a multilevel subway station. The results show the impact of these parameters on evacuation times, use of infrastructure elements, and crowd density at specific locations.

Critical Situation Monitoring at Large Scale Events from Airborne Video Based Crowd Dynamics Analysis

Comprehensive monitoring of movement behaviour and raising dynamics in crowds allow an early detection and prediction of critical situations that may arise at large-scale events. This work presents a video based airborne monitoring system enabling the automated analysis of crowd dynamics and to derive potentially critical situations. The results can be used to prevent critical situations by supporting security staff to control the crowd dynamics early enough. This approach enables preventing upraise of panic behaviour by automated early identification of hazard zones and offering a reliable basis for early intervention by security forces. This approach allows the surveillance and analysis of large scale monitored areas of interest and raising specific alarms at the management and control system in case of potentially critical situations. The integrated modules extend classical mission management by providing essential decision support possibilities for assessing the situation and managing security and emergency crews on site within short time frames.