A Heterogeneous Distributed Virtual Geographic Environment - Potential Application in Spatiotemporal Behavior Experiments

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

[1]  Dirk Helbing,et al.  Crowd behaviour during high-stress evacuations in an immersive virtual environment , 2016, Journal of The Royal Society Interface.

[2]  Hui Lin,et al.  Collaborative virtual geographic environments: A case study of air pollution simulation , 2011, Inf. Sci..

[3]  Yang Qing-shan HUMAN-ACTIVITY-GEOGRAPHICAL-ENVIRONMENT RELATIONSHIP, ITS SYSTEM AND ITS REGIONAL SYSTEM , 2001 .

[4]  Brett R. Fajen,et al.  Behavioral dynamics of steering, obstacle avoidance, and route selection. , 2003 .

[5]  Xiaogang Wang,et al.  Deeply learned attributes for crowded scene understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Sofie Notelaers,et al.  HeatMeUp: A 3DUI serious game to explore collaborative wayfinding , 2012, 2012 IEEE Symposium on 3D User Interfaces (3DUI).

[7]  Hui Lin,et al.  Virtual Geographic Environments (VGEs): A New Generation of Geographic Analysis Tool , 2013 .

[8]  Enrico Ronchi,et al.  A Virtual Reality Experiment on Flashing Lights at Emergency Exit Portals for Road Tunnel Evacuation , 2015, Fire Technology.

[9]  Li Lei,et al.  Development of Emergency Drills System for Petrochemical Plants Based on WebVR , 2011 .

[10]  Dirk Helbing,et al.  Evaluation of Control Interfaces for Desktop Virtual Environments , 2015, PRESENCE: Teleoperators and Virtual Environments.

[11]  Curtiss Murphy,et al.  Believable Dead Reckoning for Networked Games , 2011 .

[12]  Doug A. Bowman,et al.  Principles for Designing Effective 3D Interaction Techniques , 2014, Handbook of Virtual Environments, 2nd ed..

[13]  Heinrich H. Bülthoff,et al.  Behavioral Experiments in Spatial Cognition Using Virtual Reality , 1998, Spatial Cognition.

[14]  Serge P. Hoogendoorn,et al.  Pedestrian Behavior at Bottlenecks , 2005, Transp. Sci..

[15]  Christoph Hölscher,et al.  Virtual reality as an empirical research tool - Exploring user experience in a real building and a corresponding virtual model , 2015, Comput. Environ. Urban Syst..

[16]  Mordechay E. Haklay Virtual Reality and GIS : applications , trends and directions , 2002 .

[17]  Bolei Zhou,et al.  Measuring Crowd Collectiveness , 2013, CVPR.

[18]  Heinrich H. Bülthoff,et al.  Virtual Reality as a Valuable Research Tool for Investigating Different Aspects of Spatial Cognition , 2008, Spatial Cognition.

[19]  Paul M. Torrens,et al.  High-resolution space–time processes for agents at the built–human interface of urban earthquakes , 2014, Int. J. Geogr. Inf. Sci..

[20]  Daniel W. Gower,et al.  Simulator Sickness in the UH-60 (Black Hawk) Flight Simulator , 1989 .

[21]  Julian Togelius,et al.  Game Data Mining , 2013, Game Analytics, Maximizing the Value of Player Data.

[22]  Adrien Treuille,et al.  Continuum crowds , 2006, ACM Trans. Graph..

[23]  Jean-Pierre Corriveau,et al.  Dead Reckoning Using Play Patterns in a Simple 2D Multiplayer Online Game , 2014, Int. J. Comput. Games Technol..

[24]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[25]  Dmitri Williams,et al.  The Mapping Principle, and a Research Framework for Virtual Worlds , 2010 .

[26]  Ben Medler Visual Game Analytics , 2013, Game Analytics, Maximizing the Value of Player Data.

[27]  Enrico Ronchi,et al.  Virtual reality for fire evacuation research , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[28]  Brett R Fajen,et al.  Behavioral dynamics of steering, obstacle avoidance, and route selection. , 2003, Journal of experimental psychology. Human perception and performance.

[29]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

[30]  Johnell O Brooks,et al.  Simulator sickness during driving simulation studies. , 2010, Accident; analysis and prevention.

[31]  Ruggiero Lovreglio,et al.  The Need for Enhancing Earthquake Evacuee Safety by Using Virtual Reality Serious Games , 2017 .

[32]  Asya Natapov,et al.  Visibility of urban activities and pedestrian routes: An experiment in a virtual environment , 2016, Comput. Environ. Urban Syst..

[33]  Uwe D. Hanebeck,et al.  Calibrating dynamic pedestrian route choice with an Extended Range Telepresence System , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[34]  Pietro Guardini,et al.  Better Game Experience Through Game Metrics: A Rally Videogame Case Study , 2013, Game Analytics, Maximizing the Value of Player Data.

[35]  Soonhung Han,et al.  A virtual reality based fire training simulator integrated with fire dynamics data , 2012 .

[36]  Ruggiero Lovreglio,et al.  A mixed logit model for predicting exit choice during building evacuations , 2016 .

[37]  Enrico Ronchi,et al.  Social influence on route choice in a virtual reality tunnel fire , 2014 .

[38]  John C. Hart,et al.  The CAVE: audio visual experience automatic virtual environment , 1992, CACM.

[39]  Gong Jianhu On Thought and Methodology of Virtual Geographic Experiment , 2013 .

[40]  Weiya Chen,et al.  Collaboration in Multi-user Immersive Virtual Environment , 2015 .

[41]  Lars C. Wolf,et al.  On the suitability of dead reckoning schemes for games , 2002, NetGames '02.

[42]  Enrico Ronchi,et al.  Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization , 2015 .

[43]  Rong Li,et al.  Simulation and analysis of congestion risk during escalator transfers using a modified social force model , 2015 .