Leveraging human behavior models to predict paths in indoor environments

2011 ◽  
Vol 7 (3) ◽  
pp. 319-330 ◽  
Author(s):  
Bulent Tastan ◽  
Gita Sukthankar
2008 ◽  
Author(s):  
Steven Solomon ◽  
Michael van Lent ◽  
Mark Core ◽  
Paul Carpenter ◽  
Milton Rosenberg

2017 ◽  
Vol 46 (6) ◽  
pp. 985-1002 ◽  
Author(s):  
Gian Paolo Cimellaro ◽  
Fabrizio Ozzello ◽  
Alessio Vallero ◽  
Stephen Mahin ◽  
Benshun Shao

Author(s):  
Tylar Murray ◽  
Eric Hekler ◽  
Donna Spruijt-Metz ◽  
Daniel E. Rivera ◽  
Andrew Raij

2009 ◽  
Vol 6 (4) ◽  
pp. 588-597 ◽  
Author(s):  
O. Brdiczka ◽  
M. Langet ◽  
J. Maisonnasse ◽  
J.L. Crowley

2015 ◽  
Vol 2015 (0) ◽  
pp. _2P1-M10_1-_2P1-M10_3
Author(s):  
Kae Doki ◽  
Takahiro Hirai ◽  
Takashi Hattori ◽  
Shinji Doki ◽  
Yuki Funabora ◽  
...  

Author(s):  
Hiromitsu Hattori ◽  
◽  
Yuu Nakajima ◽  
Shohei Yamane

As it is getting easier to obtain reams of data on human behavior via ubiquitous devices, it is becoming obvious that we must work on two conflicting research directions for realizing multiagent-based social simulations; creating large-scale simulations and elaborating fine-scale human behavior models. The challenge in this paper is to achievemassively urban traffic simulations with fine-grained levels of driving behavior. Toward our objective, we show the design and implementation of a multiagent-based simulation platform, that enables us to execute massive but sophisticated multiagent traffic simulations. We show the capability of the developed platform to reproduce the urban traffic with a social experiment scenario. We investigate its potential to analyze the traffic from both macroscopic and microscopic viewpoints.


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