Adaptive Models for Microscopic Traffic Modelling

Author(s):  
Andrei C. Nae ◽  
Ioan Dumitrache
2009 ◽  
Author(s):  
Pramod K. Varshney ◽  
Chilukuri K. Mohan ◽  
Krishan G. Mehrotra
Keyword(s):  

Author(s):  
Jon Bendor ◽  
Daniel Diermeier ◽  
Michael M. Ting

Author(s):  
Sarvesh Bidkar ◽  
Pascal Dom ◽  
Rene Bonk ◽  
Thomas Pfeiffer
Keyword(s):  

2013 ◽  
Vol 109 (5) ◽  
pp. 1259-1267 ◽  
Author(s):  
Devika Narain ◽  
Robert J. van Beers ◽  
Jeroen B. J. Smeets ◽  
Eli Brenner

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known, however, about how priors develop in environments with nonstationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various nonadaptive and adaptive models. Simulations show that models that rapidly adapt to nonstationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment.


2018 ◽  
Vol 57 (42) ◽  
pp. 14286-14296
Author(s):  
Nobuhiro Yuge ◽  
Kenichi Tanaka ◽  
Hiromasa Kaneko ◽  
Kimito Funatsu
Keyword(s):  

2006 ◽  
Author(s):  
Lifford McLauchlan ◽  
Mehrübe Mehrübeoğlu
Keyword(s):  

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