NDLI: Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network

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Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network

Content Provider	Semantic Scholar
Author	Chong, Linsen Abbas, Montasir M. Medina, Alejandra
Copyright Year	2011
Abstract	Two microscopic simulation methods are compared for driver behavior: the Gazis-Herman-Rothery (GHR) car-following model and a proposed agent-based neural network model. To analyze individual driver characteristics, a back-propagation neural network is trained with car-following episodes from the data of one driver in the naturalistic driving database to establish action rules for a neural agent driver to follow under perceived traffic conditions during car-following episodes. The GHR car-following model is calibrated with the same data set, using a genetic algorithm. The car-following episodes are carefully extracted and selected for model calibration and training as well as validation of the calibration rules. Performances of the two models are compared, with the results showing that at less than 10-Hz data resolution the neural agent approach outperforms the GHR model significantly and captures individual driver behavior with 95% accuracy in driving trajectory.
Starting Page	44
Ending Page	51
Page Count	8
File Format	PDF HTM / HTML
DOI	10.3141/2249-07
Volume Number	2249
Alternate Webpage(s)	http://web.mit.edu/linsenc/www/Simulation%20of%20Driver%20Behavior%20with%20Agent-Based%20Back-Propagation%20Neural%20Network.pdf
Alternate Webpage(s)	https://doi.org/10.3141/2249-07
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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