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.
Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network
Transportation Research Record: Journal of the Transportation Research Board
Transportation Research Record: Journal of the Transportation Research Board ; 2249 , 1 ; 44-51
01.01.2011
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network
Online Contents | 2011
|Analog Delta-Back-Propagation Neural-Network Circuitry
NTRS | 1990
|Tunnel area driver behavior simulation method based on simulation driver
Europäisches Patentamt | 2021
|Research on Flight Accidents Prediction based Back Propagation Neural Network
ArXiv | 2024
|Research on Flight Accidents Prediction based Back Propagation Neural Network
Springer Verlag | 2024
|