Simulation of Driver Behavior with Agent-Based Back-Propagation Neural Network
نویسندگان
چکیده
but does require sufficient real traffic and action data to capture the underlying relationship between states and actions. Therefore, ANN models estimate actions on the basis of the real state–action mapping of natural behavior. Data from the naturalistic driving database of the Naturalistic Truck Driving Study (NTDS) (1), collected by the Virginia Tech Transportation Institute and Blanco et al. (2), are used to find the real causalities and responses of truck drivers in car-following situations. In this paper, first the process of calibrating the well-known Gazis–Herman–Rothery (GHR) car-following model for an individual driver is described. Then, a back-propagation (BP) neural network is constructed to train agents that represent individual drivers. Both methods use the same naturalistic data set.
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