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This paper proposes a fuzzy neural network (FNN)
based approach to construct an individual-oriented car-following
system. The feature of this system is firstly to incorporate
a personal risk-taking factor in addition to other mechanical
factors as the input parameters. Through the learning capability
of artificial network, the complex membership functions
between the input factors and the output (i.e., the appropriate
car-following headway) can be efficiently established,
and then the fuzzy logic rules can be properly constructed.
The performance of the FNN system is finally assessed against
the field data. The results are inspiring that the system
is proven capable of providing highly accurate predictions
of the required car-following headways from person to person
at various speeds. The success of this study provides some
clues of utilizing FNN techniques in exploring some individual-oriented
machines.
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