Abstract
The speed-traffic flow density interdependence diagram has a number of variations, starting with the theoretical model, through various empirical models that were developed and models based on actual research done on traffic flow. The functional interdependence is obtained using the Sugeno fuzzy logic system, where representative values proposed in HCM 2010 have been adopted as parameters of output association functions. Subsequently the neural network is trained based on actual traffic flow data, which by adjusting the association function of the fuzzy logic system yields an output form of the basic traffic flow diagram. It was noticed that this hybrid expert system produces better output results by applying the “subtractive clustering“ method on data that are used for training a neural network. Finally, the model was tested on several input data groups, and the interdependence between speed and traffic flow density is shown in graphical form.
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