摘要：Microsimulation models are frequently used in traffic analysis. Various optimization methods are used in calibration, and the one method that has shown success is neural networks. This paper shows the responses of neural networks during calibration of a microsimulation traffic model. We analyzed two calibration methods by applying neural networks and comparing their neural network learning (according to their achieved correlation and the mean error of prediction) and their generalization ability (comparison of generalization results was analyzed in two steps). The best correlation between the microsimulation results and neural network prediction was 88.3%, achieved for the traveling time prediction, on which the first calibration method is based.
其他摘要：Učestala je primjena mikrosimulacijskih modela u prometnim analizama, a realnost dobivenih rezultata simulacije u funkciji je uspješnosti postupka kalibracije. Različite metode optimiranja primjenjuju se u postupku kalibracije, a jedna od metoda koja se p
关键词：microsimulation traffic models;calibration;response of neural networks;traveling time;queue parameters
其他关键词：mikrosimulacijski prometni model;kalibracija;odziv neuronske mreže;vrijeme putovanja;parametri kolone vozila
