Review article

https://doi.org/10.13167/2015.10.8

ANALYSIS OF NEURAL NETWORK RESPONSES IN CALIBRATION OF MICROSIMULATION TRAFFIC MODEL

Irena Ištoka Otković orcid.org/0000-0003-3650-2021 ; Josip Juraj Strossmayer University of Osijek, Faculty of Civil Engineering Osijek
Damir Varevac orcid.org/0000-0002-0473-0639 ; Josip Juraj Strossmayer University of Osijek, Faculty of Civil Engineering Osijek
Matjaž Šraml orcid.org/0000-0002-0638-2703 ; University of Maribor, Faculty of Civil Engineering Maribor

Abstract

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.

Keywords

microsimulation traffic models; calibration; response of neural networks; traveling time; queue parameters

Hrčak ID:

140696

URI

https://hrcak.srce.hr/140696

Publication date:

2.7.2015.

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