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Macroscopic traffic flow model calibration using different optimization algorithms

Spiliopoulou Anastasia, Papamichail Ioannis, Papageorgiou Markos, Tyrinopoulos, Yannis, Chrysoulakis John

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URI: http://purl.tuc.gr/dl/dias/049BB549-F22D-4686-9550-3D393808CF94
Year 2017
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation A. Spiliopoulou, I. Papamichail, M. Papageorgiou, Y. Tyrinopoulos and J. Chrysoulakis, "Macroscopic traffic flow model calibration using different optimization algorithms," Oper. Res., vol. 7, no. 1, pp. 145-164, April 2017. doi: 10.1007/s12351-015-0219-4 https://doi.org/10.1007/s12351-015-0219-4
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Summary

This study tests and compares different optimization algorithms employed for the calibration of a macroscopic traffic flow model. In particular, the deterministic Nelder–Mead algorithm, a stochastic genetic algorithm and the stochastic cross-entropy method are utilized to estimate the parameter values of the METANET model for a particular freeway site, using real traffic data. The resulting models are validated using various traffic data sets and the optimization algorithms are evaluated and compared with respect to the accuracy of the produced validated models as well as the convergence speed and the required computation time. The validation results showed that all utilized optimization algorithms were able to converge to robust model parameter sets, albeit achieving different performances considering the convergence speed and the required computation time.

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