Abstract
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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Acknowledgments
This research was co-financed by the European Union (European Social Fund—ESF) and by national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funded Project: ARCHIMEDES III, Investing in Society’s Knowledge through the European Social Fund. The authors would like to thank ATTIKES DIADROMES S.A. for providing the utilised traffic data from Attiki Odos freeway in Athens, Greece and also Prof. Mike Maher and Dr. Dong Ngoduy for providing the source code for the kernel-based cross-entropy method.
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Spiliopoulou, A., Papamichail, I., Papageorgiou, M. et al. Macroscopic traffic flow model calibration using different optimization algorithms. Oper Res Int J 17, 145–164 (2017). https://doi.org/10.1007/s12351-015-0219-4
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DOI: https://doi.org/10.1007/s12351-015-0219-4