Macroscopic traffic flow model calibration using different optimization algorithms

Anastasia Spiliopoulou (), Ioannis Papamichail, Markos Papageorgiou, Yannis Tyrinopoulos and John Chrysoulakis
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Anastasia Spiliopoulou: Technical University of Crete
Ioannis Papamichail: Technical University of Crete
Markos Papageorgiou: Technical University of Crete
Yannis Tyrinopoulos: Technological Educational Institute of Athens
John Chrysoulakis: Technological Educational Institute of Athens

Operational Research, 2017, vol. 17, issue 1, No 7, 145-164

Abstract: 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.

Keywords: Macroscopic traffic flow models; Model calibration; Comparison of optimization algorithms (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s12351-015-0219-4

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