Marcello Montanino

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Automated calibration of microscopic traffic flow models is all but simple for a number of reasons, including the computational complexity of black-box optimization and the asymmetric importance of parameters in influencing model performances. The main objective of this paper is therefore to provide a robust methodology to simplify car-following models,(More)
In the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly derived by means of an optimization framework, which tries to(More)
This paper discusses a metamodel-based technique for model sensitivity analysis and applies it to the Aimsun mesoscopic model. Throughout the paper it is argued that the application of sensitivity analysis is crucial for the true comprehension and correct use of the traffic simulation model while also acknowledging that the main obstacle to an extensive use(More)
The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results(More)
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