From theory to practice: Gaussian process metamodels for the sensitivity analysis of traffic simulation models. A case study of the Aimsun mesoscopic model.

摘要

This paper discusses a metamodel-based technique for model sensitivity analysis and applies it to theAimsun mesoscopic model. Throughout the paper it is argued that the application of sensitivity analysisis crucial for the true comprehension and correct use of the traffic simulation model while alsoacknowledging that the main obstacle to an extensive use of the most sophisticated techniques is the highnumber of model runs they usually require.For this reason we have tested the possibility of performing sensitivity analysis not on a modelbut on its metamodel approximation. Important issues arising when estimating a metamodel have beeninvestigated and commented on in the specific application to the Aimsun model. Among these issues isthe importance of selecting a proper sampling strategy based on low discrepancy random numbersequences and the importance of selecting a class of metamodels able to reproduce the inputs-ouputsrelationship in a robust and reliable way. Sobol sequences and Gaussian process metamodels have beenrecognized as the appropriate choices.The proposed methodology has been assessed by comparing the results of the application ofvariance-based sensitivity analysis techniques to the simulation model and to a metamodel estimated with512 model runs, on a variety of traffic scenarios and model outputs. Results confirm the powerfulness ofthe proposed methodology and also open up to a more extensive application of sensitivity analysistechniques to complex traffic simulation models.