Theory to practice: global sensitivity analysis of the Aimsun meso model

Abstract

This paper examines a metamodel-based technique for model sensitivity analysis and applies it to the Aimsun mesoscopic model. The paper argues that sensitivity analysis is crucial for the best use of the traffic simulation models while also acknowledging that the main obstacle to an extensive use of the most sophisticated techniques is the high number of model runs they usually require. To get around this problem, the paper considers the possibility of performing sensitivity analysis not on a model but on its metamodel approximation. Important issues arising when estimating a metamodel have been investigated and commented on in the specific application to the Aimsun model. Among these issues is the importance of selecting a proper sampling strategy based on low discrepancy random number sequences and the importance of selecting a class of metamodels able to reproduce the inputs-ouputs relationship in a robust and reliable way. Sobol sequences and Gaussian process metamodels have been recognized as the appropriate choices. The paper assesses the proposed methodology by comparing the results of the application of variance-based sensitivity analysis techniques to the simulation model and to a metamodel estimated with 512 model runs, on a variety of traffic scenarios and model outputs. Results confirm the powerfulness of the proposed methodology and also open up to a more extensive application of sensitivity analysis techniques to complex traffic simulation models.