Comprehensive Approach for the Sensitivity Analysis of High-Dimensional and Computationally Expensive Traffic Simulation Models

Abstract

The reliability of traffic model results is strictly connected to the quality of its calibration. A challenge arising in this context concerns the selection of the most influential input parameters. A model sensitivity analysis should be used with this aim. However, because of the limitations of time and computational resources, a proper sensitivity analysis is rarely performed in common practice. A recent study introduced a methodology based on Gaussian process metamodels for the sensitivity analysis of computationally expensive traffic simulation models. The main limitation was a dependence on model dimensionality. When the model has more than about 15 to 20 parameters, estimation of a Gaussian process metamodel (also known as a Kriging metamodel) may become problematic. In this paper, the Kriging-based approach is coupled with a recently developed approach, quasi-optimized trajectory-based elementary effects (quasi-OTEE), for the sensitivity analysis of computationally expensive models. The quasi-OTEE sensitivity analysis can be used to identify the whole subset of sensitive parameters of a high-dimensional model, and the Kriging-based sensitivity analysis can then be used to refine the analysis and to rank the different parameters of the subset in a more reliable way. Application of this new sequential sensitivity analysis method is illustrated with the Wiedemann-74 car-following model. Results show that the new method requires 40 times fewer model evaluations than a standard variance-based sensitivity analysis to identify the influential parameters and their ranks.