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

2014 ◽  
Vol 2422 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Qiao Ge ◽  
Biagio Ciuffo ◽  
Monica Menendez
Keyword(s):  
Sensitivity Analysis ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Comprehensive Approach ◽  
High Dimensional ◽  
Computationally Expensive

Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network

2000 ◽  
Vol 1727 (1) ◽  
pp. 52-60 ◽  
Author(s):  
Loren Bloomberg ◽  
Jim Dale
Keyword(s):  
Sensitivity Analysis ◽  
Performance Measures ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Design Decision ◽  
Simulation Tools ◽  
Critical Design ◽  
Complex Program ◽  
Comparative Information ◽  
Model Approach

Traffic simulation packages like CORSIM and VISSIM are frequently used as tools for the analysis of traffic since they are effective approaches for quantification of the benefits and limitations of different alternatives. The concern of those who are cautious or skeptical about the application of a complex program to making a critical design decision is often appropriate, as many models are unproven or little information about their accuracy is available. As these simulation models become easier to use, it may be practical to use more than one model in some studies. The two-model approach was applied as a means of making the analysis more reliable and the results more defensible. The results proved the consistency and reasonableness of the simulation tools and provided everyone involved with confidence about the analysis. The study also illustrated the value of using a range of performance measures and a sensitivity analysis. More generally, it proved the value of providing as much comparative information as possible before making a design decision. The results were generally consistent, and the end product was a set of clear, defensible, and well-supported conclusions. Although the experience gained through the application of CORSIM and VISSIM was in some ways unique to the study area, this experience can provide insight to other transportation professionals charged with selecting and applying these simulation models to similar networks. To that end, some of the characteristics of both models are contrasted.

Gaussian Process Metamodels for Sensitivity Analysis of Traffic Simulation Models

2013 ◽  
Vol 2390 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Biagio Ciuffo ◽  
Jordi Casas ◽  
Marcello Montanino ◽  
Josep Perarnau ◽  
Vincenzo Punzo
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Process ◽  
Traffic Simulation ◽  
Simulation Models

scholarly journals Gaussian Processes Proxy Model with Latent Variable Models and Variogram-Based Sensitivity Analysis for Assisted History Matching

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4290
Author(s):  
Dongmei Zhang ◽  
Yuyang Zhang ◽  
Bohou Jiang ◽  
Xinwei Jiang ◽  
Zhijiang Kang
Keyword(s):  
Sensitivity Analysis ◽  
Gaussian Processes ◽  
Latent Variable ◽  
History Matching ◽  
Latent Variable Models ◽  
High Dimensional ◽  
Model Parameters ◽  
Variable Model ◽  
Assisted History Matching ◽  
Proxy Models

Reservoir history matching is a well-known inverse problem for production prediction where enormous uncertain reservoir parameters of a reservoir numerical model are optimized by minimizing the misfit between the simulated and history production data. Gaussian Process (GP) has shown promising performance for assisted history matching due to the efficient nonparametric and nonlinear model with few model parameters to be tuned automatically. Recently introduced Gaussian Processes proxy models and Variogram Analysis of Response Surface-based sensitivity analysis (GP-VARS) uses forward and inverse Gaussian Processes (GP) based proxy models with the VARS-based sensitivity analysis to optimize the high-dimensional reservoir parameters. However, the inverse GP solution (GPIS) in GP-VARS are unsatisfactory especially for enormous reservoir parameters where the mapping from low-dimensional misfits to high-dimensional uncertain reservoir parameters could be poorly modeled by GP. To improve the performance of GP-VARS, in this paper we propose the Gaussian Processes proxy models with Latent Variable Models and VARS-based sensitivity analysis (GPLVM-VARS) where Gaussian Processes Latent Variable Model (GPLVM)-based inverse solution (GPLVMIS) instead of GP-based GPIS is provided with the inputs and outputs of GPIS reversed. The experimental results demonstrate the effectiveness of the proposed GPLVM-VARS in terms of accuracy and complexity. The source code of the proposed GPLVM-VARS is available at https://github.com/XinweiJiang/GPLVM-VARS.

Multiobjective Optimization for High Dimensional Expensively Constrained Black-Box Problems

2021 ◽  
pp. 1-59
Author(s):  
George Cheng ◽  
G. Gary Wang ◽  
Yeong-Maw Hwang
Keyword(s):  
Multiobjective Optimization ◽  
Trust Region ◽  
Adaptive Strategy ◽  
Black Box ◽  
Superior Performance ◽  
High Dimensional ◽  
Multi Objective Optimization ◽  
Semiconductor Substrate ◽  
Multi Objective ◽  
Computationally Expensive

Abstract Multi-objective optimization (MOO) problems with computationally expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches for MOO are often not suitable for high-dimensional problems and often do not support expensive constraints. In this work, the Situational Adaptive Kreisselmeier and Steinhauser (SAKS) method was combined with a new multi-objective trust region optimizer (MTRO) strategy to form the SAKS-MTRO method for MOO problems with expensive black-box constraint functions. The SAKS method is an approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The MTRO strategy uses a combination of objective decomposition and K-means clustering to handle MOO problems. SAKS-MTRO was benchmarked against four popular multi-objective optimizers and demonstrated superior performance on average. SAKS-MTRO was also applied to optimize the design of a semiconductor substrate and the design of an industrial recessed impeller.

Effective Global Sensitivity Analysis for High-Dimensional Hydrologic and Water Quality Models

2019 ◽  
Vol 24 (1) ◽  
pp. 04018057 ◽  
Author(s):  
Yogesh Khare ◽  
Christopher J. Martinez ◽  
Rafael Muñoz-Carpena ◽  
Adelbert “Del” Bottcher ◽  
Andrew James
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
High Dimensional ◽  
Quality Models ◽  
Global Sensitivity ◽  
Water Quality Models
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