A Polynomial Chaos Expansion Based Approach for Efficient And Robust Calibration of Stochastic Transportation Simulation Models

The output of a transportation simulation model is stochastic due to the presence of uncertainty in model inputs. Traditionally, Monte Carlo type (MC-type) random sampling is widely used to quantify the uncertainty. However, the low convergence rate makes it prohibitive for complex models in terms of computational burden. This paper proposes an efficient and novel framework for stochastic calibration of transportation simulation models based on polynomial chaos expansion (PCE) which aims to approximate the dependence of simulation model outputs on model inputs by expansion in an orthogonal polynomial basis. The proposed framework contains two parts. The first is to quantify the stochastic simulation outputs due to uncertainties in model inputs. The second is to calibrate model parameters based on the comparison between simulation outputs and observed measures. The proposed framework is applied to calibrate a simulation network developed in SUMO, an open-source microscopic transportation simulation platform. A two-dimensional random space is constructed based on stochastic model inputs. 16 parameters are calibrated using SPSA algorithm. K-S test statistic is used to specify the error in the objective function. Flow and speed are selected as performance measures. Finally, the aggregate error is observed to be reduced from 0.75 to 0.34 after calibration. The flow and speed distributions are also observed to get closer to the observed ones after calibration for each sensor location and each direction. The calibration results reveal that the proposed PCE technique is an efficient way for uncertainty quantification in the process of calibrating a stochastic transportation simulation model.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 24p

Subject/Index Terms

Filing Info

  • Accession Number: 01764320
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02504
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:26AM