Calibration of an Integrated Land-Use and Transportation Model Using Maximum-Likelihood Estimation

• Published in: IEEE transactions on computers 2014-01, Vol.63 (1), p.167-178
• Main Authors: Dutta, Parikshit, Arnaud, Elise, Prados, Emmanuel, Saujot, Mathieu
• Format: Article
• Language: English
• Subjects: Calibration; calibration and validation under uncertainty; Computational modeling; Computer Science; Gaussian process modeling; Gaussian processes; Integrated land use and transportation; Maximum likelihood estimation; Modeling and Simulation; supervised learning; Transportation; Uncertainty; Urban areas
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2013.168

The focus of this work is calibration of the land-use module of an integrated land-use and transportation model (ILUTM). The calibration task involves estimating key parameters that dictate the output of the land-use module. Hence, an algorithm based on maximum-likelihood estimation (MLE) is developed for calibration. Furthermore, the observed values of the outputs from the land-use module are assumed to admit a Gaussian error. The ILUTM methodology used here is TRANUS which is used to model the city of Grenoble in France. The aforementioned algorithm is then applied to calibrate the land-use module of the Grenoble model. The covariance of the Gaussian error term is assumed to be unknown. It is represented as a function of the land-use module inputs and "hyperparameters.â' The resulting MLE optimization problem has 111 parameters to be estimated, 90 of which are land use parameters and 21 are hyperparameters of the Gaussian covariance kernel. The performance of the proposed calibration methodology is then compared to the traditional calibration techniques used for land use and transportation models, when applied to the Grenoble land-use model. It is observed that the proposed method outperforms the traditional technique when compared based upon a given quantity of interest.