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

Abstract : 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 model are assumed to follow a Gaussian process. 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 part of the Grenoble model. The number of parameters to be estimated are 90, and the constraints on them are linear. The performance of the proposed calibration methodology, is then compared to 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, which is the square of the L_2 norm of the difference between observed and calculated land use outputs.
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Parikshit Dutta, Elise Arnaud, Emmanuel Prados, Mathieu Saujot. Calibration of an Integrated Land-Use and Transportation Model Using Maximum-Likelihood Estimation. IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers, 2014, 63 (1), pp.167-178. ⟨10.1109/TC.2013.168⟩. ⟨hal-00748555⟩

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