Sensitivity Analysis and Optimisation of a Land Use and Transport Integrated Model

Laurent Gilquin 1, 2 Thomas Capelle 3 Elise Arnaud 1 Clémentine Prieur 2, 1
1 AIRSEA - Mathematics and computing applied to oceanic and atmospheric flows
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, UGA - Université Grenoble Alpes, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
3 STEEP - Sustainability transition, environment, economy and local policy
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : Land Use and Transportation Integrated (LUTI) models have become a norm for representing the interactions between land use and the transportation of goods and people in a territory. Through the use of these models, urban planning policies and development scenarios can be evaluated. The calibration of LUTI models is a heavy task, involving gathering of massive amounts of data and the estimation of an important number of parameters. In this paper, we consider the calibration of the open-source LUTI model Tranus. Classical calibrations of Tranus rely on ad-hoc econometric techniques and time-consuming trial and error procedures. Here, we propose a two steps calibration procedure comprise of global sensitivity analysis and optimisation. The sensitivity analysis presented is based on the replication method for the estimation of Sobol' indices and generalised to take into account multivariate outputs. The optimisation step is an iterative process combining stochastic and deterministic procedures. Our calibration method is applied to a North-Carolina urban area. Compared to a previous ad-hoc procedure, our approach results in a significant improvement of the adjustment factors of Tranus while reducing drastically the calibration time.
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Submitted on : Tuesday, March 22, 2016 - 9:58:27 AM
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  • HAL Id : hal-01291774, version 1


Laurent Gilquin, Thomas Capelle, Elise Arnaud, Clémentine Prieur. Sensitivity Analysis and Optimisation of a Land Use and Transport Integrated Model. 2016. ⟨hal-01291774⟩



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