Formulating LUTI Calibration As an Optimisation Problem: Estimation of Tranus Shadow Price and Substitution Parameters

Thomas Capelle 1 Peter Sturm 1 Arthur Vidard 2 Brian Morton 3
1 STEEP - Sustainability transition, environment, economy and local policy
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 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
Abstract : Cities and their employment catchment areas are focus points of economic activity, transportation, and social interactions. The need for land use and transport integrated modelling (LUTI modelling) as a decision aid tool in urban planning, has become apparent. Instantiating such models on cities, requires a substantial data collection, model structuring and parameter estimation effort; for conciseness, the latter is referred to here as calibration. This work is a partial effort towards the integrated calibration of LUTI models. It considers one of the most widely used LUTI models and softwares, Tranus. The usual calibration approach for Tranus is briefly reviewed. It is then reformulated as an optimisa-tion problem, in order to make it amenable to the systematic incorporation of constraints on parameters and additional data and to form a clear basis for future fully integrated calibration. The problem at hand concerns a dynamic system; an approach is shown how to " eliminate " parts of the dynamics in order to ease the parameter optimisation. We also discuss how to validate calibration results and propose to use synthetic data generated from real world problems in order to assess convergence properties and accuracy of calibration methods.
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Thomas Capelle, Peter Sturm, Arthur Vidard, Brian Morton. Formulating LUTI Calibration As an Optimisation Problem: Estimation of Tranus Shadow Price and Substitution Parameters. AAAI-15 Workshop on Computational Sustainability, Jan 2015, Austin, Texas, United States. ⟨hal-01237622⟩

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