The random-utility-based multiregional input–output model: solution existence and uniqueness

Zhao, Yong; Kockelman, Kara M.

doi:10.1016/j.trb.2003.10.005

Cited by 46 publications

(31 citation statements)

References 7 publications

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“…(2) the commodity flows should be local and concentrated. The majority of the flow to the destination region should originate in the region offering the minimum total cost, defined as p n ij = b n j + d n ij (see (Zhao and Kockelman, 2004), Section 3.1.2 for explanation).…”

Section: Discussionmentioning

confidence: 99%

“…Interested readers are directed towards Ref. (Zhao and Kockelman, 2004) and Ref. (Hunt, 1997) for detailed description of the RUBMRIO model.…”

Section: Governing Equationsmentioning

confidence: 99%

“…It has been proven that the set of equation in eqn. (2) converge to a fixed point under certain regularity conditions on the states and the inputs (Zhao and Kockelman, 2004). As will be seen later, λ n is key in ensuring that the fixed point solution of the system is ISS.…”

Section: Governing Equationsmentioning

confidence: 99%

“…Since the input-output model varies spatially, between different geographical regions, they are called random utility based input-output (RUBMRIO) models. Typically they combine traditional input-output model with multinomial logit model for calculation of commodity flows and production prices between different regions (Zhao and Kockelman, 2004;Juri and Kockelman, 2004). The RUBMRIO model is usually solved iteratively, by applying a set of algebraic equations (Hunt, 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Particularly Kockelman et al have used the model extensively to forecast land use and transportation scenarios in Texas and United States (Du and Kockelman, 2012;Ruiz Juri and Kockelman, 2006). They have also suggested variants of the original RUBMRIO model, in order to accommodate various objectives (Huang and Kockelman, 2010;Zhao and Kockelman, 2004;Juri and Kockelman, 2004). However, a methodology for inverse solution of the RUBMRIO model, where inputs are designed based on desired output values is missing from the literature.…”

Section: Introductionmentioning

confidence: 99%

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Inverse identification of inputs of a random utility based model using optimal control

Dutta

2014

Computers, Environment and Urban Systems

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To cite this version:Parikshit Dutta. Inverse identification of inputs of a random utility based model using optimal control. Computers, Environment and Urban Systems, Elsevier, 2014, 46, pp.1 -11. <10.1016/j.compenvurbsys.2014 AbstractThe random utility based multiregional input-output (RUBMRIO) model is used by several integrated land use and transportation models (ILUTMs) to forecast commodity flows in a region and for spatial allocation of production activities. It makes use of multiregional input-output models which are based on random utility theory. In this work, we use optimal control techniques to find optimal final demand and transportation costs of a RUBMRIO model, that would lead to a desired level of commodity flow and production costs. At first, the RUBMRIO model is formulated as a discrete time dynamical system. It is shown, using Lyapunov argument, that the fixed point of the dynamical system is input-to-state stable (ISS). Then, a discrete time optimal control problem is formulated with states as commodity flows and production costs, and the final demand and transportation costs as control inputs. An optimization problem is then solved to obtain control inputs that would lead to desired commodity flows and production costs.Further, the proposed methodology is applied to a numerical example. It is shown that the optimal control based method can achieve user specified commodity flows and production costs up to an acceptable accuracy level.

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Section: Discussionmentioning

confidence: 99%

“…Interested readers are directed towards Ref. (Zhao and Kockelman, 2004) and Ref. (Hunt, 1997) for detailed description of the RUBMRIO model.…”

Section: Governing Equationsmentioning

confidence: 99%

Section: Governing Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%