Reducing complexity of transport-land use models

Transport-land use models are usually complex and computationally intensive, since they need to account for multiple factors. In this context, sensitivity analysis plays a crucial role in evaluating the reaction of model outputs to changes in the baseline scenario, although.
In this paper, we propose a systematic approach for the conduct of sensitivity analysis of complex transportation and land use models. In particular, we consider an analyst who is evaluating a complex simulation model across different scenarios. We rely on a new and different method that produces finite change sensitivity indices for the variation of exogenous variations across scenarios.
With respect to other methods (see e.g. Sevcikova et al., 2007 and 2011), calculation of the indices is computationally frugal and indices can be rapidly obtained for sectors and situations and plotted across the space. This feature is particularly appealing when the set of uncertain variables is particularly large since our procedure requires a relatively low number of model runs.
Furthermore, our approach is also able to identify the relevance of interactions among variables with few additional model runs.
An application to the case of scenarios in transport models through an analysis of the Gravity Land Use Model (G-LUM) proposed by Zhou et al. (2009) is also presented for illustrative purposes.

Sevcikova H. and Raftery A. and Waddell P., 2007: Assessing Uncertainty in Urban Simulations Using Bayesian Melding. Transportation Research Part B: Methodology, 41(6), pp. 652-659.
Sevcikova H., Raftery A. and Waddell P., 2011: Uncertain Benefits: Application of Bayesian Melding to the Alaskan Way Viaduct in Seattle. Transportation Research Part A, 45, pp. 540-553.
Zhou B, Kockelman K. and Lemp J., 2009: “Applications of Integrated Transport and Gravity-Based Land Use Models for Policy Analysis, ” Transportation Research Record, 2133, pp. 123-213.



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