Assessing and integrating uncertainty into land-use forecasting

Authored by Hana Sevcikova, Mark Simonson, Michael Jensen

Date Published: 2015

DOI: 10.5198/jtlu.v8i3.614

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Uncertainty in land use and transportation modeling has received increasing attention in the past few years. However, methods for quantifying uncertainty in such models are usually developed in an academic environment and in most cases do not reach users of official forecasts, such as planners and policymakers. In this paper, we describe the practical application of a methodology called Bayesian melding and its integration into the land-use forecast published by the Puget Sound Regional Council, a metropolitan planning organization. The method allows practitioners to assess uncertainty about forecasted quantities, such as households, population, and jobs, for each geographic unit. Users are provided with probability intervals around forecasts, which add value to model validation, scenario comparison, and external review and comment procedures. Practical issues such as how many runs to use or assessing uncertainty for aggregated regions are also discussed.

Tags

models Travel demand transportation Location Urban-development Projections Inference