Assessing and integrating uncertainty into land-use forecasting

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 p...

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Bibliographic Details
Main Authors:	Hana Sevcikova, Mark Simonson, Michael Jensen
Format:	Article
Language:	English
Published:	University of Minnesota 2015-03-01
Series:	Journal of Transport and Land Use
Subjects:	Uncertainty Land Use Forecast Bayesian Melding UrbanSim Agent-based Models PSRC
Online Access:	https://www.jtlu.org/index.php/jtlu/article/view/614

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spelling	doaj-f8ad1a67dbd54293b0d1a67563a3eaf22021-08-31T04:38:16ZengUniversity of MinnesotaJournal of Transport and Land Use1938-78492015-03-018310.5198/jtlu.2015.614Assessing and integrating uncertainty into land-use forecastingHana Sevcikova0Mark Simonson1Michael Jensen2Puget Sound Regional Council University of WashingtonPuget Sound Regional CouncilPuget Sound Regional CouncilUncertainty 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.https://www.jtlu.org/index.php/jtlu/article/view/614UncertaintyLand Use ForecastBayesian MeldingUrbanSimAgent-based ModelsPSRC
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Hana Sevcikova Mark Simonson Michael Jensen
spellingShingle	Hana Sevcikova Mark Simonson Michael Jensen Assessing and integrating uncertainty into land-use forecasting Journal of Transport and Land Use Uncertainty Land Use Forecast Bayesian Melding UrbanSim Agent-based Models PSRC
author_facet	Hana Sevcikova Mark Simonson Michael Jensen
author_sort	Hana Sevcikova
title	Assessing and integrating uncertainty into land-use forecasting
title_short	Assessing and integrating uncertainty into land-use forecasting
title_full	Assessing and integrating uncertainty into land-use forecasting
title_fullStr	Assessing and integrating uncertainty into land-use forecasting
title_full_unstemmed	Assessing and integrating uncertainty into land-use forecasting
title_sort	assessing and integrating uncertainty into land-use forecasting
publisher	University of Minnesota
series	Journal of Transport and Land Use
issn	1938-7849
publishDate	2015-03-01
description	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.
topic	Uncertainty Land Use Forecast Bayesian Melding UrbanSim Agent-based Models PSRC
url	https://www.jtlu.org/index.php/jtlu/article/view/614
work_keys_str_mv	AT hanasevcikova assessingandintegratinguncertaintyintolanduseforecasting AT marksimonson assessingandintegratinguncertaintyintolanduseforecasting AT michaeljensen assessingandintegratinguncertaintyintolanduseforecasting
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Assessing and integrating uncertainty into land-use forecasting

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