Testing microsimulation uncertainty of the parcel-based space development module of the Baltimore PECAS Demo Model

A precise and stable microsimulation space development module is fundamental for supporting various policy decision-making exercises related to land development. This paper studies the dynamics or uncertainty of outputs of the parcel-based space development module of an integrated land-use and transport forecasting model—the Baltimore PECAS Demo Model. It is tested with two sub-studies: (1) running the model three times over the entire planning window from 2000 to 2030; and (2) running the model 30 times just one year ahead from 2000 to 2001. The outputs obtained are used to analyze such dynamics or uncertainty. Study results from the first sub-study show that, in general, the system is stable and consistent over runs and time, as supported by a set of paired t-tests. However, the coefficient of variation (COV) measuring the variation of estimated space quantity by category over four cross-section years indicates that the differences among runs are increasing over time through the planning window. The COV test over the second sub-study indicates the estimated space quantity is stable for most of the zones, except for a small portion of zones with a small space quantity.

[1]  Hjp Harry Timmermans,et al.  Uncertainty in travel demand forecasting models: literature review and research agenda , 2012 .

[2]  Ming Zhong,et al.  Developing and Applying a Parcel-Level Simulation of Developer Actions in Baltimore , 2008 .

[3]  Ming Zhong,et al.  Design and Development of a Statewide Land Use Transport Model for Alberta , 2007 .

[4]  Kara M. Kockelman,et al.  Uncertainty Propagation in an Integrated Land Use-Transportation Modeling Framework: Output Variation via UrbanSim , 2002 .

[5]  M. Wegener From Macro to Micro—How Much Micro is too Much? , 2011 .

[6]  Volker Kreibich Modelling car availability, modal split and trip distribution by Monte-Carlo simulation: A short way to integrated models , 1979 .

[7]  P. Waddell UrbanSim: Modeling Urban Development for Land Use, Transportation, and Environmental Planning , 2002 .

[8]  Martin Clarke,et al.  MICROSIMULATION METHODS IN SPATIAL ANALYSIS AND PLANNING , 1987 .

[9]  Martin Greenberger,et al.  Microanalysis of Socioeconomic Systems: A Simulation Study , 1962 .

[10]  Kazuaki Miyamoto,et al.  A land use-transport model for metropolitan areas , 1983 .

[11]  Caroline J Rodier,et al.  Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity? , 2002 .

[12]  Kara M. Kockelman,et al.  Three Methods for Anticipating and Understanding Uncertainty of Outputs from Transportation and Land Use Models , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[13]  Jennifer Duthie,et al.  UNCERTAINTY ANALYSIS AND ITS IMPACTS ON DECISION-MAKING IN AN INTEGRATED TRANSPORTATION AND GRAVITY-BASED LAND USE MODEL , 2010 .

[14]  M. Wegener Overview of Land Use Transport Models , 2004 .

[15]  Robert A. Johnston,et al.  Multivariate Uncertainty Analysis of an Integrated Land Use and Transportation Model: MEPLAN , 2006 .

[16]  Eric J. Miller,et al.  The case for microsimulation frameworks for integrated urban models , 2018, Journal of Transport and Land Use.

[17]  Kara M. Kockelman,et al.  Propagation of Uncertainty in Transportation Land Use Models: Investigation of DRAM-EMPAL and UTPP Predictions in Austin, Texas , 2003 .

[18]  E J Miller,et al.  THE INTEGRATED LAND USE, TRANSPORTATION ENVIRONMENT (ILUTE) MICROSIMULATION MODELLING SYSTEM: DESCRIPTION & CURRENT STATUS. IN: TRAVEL BEHAVIOUR RESEARCH. THE LEADING EDGE , 2001 .

[19]  Hjp Harry Timmermans,et al.  Probabilistic forecasting of time-dependent origin–destination matrices by a complex activity-based model system: effects of model uncertainty , 2013 .

[20]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[21]  Kara M. Kockelman,et al.  The propagation of uncertainty through travel demand models: An exploratory analysis , 2000 .

[22]  D. Goodin The cambridge dictionary of statistics , 1999 .

[23]  F. Stuart Chapin,et al.  A probabilistic model for residential growth , 1968 .

[24]  Paul Waddell,et al.  Parcel-Level Microsimulation of Land Use and Transportation : The Walking Scale of Urban Sustainability , 2011 .

[25]  Yousef Shafahi,et al.  The treatment of uncertainty in the dynamic origin–destination estimation problem using a fuzzy approach , 2015 .

[26]  J. Kain,et al.  Housing and Neighborhood Dynamics: A Simulation Study , 1985 .

[27]  M. Wegener,et al.  Microsimulation of Land Use , 2003 .

[28]  Everett M. Rogers,et al.  Innovation Diffusion As a Spatial Process , 1967 .

[29]  Robert A. Johnston,et al.  Univariate Uncertainty Analysis of an Integrated Land Use and Transportation Model: MEPLAN , 2005 .

[30]  F. Moura,et al.  The Influence of the Volume–Delay Function on Uncertainty Assessment for a Four-Step Model , 2014 .