The simple,efficient, elegant,and effective model (SE3M) of land use and transportation

Integrated land use/transportation forecasting models add significant policy and infrastructure alternatives analysis capabilities to the urban planning process. Historically, the financial, time, and staff requirements to develop one of these models has put them beyond the reach of most small- to medium-sized urban areas. The purpose of this paper is to present the large zone economic submodel of SE³M, an integrated model – founded upon economic base theory and bid-rent theory – that is reasonably accurate, yet simpler in form, function, and implementation than competing models. The US territory of Guam is used as the case study/proof of concept implementation for this model framework. The submodel presented here was validated against a horizon year with known data for zonal level population and employment totals together with control totals for the island as a whole. The model was able – across two base years and one validation, horizon year – to locate all jobs and a high percentage of the population on each zone on the island.     

Uncertainty analysis of the large zone economic module of the simple,efficient, elegant,and effective model (SE3M) of land use and transportation

Integrated land use and transportation forecasting models are used to assist decision-makers in the policy analysis and infrastructure capital improvement selection process. These models are typically given precise, point-estimate inputs that are mathematically linked, through a series of submodels, to forecasted model outputs. These point-estimate inputs represent an unrealistic level of precision and a growing body of research is focusing on statistical techniques to model uncertainty in model inputs and parameters and tracking the effects of this uncertainty through the various submodels to the model outputs. This paper presents an uncertainty analysis of the Large Zone Economic Module (LZEM) of the Simple, Efficient, Elegant, and Effective Model (SE³M) of land use and transportation. Three case-study implementations of the model are used to obtain a reasonably sound approximation of how uncertainty affects LZEM outputs: Guam, Puerto Rico, and Oahu, Hawaii. These case studies were the subject of an early transferability study with SE³M and were selected based on both their insularity and diverse physical, economic, and demographic geographies. The findings of this research demonstrate that LZEM has a robust framework, with the potential to estimate error both in the positive and negative direction under uncertain input/parameter conditions.