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.     

The Bid-rent Land Use Model of the simple,efficient, elegant,and effective model of land use and transportation

Integrated land use/transportation forecasting models add significant policy and infrastructure alternatives analysis capabilities to the urban planning process. The financial, time, and staff requirements to develop these models has put them beyond the reach of most small to medium sized urban areas. This paper presents the land use allocation submodel of the Simple, Efficient, Elegant, and Effective model of land use and transportation (SE³M), an integrated land use and transportation forecasting model founded upon Economic Base Theory and Bid-rent Theory. The Bid-rent Land Use Model (BLUM) is an agent based, spatial competition model utilizing unique utility curves for willingness to pay and incomes for budget constrained abilities to pay for each agent. The model structure, estimation, calibration, implementation, and validation are presented. With a single year of land use data available, the validation approach used the Kappa Index of Agreement to spatially check model outputs against base year control data while controlling for agreement by chance. The U.S. territory of Guam is used as the case study/proof of concept implementation for this model framework. Once calibrated, BLUM could solve the spatial competition problem on Guam in less than two minutes of processing time with over 90% accuracy.