Development of a Prototype Land Use Model for Statewide Transportation Planning Activities

Abstract

Future land use forecasting is an important input to transportation planning modeling. Traditionally, land use is allocated to individual traffic analysis zones (TAZ) based on variables such as the amount of vacant land, zoning restriction, land use planning and policy limitations, and accessibility, under an externally estimated control number in population and employment growth at the county level. This land use allocation approach does not consider agglomeration factors, the market equilibrium of supply and demand, and is not sensitive to different land use and transportation policy changes. To overcome the limitations of this conventional approach, this research project uses a new analytical approach, i.e., a combination of cellular automata (CA) and agent-based modeling methods to estimate future land use allocation. The major advantage of this modeling approach is its integration with FSUTMS models. The feedback cycle between land use and transportation models can simulate the interactions between the two. This study employs three indicators to compare the simulation accuracy between the integrated framework and standalone FSUTMS models, including link saturation in the transportation network, overall vehicle miles traveled (VMT), and vehicle hours traveled (VHT). The results show that the inclusion of the land use and transportation feedback in the integrated modeling framework produces better results than the land use of transportation model alone, can help modelers better simulate land use-transportation system, and help decision makers better understand the consequences of different scenarios of the land use and transportation planning.

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