Development of a prototype land use model for statewide transportation planning activities.

Final; 12/19/08-11/30/11.PDFTech ReportBDK77 977-03ForecastingLand use modelsTables (Data)ManualsFloridaUniversity of Florida. Dept. of Urban and Regional PlanningPeng, Zhong-RenZhao, Li-YuanYang, FeiUniversity of Florida. Dept. of Urban and Regional PlanningFlorida. Dept. of TransportationNTL-PLANNING AND POLICY-PLANNING AND POLICYNTL-PLANNING AND POLICY-Travel DemandNTL-PLANNING AND POLICY-Land UseUS Transportation CollectionFuture land use forecasting is an important input to transportation planning modeling. Traditionally, land use is allocated to individualtraffic analysis zones (TAZ) based on variables such as the amount of vacant land, zoning restriction, land use planning and policylimitations, and accessibility, under an externally estimated control number in population and employment growth at the county level. Thisland use allocation approach does not consider agglomeration factors, the market equilibrium of supply and demand, and is not sensitive todifferent land use and transportation policy changes. To overcome the limitations of this conventional approach, this research project usesa new analytical approach, i.e., a combination of cellular automata (CA) and agent-based modeling methods to estimate future land useallocation.CA models have been used extensively in modeling and simulating complicated spatio-temporal processes like land use change. Itcan model the changes of land use patterns over time and can simulate a variety of spatial processes and influences relevant for land usechanges. Agent-based models represent the interactions of different decision-making entities. The agent-based model provides a flexiblerepresentation of heterogeneous decision makers or agents, whose behaviors are potentially influenced by interactions with other agentsand with their natural and built environment.This study uses CA to capture the spatial relationships (e.g., clustering) of land development, as well as agglomeration factors. CArepresents complicated systems well and is thus a good method to show changes of land use patterns. However, the CA model alonecannot sufficiently explain the changes, because CA model is not sensitive to policy variables. Thus, the study also uses agent-basedmodels to capture the behavior of each agent, which makes it sensitive to policy changes. Agent-based models, unlike CA, can modelindividual decision-making entities\ue2\u20ac\u2122 behavior as well as their interactions. In addition, this study applies multinomial logit (MNL) modelto formulate the CA transition rule for different land types, which estimates the probability of future land use for each cell. Therefore, themodel this study developed is an MNL-CA-Agent land use model, which is called LandSys.In the developed LandSys model, land use changes are performed by the CA model, with external drivers as agents. These agentsinclude: employer, household, developer and government. The model is estimated and validated using a cell-based representation of land(50m x 50m). Then the estimated land use changes can be plugged into Florida Standard Urban Transportation Modeling Structure(FSUTMS) models as updated inputs. In response, FSUTMS models update their demand modeling of the transportation system. Theupdated traffic information (e.g., accessibility) is then fed back to the LandSys to capture the interaction between land use andtransportation and generate more accurate simulation results. LandSys simulates land use change at multiple spatial and temporal scales, aswell as representing decision making behaviors of households, employment, developers, and government policies. Future land use patternsand socioeconomic data can be produced to update those inputs of the transportation model. Policy scenarios, such as mixed land usegrowth management policies, can be simulated and analyzed for decision makers.The major advantage of this modeling approach is its integration with FSUTMS models. The feedback cycle between land use andtransportation models can simulate the interactions between the two. This study employs three indicators to compare the simulationaccuracy 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 andtransportation feedback in the integrated modeling framework produces better results than the land use or transportation model alone, canhelp modelers better simulate land use-transportation system, and help decision makers better understand the consequences of differentscenarios of the land use and transportation planning.17. Key Word: Land use modeling, transportation de