Using Land Use Model Output as Input for a Travel Demand Model Data Set

Small and medium-sized communities are continually challenged with planning for the future. Many agencies rely upon land use models to help with planning decisions, and travel demand models to predict the best ways of handling the effects of growth on the transportation infrastructure. In general, the staff tasked with developing input data sets for travel demand models and those tasked with overall land use planning reside in separate departments with separate missions. This separation creates a disconnect between building a travel modeling tool and obtaining and forecasting data for that tool. Unfortunately, the output data from the land use model is not always incorporated into the area’s travel demand model. By challenging the disconnect between the land use and travel demand models, communities can make the most of their modeling tools. This paper focuses on how land use modeling software and travel demand modeling software can be used in conjunction with one another. Using these tools in partnership, communities will be able to not only develop better data, but also better illustrate how alternative land use scenarios have different impacts on travel patterns and transportation decisions. Two projects for small and medium-sized areas have successfully used output from the land use modeling software CommunityViz as input for a travel demand model. Sumter, SC (2030 study area projected population of 110,000) and Mooresville, NC (2030 study area projected population of 110,000) each were able to incorporate land use model output into their respective Tranplan and TransCAD travel demand models. Using these two projects as examples, this presentation will help communities discover the benefits of this approach by discussing: (1) Background of travel demand and land use models; (2) Process of converting the land use output to input for the travel demand model; (3) Output from the travel demand model; (4) Output travel demand model measures of effectiveness; (5) Comparison of alternative land use/travel demand model runs; and (6) Lessons Learned.