Developing statewide land use forecasting model and integrate with tdot’s statewide travel demand model

The dynamic nature of urban systems involves the interaction of different agents such as infrastructure, facilities, administration and individuals in an integrated environment. Transportation is crucial for sustainability of an urban system. The significant increase of private cars use had a major negative impact on the efficiency of transportation systems. The need for more research in the area of congestion management and travel demand modeling became crucial. This resulted in the development of the first generation of travel demand models in the 1950s. In the U.S., researchers immediately realized the interdependence of transportation systems and land use patterns. Land use models were developed that utilized economic theory and statistics to produce forecasts of future changes in land use, demographics and socio-economic characteristics of a case study area. It was obvious that changes in transport systems could affect the patterns of urban development and location choices of households and employment. On the other hand, major changes in land use patterns could affect the number of trips, their destinations and modes. The interdependence of transportation and land use patterns resulted in the development of integrated land use and transportation models (ILUTM). The first generation of land use models was introduced during the 1960s and were aggregate models of spatial interaction and gravity models. Then, utility-based econometric and discrete choice models were developed. The development of advanced micro-simulation land use models and activity-based travel demand models created the need for a new generation of integrated land use-transport systems. New models such as ILUTE and ILUMASS were developed and existing models such as UrbanSim, PECAS, and MUSSA were updated to facilitate the needs for advanced research in the field of integrated land use-transport modeling. Tennessee Department of Transportation (TDOT) developed a new statewide travel demand model in 2015. A number of land use inputs such as household, population, household by income, household by size, employment, and employment by category were derived from regional Metropolitan Planning Organizations (MPOs) socio-economic forecasts along with use of National Household Travel Survey with Tennessee add-on data. While the current travel demand model uses 2010 and 2040 as the base and future year of analysis, no land use model currently exists to provide additional future year data or ability to perform scenario planning. To strengthen scenario analysis and policy planning the travel demand model will need adequate land use inputs. However, currently there is no statewide land use model in TN that can be used to generate inputs for the travel demand model. A need for statewide land use model is imperative to obtain (1) accuracy of future year land use forecast that represent long range transportation improvements and planned zoning, (2) cumulative and indirect effects of transportation projects, (3) evaluation of economic effects of various state and regional policies, (4) land use changes because of rapid changes in travel behavior owing to emerging technologies, (5) accurate choices of residential locations because of emerging greener and tech-savy lifestyle choices, and finally (6) facilitation of the land use model to be integrated with the travel demand model. MPO level linked land use and travel models are becoming relatively common in larger urban areas of the US. However, with the exception of Oregon and Ohio, no states currently have statewide land use models in operation that are integrated with the statewide travel demand model. A key innovation of this project will be the development of a functional statewide land use model that can be linked to the TN statewide travel demand model. This will likely require novel approaches to a number of the system components including: the model structure, database design, algorithm implementation, computing performance, and validation techniques, to permit the desired sensitivity to policy changes.