The interaction between land use and transportation has long been the central issue in urban and regional planning.  Models of such interactions provide vital information to support many public policy decisions, such as land supply, infrastructure provision, and growth management. Both the transportation system and the land use system exhibit historical dependencies in policy decisions. For instance, the expansion of a roadway today will change travel demand patterns, and make certain other roads more or less likely to be expanded in the future. A specific land supply decision made at one point of time, by changing the relative attractiveness of other areas in the region, can have profound impact on future land supply decisions. Today’s land use decisions clearly influence future transportation policies and vice versa. 

This project examines the land use-transportation interaction from an evolutionary perspective—Once a certain set of goals are determined and pursued by politicians and planners, their land supply and transportation investment decisions are to a large extent driven by their previous decisions and the supply-demand dynamics in the urban system. Built upon this recognition of historical dependency and a transportation network growth model previously developed by the P.I., a model of the co-evolution of land use and transportation is proposed in this project. Different from existing integrated land use and transportation models that assume exogenous network investment decisions, the co-evolution model considers both land use growth and transportation network growth as endogenous and market-driven. The central research question is how market and policies translate into transportation facilities and land use developments on the ground. The co-evolution model achieves a novel Urban Growth Equilibrium, which is a useful concept for planning and policy analysis. An agent-based simulation approach is employed to integrate an existing land use model and the transportation network growth model. The resulting integrated co-evolution model is demonstrated in a series of policy sensitivity tests.