The goal of this study is to identify transportation planning strategies that will lead to envisioned smart growth patterns. In pursuit of this goal, the study aims at achieving two objectives: (1) understand the dynamic working process of planning strategies and (2) design efficient transportation policies and investment plans that will result in optimal agglomeration patterns. Most urban areas today are either experiencing notable deagglomeration or agglomeration. The former leads to sprawl into suburbia and loss of vibrancy in downtown areas while the latter suggests increasingly high population density and overload of infrastructure systems. Realizing consequences of such trends, planners are now eager for “smart growth”, which aims at stimulating optimal urban agglomeration patterns, hence sustainable and livable communities. Unfortunately, despite of the existence of numerous sophisticated models on urban systems, designing the optimal planning strategies remains a challenge. One major reason is the intrinsic nature of current predictive models: they are designed to address the after-effects of certain strategy. The implication is that residents respond to given stimuli by either relocating or changing their activity patterns until the system achieves new equilibrium. The predictive models only address the static status of this new equilibrium. Such framework has several limitations. First, even with exhaustive data collection and rigorous modeling, the findings still cannot be directly transferred to future scenarios because of the rapid changes of technology and people’s value systems. It thus has little real “predictive” power. Second, it is unable to describe the transitioning process from the original equilibrium to the new one, which means it disregards the interactions between planners and residents once the strategy is implemented. In reality, planners are often willing and able to take more proactive roles to make adjustments based on residents’ reactions, making the planning a dynamic process. This study will establish a framework that addresses the interactions between all stakeholders and the transition process caused by the implementation of planning strategies. Dynamic transportation planning strategies will thus be designed to induce the agglomeration gradually to the desired pattern. The study will build on the combination of urban economic models and experimental economic approaches: First, factors influencing urban agglomeration will be summarized based on previous studies. “Homogeneous communities” will then be defined and used as the basic units: Tiebout Sorting suggests that people sharing similar socio-economic characteristics will naturally aggregate to get public goods of their common interests. Therefore, demographic characteristics, residence and employment locations may be used to define such “homogeneous communities”. These communities will be used as the basic units because their members respond similarly to certain incentives. This smart way of aggregation allows for enough analysis accuracy without suffering from the computational efficiency problems encountered by disaggregate agent-based models. The third step is to extend the classic urban economic models into irregularly-shaped urban areas in GIS. This step will establish a theoretical framework that links influential factors to every “homogenous community” across the urban area. The last step is experimental economic analysis based on this theoretical framework. The economic connections between stakeholders will be mimicked by players with simulated interactions. Through experiments, the working process of different transportation planning strategies can be observed, and the most efficient one will be selected. This proposed study will contribute significantly to integrated land use-transportation studies. Especially for the State of New York, where unfavorable deagglomeration and agglomeration are observed in different parts of the state, designing dynamic transportation planning strategies to stimulate “smart growth” is more than necessary. From this perspective, the proposed study directly addresses UTRC’s mission of planning, management, and responses to change.