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This paper assesses the state of practice in megaregion-scale transportation planning according to a survey of metropolitan planning organizations (MPOs) and state departments of transportation (DOTs) conducted by researchers at the Center for Quality Growth and Regional Development at the Georgia Institute of Technology in 2012. The survey was distributed to all MPOs and DOTs. Results cover a broad range of topics, including the state of practice, governance structures, planning approaches, general obstacles to megaregion planning and decision making, and finance. Megaregion-scale projects and cross-jurisdiction initiatives, whether contained within previously identified megaregions or extended beyond these geographic designations, received similar ratings of effectiveness. The Cascadia megaregion reported the highest percentage of organizations with cross-jurisdiction initiatives; the Midwest reported the highest absolute number. Planning and decision making within the megaregion were characterized by numerous actors interacting through largely informal or ad hoc processes with contested leadership. That partner roles were unclear likely increased uncertainty and transaction costs. Responses indicated that inadequate funding was one of the largest obstacles to megaregion planning. Funding was stagnant for DOTs and especially for MPOs. Practitioners did not appear to be optimistic about innovative funding methods that would fill gaps in megaregion transportation planning. Finally, governance networks were densest within states, but large MPOs, DOTs, and federal agencies may play a bridging role among states. Future research may focus on decreasing transaction costs, overcoming funding obstacles, and promoting dense interstate governance networks.

This paper develops an optimization modeling framework to select strategies of land development and population and employment densities for a growing metropolitan area. The modeling core involves a non-linear commuting model, which accounts for spatial structure variables and is empirically estimated by Tobit regression. This commuting model is then embedded into a non-linear optimization model that allocates increments in the population and employment (activities) to available land, while minimizing the total future commuting costs under various combinations of land expansion boundaries and population and employment densities. The resulting minimum cost surface is approximated via polynomial regression and combined with land development and congestion cost functions to derive the overall optimal strategy. These models are estimated and calibrated with data from the Census Transportation Planning Package (CTPP) and Auditor’s property database, and are applied to the Fredericksburg metropolitan area, Virginia. The results demonstrate that the optimal development densities are very sensitive to the congestion cost function. A land development strategy that allows for limited sprawl might be a smart policy to reduce both regional vehicle mile travel (VMT) and related congestion and pollution.

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