Welfare and Financial Implications of Unleashing Private Sector Investment Resources on Transportation Networks

With an increasing number of private toll road projects constructed or proposed in the U.S. through various forms of public private partnerships, and with more states removing legal constraints on the entry of private roads into the existing public road system, there are imperative needs to analyze the welfare and financial implications of utilizing private sector capitals in road financing for both public policy decisions and private investment decisions. This paper develops models of market entry, price, and capacity choices on mixed-ownership networks to address these research needs. A small-network equilibrium model enables theoretical analysis of welfare and financial effects of private toll roads on stylized parallel and serial networks, while an evolutionary simulation model is applicable to large real-world networks. The calibrated evolutionary model for the Twin Cities, Minnesota road network (7776 nodes, 20486 links) shows that under a free-entrance policy without any toll or capacity regulation on private roads, the private sector would invest $19.5 billion in the next 15 years to construct more than 400 lanekilometers of private freeways. The average annual rate of investment returns would be 18.2% for the private investors. The total social welfare would be improved by more than $6.7 billion, with only 16.4% of the welfare gains enjoyed by system users. The average toll on private roads would be $0.17/km, about seven times higher than that on public roads. Findings suggest that unleashing private sector investment resources alone can provide only one fourth of total new capacity needed to build our way out of congestion. In order to attract private sector investments, public policy makers do not have to, nor should they, guarantee short-term profits for private toll roads when there exist multiple private investment firms. Private investors can efficiently find optimal tolls and increase profits by making adjustments with adaptively-learned demand information.