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

Abstract

With an increasing number of private toll road projects constructed or proposed in the United States through various forms of public–private partnerships and with more states removing legal constraints to the entry of private roads into the existing public road system, there are imperative needs to analyze the welfare and financial implications of using private-sector capital in road financing for 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, and an evolutionary simulation model is applicable to large real-world networks. The calibrated evolutionary model for the Twin Cities, Minnesota, road network (7,776 nodes, 20,486 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 lane kilometers of private freeways. The average annual rate of investment returns would be 18.2% for 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 the total new capacity needed to build our way out of congestion. 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.