University of California Institute of Transportation Studies

Tools used to evaluate transportation infrastructure investments are typically deterministic and rely on present value calculations, even though it is well-known this approach is likely to result in sub-optimal decisions in the presence of uncertainty, which is pervasive in transportation infrastructure decisions. This dissertation proposes a framework based on real options and advanced numerical methods to make better road infrastructure decisions in the presence of demand uncertainty. A real options framework was developed to find the optimal investment timing, endogenous toll rate, and road capacity of a private inter-city highway under demand uncertainty. Traffic congestion is represented by a BPR function, competition with an existing road is captured by user equilibrium, and travel demand between the two cities follows a geometric Brownian motion with a reflecting upper barrier. The result shows the importance of modeling congestion and an upper demand barrier – features missing from previous studies. The real options framework was extended to study two additional ways of funding an inter-city highway project: with public funds or via a Public-Private Partnership (PPP). Using Monte Carlo simulation, the value of a non-compete clause was investigated for both a local government and for private firms involved in the PPP. Since road infrastructure investments are rarely made in isolation, the real options framework was extended to the multi-period Continuous Network Design Problem (CNDP) to analyze the investment timing and capacity of multiple links under demand uncertainty. No algorithm is currently available to solve the multi-period CNDP under uncertainty in a reasonable time. A new algorithm called “Approximate Least Square Monte Carlo simulation” is proposed and tested that dramatically reduces the computing time to solve the CNDP while generating accurate solutions