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Open Access Publications from the University of California

Real Options Models for Better Investment Decisions in Road Infrastructure under Demand Uncertainty


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

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