Understanding and Accommodating Risk and Uncertainty in Toll Road Projects

Abstract

Forecasting traffic and toll revenues for new highway projects involves great uncertainty because of the inherent uncertainty in the models used to make forecasts. As private investment becomes more common in project financing, quantifying the levels of risk and uncertainty associated with such projects becomes critical. This paper represents a review of many key studies and reports dealing with uncertainty in traffic and revenue forecasts for highway projects. These studies found that tolled projects tend to suffer from substantial optimism bias in forecasts, with predicted traffic volumes exceeding actual volumes by 30% or more about half the time. Moreover, projects with greater uncertainty tend to overestimate Year 1 traffic volumes more and stabilize at lower final traffic volumes. But after one controls for added optimism bias in traffic forecasts (compared with nontolled projects), there is little difference in uncertainty levels between tolled and nontolled forecasts. A typical way to address uncertainty in traffic forecasts is through sensitivity testing via variations in key inputs and parameters. A more extensive and less arbitrary version of this, Monte Carlo simulation, can provide probability distributions of future traffic and revenue, although it tends to require many simulations, demanding greater computational effort and time, unless networks are streamlined. Nonetheless, if reasonable assumptions for model input and parameter distributions can be made, Monte Carlo simulation generates a variety of useful information and establishes the actual likelihood of loss (rather than more basic win–lose indicators from a limited set of stress tests).

Analytical Error Propagation in Four-Step Transportation Demand Models

“…Moreover, such techniques require assumptions about the input error PDF and the covariance of inputs, which are often unknown (Lemp and Kockelman 2009;Clay and Johnston, 2006). On the other hand, Krishnamurthy and Kockelman (2003) state that although sampling-based methods require substantial computer run time, they produce more accurate and realistic results compared to other methods.…”

“…Similarly, Lemp and Kockelman (2009) and Kriger et al (2006) expressed that the scenario-based SA methods are only able to determine a lower and an upper bound on model outputs, but certainly not a PDF for the output error. Moreover, according to Lemp and Kockelman (2009), as one of the main drawbacks of the scenario-based SA method, the number of defined scenarios is typically constrained to three or four.…”

“…The sampling-based methods require many simulations leading to great computational effort and time (Zhao and Kockelman, 2002;Lemp and Kockelman, 2009).…”

“…Moreover, according to Lemp and Kockelman (2009), as one of the main drawbacks of the scenario-based SA method, the number of defined scenarios is typically constrained to three or four.…”

“…In this light, it is required to provide a tool to measure forecast errors. Lemp and Kockelman (2009) mentioned that as the participation of the private sector in investing in the transport project increases, measuring the error associated with such projects becomes critical. Hartgen (2013) also expressed that the rapid increase in toll-based project financing as well as the natural risk of investing in this type of project for private investors has increased the requests for a more accurate traffic forecasts.…”