Addressing Uncertainty about Future Airport Activity Levels in Airport Decision Making (2012)

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136 Section 8.1 (part of Step 2 of the methodology) identified six modeling techniques to assess the cumulative impact of uncertainty: 1. Structure and logic diagrams, 2. Decision trees, 3. Influence diagrams, 4. Program flowcharts, 5. Stock and flow diagrams (system dynamics), and 6. Reference class forecasting. For the sake of brevity, only two of the most relevant and accessible techniques—structure and logic diagrams and ref- erence class forecasting—were described in Section 8.1. For the benefit of more technical readers, this appendix provides an overview of all six techniques. Structure and Logic Diagrams An S&L diagram is a graphical representation of a model where each box is a variable (input, intermediate output, out- put) and links between boxes are operations (add, multiply, divide, and so forth). S&L diagrams reflect cause-and-effect relationships among economic, financial, demographic, policy, and political factors. Figure F-1 is an example of a structure and logic diagram for estimating aircraft movements. Decision Trees To the extent that airport planning decisions may depend on the realization of uncertain events and, in turn, affect demand forecasts and/or airport performance, decision trees may be developed to illustrate the cumulative impact of these events and decisions. Decision trees combine chance nodes, decision nodes, and end nodes to represent a set of competing alternatives and help assess their implications. They are essential in under- standing the impacts of flexible planning strategies and help- ing design or select these strategies. An example of a decision tree is shown in Figure F-2. The figure illustrates a flexible airport strategy under uncertain introduction of aviation cap-and-trade policy. The decision tree represents the sequential development of airport actions in response to the event of imposing a cap-and-trade mechanism during a given period of time. However, the outcome or end nodes (represented in the dia- gram with a triangle) have not yet been quantified. In a real- life situation, the end nodes would provide information on the change in airport activity resulting from the occurrence of an event and from the action taken by airport manage- ment in response to that event. For example, the first end node (reading the figure from left to right) could be associ- ated with an X percent increase in airport revenue, the sec- ond end node could be associated with a Y percent increase in airport revenue, and the third could represent a Z percent reduction in revenue. Influence Diagrams An influence diagram is a simplified, graphical representa- tion of a decision situation. It is made of a series of nodes and arcs (i.e., arrows between nodes). Three types of arcs (functional, conditional, and informational) and four types of nodes are generally considered. The symbols typically used to represent different nodes and arcs are shown in Table F-1. Figure F-3 and Figure F-4 provide examples of influence diagrams in the context of airport activity forecasting and planning. Influence diagrams may be used as a basis for develop- ing computer tools that describe and simulate a system or as a description of mental models planners or managers use to assess the impact of their actions. They are often used as an alternative to decision trees, in particular when there are many variables to consider. A p p e n d i x F Modeling Techniques to Assess Cumulative Risk Impacts

137 Source: Hickling Corporation, 1990, p. 51. Figure F-1. Structure and logic diagram for estimating aircraft movements.

138 Figure F-2. Illustrative example of a decision tree—cap-and-trade policy. Symbol Purpose Decision node, corresponding to each decision to be made Uncertainty node, corresponding to each chance event or uncertainty to be modeled Deterministic node, corresponding to a special type of uncertainty whose outcomes are known once the outcomes of some other uncertainties are also known Value node (intermediate and/or outcome variable) Conditional arcs (influence between elements) Informational arcs (information communicated between elements and/or precedence) Functional arcs Table F-1. Symbols typically used to represent nodes and arcs in an influence diagram. Program Flowcharts Flowcharts may be used in the assessment of airport capital programs and/or major capital projects, where the sequence and/or timing of activities are important and where the cumu- lative impact of schedule risks needs to be evaluated. Flow- charts are sequential and are best understood as a simplified, graphical representation of a program or project schedule. Figure F-5 provides an example of a program flowchart. Program flowcharts are mainly used in airport planning exercises to determine the time a specific improvement may take to implement. As such, they can be used with certain milestones of airport activity—such as number of passen- gers using the airport—to determine time windows when decisions or actions about enhancements have to be made in order to ensure a smooth transition to a new level of airport activity. Since airports’ master plans are somewhat flexible with respect to the timing of their investments, a good use of flowcharts in this context may consist of combining them with preferred risk (or opportunity) response strategies to trigger revisions of the plan and adjust it based on changes in airport activity.

139 Jet Fuel Price Airport Revenues Crude Oil Price Price of airplane ticket Demand for air travel Gasoline Price Fleet composition Flight itineraries and destinations Airport’s market share Utilization of airport capacity Airport Operating Costs Figure F-3. Influence diagram for understanding the impacts of sustained increases in fuel prices. Cap on Emissions Airport Revenues Demand for air travel Fleet composition Flight itineraries and destinations Airport’s market share Utilization of airport capacity Airport Operating Costs Number of certificates purchased/ sold Price of airplane ticket Price of certificates Figure F-4. Influence diagram for understanding the impacts of introducing aviation cap and trade.

140 Figure F-5. Illustrative example of a program flowchart. Stock and Flow Diagrams (System Dynamics) Stock and flow diagrams are used in system dynamics to assess the impact of shocks to a system modeled as a series of stocks and flows. The idea behind these diagrams is simple: stocks are entities that accumulate or deplete over time, and flows are the rates at which the stocks accumulate or deplete in a defined unit of time. As such, stock and flow diagrams explicitly include the concept of dynamic analysis in the rela- tionship between variables. An example of a stock and flow diagram in the context of airport planning can be found in Figure F-6, where the analysis is performed for a single airport along two clearly identified outcomes—airport attractiveness to airlines and passengers. In this example, four feedback or causal loops are identified, each with different sets of stock and flow relations. The four loops feature the same type of reinforcement for both output variables and are identified as demand stimula- tion (positive reinforcement), airport growth (positive rein- forcement), airport congestion (negative reinforcement), and airport capacity adjustment (negative reinforcement). The system works through a series of measurable air travel activity indicators—stocks—including demand for air trans- portation, enplanements, commercial operations, general aviation operations, and the summation of these last two, total operations. Reference Class Forecasting Flyvbjerg recommends the use of reference class forecasting to address optimism bias and general uncertainty in demand forecasting for public works (Flyvbjerg, 2005). Reference class forecasting for a specific project involves the following steps: • Identify a group of similar past projects, called the refer- ence class. • Using data from projects within the reference class, estab- lish a probability distribution for the variable of interest (e.g., traffic levels).

141 Source: Bonnefoy and Hansman, 2005. Figure F-6. System dynamics analysis of a single airport. • Compare the specific project with the reference class dis- tribution in order to establish the most likely outcome for the new project. Applications in the transportation sector include guidance on dealing with optimism bias in project cost estimates for the UK DfT. Another example is Butts and Linton’s Joint Confidence Level approach to correcting optimism bias in project cost and schedule estimates for the National Aeronautics and Space Administration. The approach consists of developing probability distributions for project costs and schedule, based on historical project performance (Butts and Linton, 2009). Essentially, a “fat tail” is added to the right side of the distri- bution to accommodate for cost or schedule increases due to unknown-unknown events. That adjustment is reduced— along with the probability of cost growth—as the project progresses and more risks are being recognized. Importantly in this approach, corrections to the initial cost estimates are applied probabilistically and adjusted over time. As in refer- ence class forecasting, there is no need to identify and forecast the impact of specific events. Or in the words of Flyvbjerg: The outside view is established on the basis of information from a class of similar projects. The outside view does not try to forecast the specific uncertain events that will affect the particular project, but instead places the project in a statistical distribution of outcomes from this class of reference projects (Flyvbjerg, 2005, p. 140). There are, to our knowledge, no formal applications of reference class forecasting for aviation demand. However, informal use of this approach likely occurs—for example, comparing a forecast against traffic development at other similar airports.

Abbreviations and acronyms used without definitions in TRB publications: AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation