Guidebook for Advancing Collaborative Decision Making (CDM) at Airports (2015)

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38 Airports routinely make investment decisions based on their operational, strategic, safety, financial, and environmental objectives. The goal of these decisions is to identify the most pru- dent investment of public funds—the alternative that generates the most value per dollar invested over the lifecycle of the project. While airports always quantify the cost of a project, the ben- efits are usually treated in a qualitative matter. For some projects, alternatives may be treated quantitatively—the reduction in noise or increase in capacity may be measured. However, unless these quantities are converted into monetary terms by assessing the economic value of the benefits, which is often a challenging task, they cannot be directly compared to the costs of each alternative. Role of Benefit-Cost Analysis This Guidebook includes a primer on the development of a benefit-cost analysis (BCA)—a formal comparison of the monetized value of the project’s benefits against its costs. However, with few exceptions, BCAs are not required to be conducted for airport-funded projects. Since developing the BCA represents a cost in and of itself (either by hiring a consultant or assigning airport staff labor resources), airports must consider on a case-by-case basis whether the project warrants an analysis of monetized benefits. In making these decisions, the airport should con- sider the following questions: • What is the approximate lifecycle cost of the system? Establishing a rough order-of-magnitude (ROM) cost estimate can help to determine the merit of a BCA. If the ROM cost suggests that acquiring and maintaining the system represents a major expense, the value of the BCA increases. A major benefit of the BCA is ensuring that the airport receives maximum value for its limited capital funds. This is of particular importance in the case of larger investments that compete with other potential acquisitions and construction. • What other projects are competing for the same source of funds? In cases where there are dis- similar projects whose benefits may be difficult to compare, the BCA helps to clarify the value of each alternative in a succinct way. In cases where there are similar projects competing for funding, or different alternatives within one project, the BCA provides rationale for selecting the best option. For acquisition of commercial-off-the-shelf (COTS) systems, the BCA can provide more objective metrics than manufacturers’ claims. • What review and administrative processes are required for the investment decision? If the processes for arriving at an investment decision are complex, the value of the BCA increases. For example, if the investment decision requires environmental reviews, public participa- tion, stakeholder coordination, and/or formal approval by a board or authority, the ability to present a structured analysis with quantified benefit metrics can help to strengthen the case C H A P T E R 6 The Case for Quantifying ACDM Benefits

The Case for Quantifying ACDM Benefits 39 for the investment. The BCA provides decision-makers with comprehensive data on the net economic value of each alternative under consideration. • What risks and uncertainties exist that may affect successful implementation of the project? The risk management step of the BCA provides an opportunity to identify uncertainties that may jeopardize both the investment decision itself and the outcomes of the project. This helps to ensure a comprehensive treatment of risk factors. Moreover, if a quantified risk analysis is performed as part of the BCA, the lifecycle benefits and cost estimates derived in a BCA analy- sis can be associated with specific probabilities of success. This allows the decision-makers to dial in a specific willingness to assume risk. For example, in its own investment decisions, the FAA adopts a conservative approach by comparing the 80th percentile of the cost estimate against the 20th percentile of the benefit estimate. The BCA is not normally used to draft the specifications for the desired ACDM system. How- ever, the BCA methodology can be used to rate several design alternatives derived from the airport’s operational needs. The BCA can then be used to compare and rank the alternatives or individual options in a portfolio of candidate enhancements. Using an iterative process, the BCA methodology can help to fine-tune the design of the specifications by eliminating or revising elements that are not cost-effective. Even if the airport determines that a BCA is not warranted, the BCA principles presented here can add considerable value in supporting the investment decisions. Examples include the following: • Identifying specific benefit mechanisms helps to structure the discussion of the investment decision. • Benefits that are quantified, but which are not monetized, can be used to evaluate the relative benefit of alternatives. Possible examples include reduced risk of controller errors or reduc- tions in GHG emissions. • BCA principles encourage planning around lifecycle benefits and costs instead of focusing on the initial acquisition costs or benefits in a specific year. This is significant since the majority of costs often occur at the beginning of the lifecycle, whereas benefits often grow during the course of a lifecycle as traffic increases and the system matures. • BCA principles consider opportunity cost. This represents the economic value of alternative uses of the airport’s capital funds, including the value of no-build or no-buy alternatives. The BCA takes into account the change of the value of money over the course of the lifecycle, which can be significant for capital projects. • BCA principles encourage the airport management and planning staff to consider risks and uncertainties. Examples include uncertainties in activity forecasting, the risk of cost escala- tion, unpredictable fuel prices, etc. Note that when public entities are involved, such as airports or air navigation service providers, the BCA approach considers the total benefits and costs to society as a whole. The benefits analysis can be structured to identify how benefits accrue across individual stakeholders (i.e., airports, carriers, the traveling public, and neighboring communities), but the investment decision should be based on the combined benefits across all stakeholders: The first critical point is that BCA focuses on the “net social benefit” or “social return on investment.” In this context, the word social refers to societal benefits and costs, which include public, private, and government benefits and costs. Ideally, it is used to identify all impacts to society associated with taking an action, regardless of whether the impacts come as a cost or benefit or whether they are borne by the government or a direct beneficiary or a third party. In economic terms, BCA can identify which project maximizes net social benefit (Landau and Weisbrod 2009, p. 9). Even if an airport is considering an investment where it carries all the cost and the airlines accrue all the benefits, it would potentially have a positive BCA determination if the benefit-cost

40 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports ratio is greater than one. Subsequent to the investment, the airport can seek cost recovery through its regular rate-setting mechanisms and tenant negotiations so that the costs associated with achieving the benefits are equitably shared. Potential ACDM Benefits In the case of ACDM, the specific benefits will vary depending on the application. Based on pre- vious research and studies of existing CDM implementations, some of the key sources of benefits likely include the following. Note that several of the benefits identified would require participation by the ATC facility at the airport. This may preclude some of the benefits from being realized, or at least fully realized, through commercial ACDM applications intended primarily for use by the airport staff. In general, the greater and ardent the formal participation of the airports’ stakeholders, including ATC, the broader the range of potential benefits. Better Management of Airport Resources Coordination and data exchange can provide airports with more accurate information on resource use. Better information on short-term gate needs can help with tactically allocating gates to meet demand, while historical data on gate and taxiway usage can help both for long-term planning purposes and to monitor conformance with airport use agreements. In adverse condi- tions, data sharing may alert airport operators to events that will put a strain on their resources, such as multiple diversions heading to the airport in a short period of time. With more advanced awareness of the situation, airport operators can more effectively plan their response and utilize their resources in a way that minimizes the disruption to regular operations and to the passengers. With greater coordination among stakeholders, airports can recover more quickly from events that disrupt normal operations (such as snow or fog). This reduces the delays experienced by passengers and increases the throughput of the airport. Reduced Congestion Sharing information such as traffic demand, predicted delays, and impacts from traffic man- agement initiatives will give operators more insight into issues affecting a flight before it leaves the gate. Additional coordination based on this insight can result in a better balance between time spent at the gate versus time spent taxiing. Airports can achieve reductions in queue lengths and taxi-out times by holding aircraft at the gate in a virtual queue when departure demand is high rather than having all aircraft competing for runway access. Flights with specified departure times assigned by the FAA’s various automation initiatives can receive that information prior to leaving the gate. Delay can be absorbed in the Non-Movement Area with engines off instead of learning of delays after an aircraft has already begun taxiing. Shifting delay from the taxi phase to the gate or Non-Movement Area will reduce airline operating costs and fuel consumption, resulting in improved air quality, a reduction in GHG emissions, and reduced noise impacts. Figure 7 graphically represents the pool of benefits that can be targeted by a CDM system to reduce taxi delay. The benefit begins with the concept of unimpeded taxi time—the theoreti- cal minimum or ideal time required to taxi from the gate to the runway in the absence of any inefficiencies or delays. The difference between the unimpeded taxi time and the actual taxi time represents a pool of delay that is the target of potential improvements. This pool is the combi- nation of all sources of delays and inefficiencies, such as gate delays, taxiway inefficiency due to suboptimal sequencing, and runway queuing delay. The economic value of the pool, expressed in excessive fuel consumption, operating costs, etc., represents the so-called shortfall—the gap

The Case for Quantifying ACDM Benefits 41 between the actual system in place at the airport and the ideal, unimpeded one. The portion of this pool that can be reduced as a result of improvements attributable to CDM represents the economic value of the taxi delay reduction benefit. Data can be collected to assess the size and nature of the taxi delay shortfall. As an illustration of such an analysis, Figure 8 displays average taxi delay times for a sample of airports. The sam- ple consists primarily of larger commercial airports where taxi delays are more prevalent. The Figure 7. Potential benefits pool for reduced taxi delay (FAA 2010, pg. 35). 0 20 40 60 80 100 120 140 160 180 200 220 A N C A TL B D L B O S B W I CL E CL T CV G D CA DE N D FW D TW EW R FL L H N L H O U IA D IA H JF K LA S LA X LG A M CI M CO M D W M EM M IA M K E M SP M SY O RD PD X PH L PH X PI T PV D SA N SD F SE A SF O SL C SN A ST L 80% 73% 110% 84% 77% 79% 101% 72% 94% 64%91% 86% 132% 76% 77% 57% 82% 73% 109% 123% 83% 91% 76% 75% 64% 67% 58% 68% 75% 75% 82% 67% 131% 117% 96% 89% 74% 91% 63% 61% 87% 94% 77% To ta l t a x i-o u t d el ay ti m e (h ou rs /d ay ) Airport Figure 8. Average taxi-out delay (FAA 2010, pg. 35).

42 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports percentage value above each bar is the ratio between average actual taxi times and un impeded taxi times and is an approximate measure of the relative size of the potential benefit pool. Increased Flexibility and Predictability Data exchange and improved coordination can give airlines, ATC services, and airport opera- tors more flexibility to prioritize and re-sequence flights based on business needs or other con- cerns. For example, flight A that has already experienced a significant delay may be able to swap its departure time with flight B that is on-time or early in order to minimize the impact of the delay. Figure 9 shows a notional swapping algorithm similar to what might be implemented in CDM systems to improve the distribution of delays incurred by aircraft, and Figure 10 illustrates a sample swap. As shown in the example, even when there is no net reduction in delay, spreading the distribution of the delay across multiple flights can benefit operators and passengers. This is because when a flight incurs large delays, the risk increases for missed passenger connections, missed baggage transfers, and for the crew to reach its flight duty and flight time limits. The information on which such a decision would be based is typically known to the flight operator, while execution of the decision may fall within ATC’s jurisdiction. Collaboration is the key to recognizing and acting on such opportunities involving multiple parties. In addition to optimizing the sequencing of flights, ACDM can potentially increase predict- ability, for example by increasing the precision and accuracy of departure times. The runway departure process can be modeled as a queue with a probability distribution of service times (i.e., time intervals between successive departures). In an airport operation with low levels of predictability, the service time has a relatively high variance. A fundamental result of queuing theory is that the greater the variance in service times, the greater the resulting delays. Increasing predictability lowers the variance and should result in less delay. This is illustrated in Figure 11, which demonstrates how reducing the uncertainty in the service time results in a reduction of runway delay estimates generated using a queuing model applied to a small sample of airports. Improved Safety ACDM can potentially provide several opportunities for improving the safety of airport oper- ations. At its most basic, data sharing improves situational awareness among the stakeholders so that decisions can be made with more complete information on safety considerations. An overall reduction in surface congestion reduces potential conflicts and can lead to fewer acci- dents and incidents. More advanced applications of CDM could include methods of monitoring conformance to taxi paths, further reducing the risk of accidents from surface operational errors, aircraft tugging operations by non-pilots, or taxi route deviations. Figure 9. Sample decision process for flight-swapping algorithm (McInerney and Howell 2011, pg. 4).

The Case for Quantifying ACDM Benefits 43 Figure 10. Flight-swapping example for improved delay distribution (McInerney and Howell 2011, pg. 5). Figure 11. Impact of improved predictability on delay (FAA 2010, pg. 42).

44 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports Increased Opportunities for Data Collection and Assessment ACDM data exchange capabilities could provide an opportunity to collect and record oper- ational data that are important for airport planning purposes. New data archives may allow certain data processing actions to be automated and increase time available for other tasks. Further, additional tracking and record keeping may allow for real-time review of events or a centralized, post-event review of actions to identify areas where performance and practices can be improved. The timely sharing of operational data among stakeholders assists in the evalua- tion and improvement of the airport’s and stakeholders’ performance. For example, mowing and taxiway light maintenance can be planned around real-time lulls in operations rather than less accurate scheduled ones. System Consolidation Data exchange and improved situational awareness under ACDM can identify inefficient processes and consolidate tasks, resulting in a more streamlined operation. In certain configu- rations, modern ACDM systems may be able to replace a conglomerate of existing, standalone systems, each with their own hardware, display, and maintenance costs. In addition to stream- lining airport operations and planning functions and improving situational awareness, this may potentially result in a net cost savings. Other Benefits During the stakeholder outreach effort conducted as part of this project, the Research Team interviewed several airport operators and aircraft operators to document existing challenges and benefits. One airport operator reported that it had collected testimony from its stake holders in order to document perceived benefits. Examples of the benefits described by the airport’s stake- holders include the following: • Sharing of information, improved situational awareness, and more efficient decisions during IROPS and winter operations; • Increased throughput during winter weather events; • Improved schedule compliance; • Reduced fuel consumption and GHG emissions (210,000 gallons of fuel and 1,700 tons of CO2 emissions for the carrier in question); • Optimized scheduling of flight dispatchers, improving time-on-duty; and • Improved communications within meteorological technical group attributed to web-based technologies. As described in Chapter 3, certain airports frequently receive diversions from nearby airports. The airports that generate the diversions are often larger metropolitan airports, such as hub airports, whereas the receiving airports are often smaller, regional airports. A solitary aircraft diversion, for example due to a medical emergency, is not likely to substantially disrupt opera- tions at the receiving airport. Conversely, serial diversions of multiple aircraft, for example due to a winter weather event, can severely disrupt operations for an extended period of time. Such diversions have the potential to create large and unexpected peaks of demand that exceed exist- ing airport capacity, particularly at the terminal and in the taxiway-apron system. The Guidebook validation exercise conducted as part of this project identified several specific challenges associated with the handling of diversions: • Delays due to surface congestion; • Delays due to limited gate availability;

The Case for Quantifying ACDM Benefits 45 • Delays due to limited refueling and/or de-icing capacity; • Delays due to limited availability of federal security and inspection services; and • Financial penalties associated with extended tarmac delays. These problems are compounded by the inability to plan for the best use of the airport’s lim- ited resources due to high levels of uncertainty regarding the number and timing of inbound diversions. The presence of ACDM capabilities may not be sufficient to eliminate these challenges, but has the potential to reduce the negative impacts by providing an estimated arrival time for each diversion. Consequently, in the case of diversions, a major benefit of ACDM is the ability to reduce uncertainty. The impact, quantitatively, would be a reduction in the operating and fuel costs associated with delays, an accompanying savings in passenger travel time, and a potential reduction or elimination of DOT fines for extended tarmac delays. These benefits may be quan- tifiable and could then be included in a benefits analysis as described herein. Alternatively, they could be described qualitatively in support of the business case for the ACDM investment. Sample ACDM Benefits Analysis In order to illustrate the concepts described in this chapter, this section provides a step-by- step example of quantifying the value of a specific ACDM benefit. The example does not assume a specific ACDM platform or implementation, however. This example uses Richmond Inter- national Airport, a regional airport with approximately 100,000 annual operations and 1.6 million annual enplanements. The focus is on a regional airport to demonstrate that even small air- ports (relative to hubs) can accrue substantial economic benefits over the lifecycle of an ACDM solution. Note that in a full BCA, several preliminary steps are usually included which are skipped here. Figure 12 is a notional diagram that illustrates the steps normally required to conduct a BCA. This example assumes that several of these steps have already been completed, including establishing the overarching objectives, identifying alternative solutions, and researching and determining the appropriate methodology for evaluating the benefits. Also, this example only covers the calculation of benefits, so the steps that refer to analyzing costs and comparing ben- efits against those costs are not included. Figure 12. The BCA process.

46 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports Step 1: Establish Ground Rules and Assumptions This sample benefits analysis incorporates the following assumptions: • The project lifecycle is 20 years, beginning in 2017 and ending in 2036. • Future demand is given by the FAA’s Terminal Area Forecast. To account for uncertainty in the forecast and to ensure the benefits estimate is conservative, activity levels are assumed to be flat beyond 2025. • Economic values (including aircraft operating costs) are based on the guidance provided in the FAA’s Economic Information for Investment Analysis. Note that this example considers a relatively large investment in a NAS-wide system with periodic technology upgrades, warranting the use of a 20-year lifecycle. Smaller investments in COTS ACDM systems and decision support tools will most likely have shorter lifecycles—5 to 10 years, depending on whether upgrades are included in the business case. Step 2: Identify Benefits In the absence of ACDM, aircraft are assigned departure release times after push-back from the gate when certain types of demand management procedures are in effect, such as en route spacing programs. This is because air traffic controllers lack accurate push-back time estimates. This means that any Call for Release (CFR) delay must be taken during the taxi phase instead of at the gate. With ACDM and the associated data exchange capabilities, the departure release time can be predicted accurately prior to push-back. This allows for the CFR delay to be absorbed at the gate prior to engine start, creating fuel savings that translate into a cost savings for the aircraft operators. Benefit: Reduced fuel consumption due to absorbing CFR delay at gate instead of during taxi While this example only considers the monetary value of the fuel savings, there are several environmental benefits associated with reduced fuel consumption. These include reductions in noise and emissions. Environmental impacts can also be quantified in terms of their economic value to society, and should be considered in a complete BCA. Step 3: Estimate Benefits The following methodology was used to compute the CFR benefit: • Use 2012 data from the joint NASA/FAA Operational TMA/TBFM Repository (OTTR) sys- tem and the FAA’s ASPM database to measure the total taxi-out delay for each CFR flight. • Since non-CFR flights also have taxi-out delay, the average taxi-out delay for non-CFR flights was calculated using 2012 data from ASPM (3.34 min). • The net CFR delay savings was calculated as the total taxi-out delay for each flight, less the sum of a 10-minute buffer to account for uncertainty in surface modeling and the 3.34-minute buffer representing the average taxi-out delay for non-CFR flights. Flights with a total taxi-out delay less than the total buffer of 13.34 minutes were assigned a zero benefit. • The CFR delay benefit represents a shifting of delay from the taxi phase to the gate phase. To monetize the benefit, the difference between the Aircraft Direct Operating Cost (ADOC) during the taxi phase and the gate ADOC was used. This difference represents the portion of ADOC that can be attributed to the additional fuel consumption incurred during taxi. Average gate and taxi ADOC values were obtained from the FAA’s Economic Information for Investment Analysis by TAF aircraft category (i.e., air carrier, commuter and air taxi, general aviation, and military). To calculate specific ADOC values for Richmond Inter- national Airport, a weighted average was computed for each year in the lifecycle using the predicted share of operations in each category according to the aircraft operations forecast of the TAF.

The Case for Quantifying ACDM Benefits 47 Table 9 summarizes the analysis of the OTTR and ASPM data sample used to assess the net potential CFR delay savings. For CFR flights, the net delay in excess of the buffer was 10.4 minutes, resulting in a total CFR delay of 125 hours per year. In order to estimate the monetary value of this potential delay savings, the CFR delay was grown using the TAF, with growth capped beyond 2025, and mon- etized using the portion of the ADOC attributable to fuel consumption. The resulting benefit, expressed in 2014 dollars, is shown in Table 10. Note that when monetizing the benefits it is important to use real dollars throughout the lifecycle. Real dollars (or constant dollars) have been adjusted so as to exclude the effect of infla- tion. This allows for the benefits to be compared from year to year across the lifecycle. It also prepares the analysis for the final step, discounting the benefits to take into account the future value of money. In this step, the benefits are converted to their present value (PV) using a speci- fied rate used to discount future dollars. In this example, a real discount rate of 7% was used. This is the recommended value for the FAA’s own investment analyses, per the FAA’s Economic Information for Investment Analysis. Airports should consult with its planning and finance staff to determine the appropriate discount rate for its BCA studies. Flights in Sample With Net CFR Delay > 0 min Avg Net CFR Delay (min) Total CFR Delay (hrs) 42,272 717 10.4 125 Table 9. Sample benefits case—net CFR delay, Richmond International Airport, 2012. Year Operations Forecast CFR Delay Savings (hrs) Fuel Portion of ADOC (2014 $/hr) Benefit (2014 $) 2017 105,144 132 $404 $53,208 2018 107,938 135 $411 $55,648 2019 110,836 139 $419 $58,233 2020 113,842 143 $427 $60,970 2021 116,959 147 $436 $63,864 2022 120,191 151 $444 $66,925 2023 123,542 155 $453 $70,159 2024 127,016 159 $462 $73,575 2025 130,617 164 $472 $77,180 2026 130,617 164 $472 $77,180 2027 130,617 164 $472 $77,180 2028 130,617 164 $472 $77,180 2029 130,617 164 $472 $77,180 2030 130,617 164 $472 $77,180 2031 130,617 164 $472 $77,180 2032 130,617 164 $472 $77,180 2033 130,617 164 $472 $77,180 2034 130,617 164 $472 $77,180 2035 130,617 164 $472 $77,180 2036 130,617 164 $472 $77,180 Table 10. Sample benefits case—lifecycle benefits of CFR delay savings, Richmond International Airport.

48 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports Converting benefits into PV dollars takes into account the notion that benefits accrued today are more valuable than those earned in the future. This is because the savings generated by the benefit can be invested and generate a return during the interim years. The rationale for express- ing benefits in PV dollars is explained further in ACRP Synthesis 13: Effective Practices for Pre- paring Airport Improvement Program Benefit-Cost Analysis (Landau and Weisbrod 2009, p. 10): PV measures the current worth of a stream of future costs and a stream of future benefits (expressed in money terms), based on the concept of the “time value of money.” An annualized “discount rate” is applied to represent all future year benefits and costs in terms of their “present value.” This is done because, after adjusting for inflation, people would rather receive a dollar now than receive a dollar several years from now (since a dollar received now can be put to productive use that is foregone if the dollar is received later). The PV of a benefit value for a single year in the future is given by: 1 (Eq.1)PV B r t t t( )= + ∆ where Bt is the annual benefit in year t, expressed in real dollars, r is the real discount rate, and Dt is the number of years into the future that the benefit is accrued. The PV of the entire lifecycle stream of benefits is then simply the sum of the PV of the benefit accrued each year in the life- cycle. In this sample benefit analysis, the lifecycle benefit of taking CFR delay at the gate instead of during taxi is approximately PV $634,000 (see Table 11). Figure 13 uses this sample benefits case to compare and contrast the economic value of the associated benefits expressed in real dollars to their economic value expressed in PV dollars. Year ∆t Benefit (2014 $) PV Discount Factor Benefit (PV $) 2017 3 $53,208 0.816 $43,434 2018 4 $55,648 0.763 $42,454 2019 5 $58,233 0.713 $41,520 2020 6 $60,970 0.666 $40,627 2021 7 $63,864 0.623 $39,772 2022 8 $66,925 0.582 $38,951 2023 9 $70,159 0.544 $38,162 2024 10 $73,575 0.508 $37,402 2025 11 $77,180 0.475 $36,668 2026 12 $77,180 0.444 $34,269 2027 13 $77,180 0.415 $32,027 2028 14 $77,180 0.388 $29,932 2029 15 $77,180 0.362 $27,974 2030 16 $77,180 0.339 $26,143 2031 17 $77,180 0.317 $24,433 2032 18 $77,180 0.296 $22,835 2033 19 $77,180 0.277 $21,341 2034 20 $77,180 0.258 $19,945 2035 21 $77,180 0.242 $18,640 2036 22 $77,180 0.226 $17,421 Total: $633,946 Table 11. Sample benefits case—present value of benefits of CFR delay savings, Richmond International Airport.

The Case for Quantifying ACDM Benefits 49 The comparison between real and PV dollars highlights the notion that the further in the future benefits are accrued, the less they contribute toward the business case for the ACDM investment under consideration. This is significant, for two reasons: • The cost side of a benefit-cost comparison involving an ACDM investment is often dominated by hardware and software acquisition costs. These occur early in the lifecycle and are therefore discounted much less than the benefits, which tend to be spread throughout the lifecycle. In other words, ACDM acquisition costs are likely to carry more weight, on a relative basis, than benefits. • ACDM benefits attributable to operational efficiencies, such as fuel savings, often grow as traffic increases. However, this growth in benefits is offset by the discounted value of future dollars. Unless the benefits grow at a higher rate than the discount rate, they will decline in value through the lifecycle, as is the case in the example shown here. As shown in Table 11, in this particular example, the PV of a benefit accrued in the first year in the lifecycle is discounted by a total of 18.4%. Conversely, the PV of a benefit accrued in the tenth year of the lifecycle, is discounted by 55.6% and the PV of a benefit in the final year is discounted by 77.4%. Additional Steps As shown in Figure 12, in a complete BCA the PV of the lifecycle benefits would then be com- pared against the lifecycle costs, also expressed in PV dollars. The cost estimate normally consists of a number of cost components, including: • Acquisition costs • Operations and maintenance costs • Labor costs • Training costs • Tech refresh costs Figure 13. Benefits example—comparison between real dollars and present value.

50 Guidebook for Advancing Collaborative Decision Making (CDM) at Airports An important step in the BCA process is the risk and sensitivity analysis. The purpose of this step is to identify uncertainties in the analysis and their impacts on the results. Examples include forecasting errors and inherent uncertainties that are present when simplifying assumptions are used. This step helps ensure that the investment decision is conservative and allows the risk for cost overruns to be quantified and managed. Several statistical techniques are available to conduct the risk and sensitivity analysis, including Monte Carlo simulation. A full description of these techniques is beyond the scope of this Guidebook. The example described in this chapter does include specific measures to ensure the benefits estimate is conservative, however. These include the assumption of flat growth beyond 2025 and incorporating a 10-minute buffer to account for the uncertainty in surface modeling.