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Defining and Measuring Aircraft Delay and Airport Capacity Thresholds (2014)

Chapter: Chapter 5 - Future Trends in Improving Metrics

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Suggested Citation:"Chapter 5 - Future Trends in Improving Metrics." National Academies of Sciences, Engineering, and Medicine. 2014. Defining and Measuring Aircraft Delay and Airport Capacity Thresholds. Washington, DC: The National Academies Press. doi: 10.17226/22428.
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Page 54
Page 55
Suggested Citation:"Chapter 5 - Future Trends in Improving Metrics." National Academies of Sciences, Engineering, and Medicine. 2014. Defining and Measuring Aircraft Delay and Airport Capacity Thresholds. Washington, DC: The National Academies Press. doi: 10.17226/22428.
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Page 55

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54 This research effort has identified some additional metrics that would be beneficial to the industry. Some of these are focused on communicating delays to the general public so they can gain a better understanding and appreciation of opera- tional delays’ impacts on consumers. Others are more useful for those inside the industry. And, of course, public metrics also are useful for industry analysts and vice versa. Coming from stakeholders with very different viewpoints, there is concurrence that ideal capacity/delay metrics would be as follows: • Easily understandable by industry experts and lay people; • Able to communicate the impact of delays on the traveling public and tell the story that will resonate the benefit of new projects; • Used as a common measure at any airport, such as measuring in numbers of passengers instead of number of operations; and • Applied consistently across different airports. Future metrics describing the impact on passengers will assist in understanding the money and time that air traffic delays cost passengers. It is important to clarify that none of the available data really get at the passengers’ delay experience, which is how passengers relate to delays. Perhaps there should be new metrics regarding the ability to meet passenger/consumer expectations, to measure total trip time, missed connections, passenger tolerance, or passenger frustration. A more positive metric would better serve the industry and consumers. “Delay” is a negative measure or is perceived nega- tively, while “level of service” would be a positive metric of pas- senger perceptions. The inverse could be a passenger tolerance measure of delays. Metrics about meeting passenger/consumer expectations could encompass the following: • A more positive metric that better serves the industry and consumers, • Metrics not influenced by flight schedules, and • Metrics that the general public can understand and appreciate. As previously noted, curb-to-curb or door-to-door mea- sures likely will be key measures in the future. There is no agreement on how to factor in passenger time. Total trip time encompasses delays on more transportation modes than just the airport. Delay for a passenger may start when they leave their home/business—at the front door—and be encountered on roads, at parking facilities, or at ticket counters. Some pas- sengers, if they have a choice of multiple airports at either the origin or destination market, take into account such items as the expected travel time to/from the airport, time getting through the airport, and expected airfield delays. Future metrics also can be useful in gaining public sup- port of airport development needs. For local communities affected by increased flight operations (e.g., noise), measur- ing expected delay savings of potential capital projects at an annual average of 1 minute or 5 minutes per flight opera- tion does not sound very large or worthwhile even if it has dramatic impacts on fuel burn and overall passenger delay. A better metric that will resonate with the overall benefit of new projects is needed. Delays could be shown in distribu- tions rather than a specific number: peak delays and the dis- tribution of delays tend to show the impact better. Additional delay statistics are needed to supplement average delay, such as standard deviation (or ranges), maximum delays, peak-hour delays, bad weather delays, average delay on delayed flights, or overall total delays for all flight traffic. Another measure is the count or percentage of flights that arrive on time (as com- pared to unimpeded). Metrics in terms of passengers, such as numbers of passengers delayed, would also help to capture the overall impact of capacity constraint delays. Using a common metric for all airport areas, something like millions of annual passengers (MAP), may allow passenger- based metrics to work for balancing all areas: airside, landside, C H a P T E R 5 Future Trends in Improving Metrics

55 and terminal processors. This would require airfield delay to be translated into an annualized MAP value. If terminals and other facilities become overcrowded, then the analysis will take into account capacity measures in terms of passengers. Current measures used in the industry that have the best opportunity to meet the needs of the community and become a national standard for talking about delays include the following: • The annual costs of delay at this airport is $X, while the average cost per year to provide new capacity is $Y. • The percentage of flights that will encounter capacity con- straint delays of X minutes or more will increase from Y% in Year-1 to Z% in Year-2. • The average delay for delayed flights (or flights during peak periods) will increase from X to Y as well as the distribution and maximums of these delayed flights. It also should be possible to represent the down-line impact of initial delays with these metrics, similar to how the current FAA’s BCA guidelines give some estimates of propagated delay throughout the national aviation system. Once demand reaches a certain capacity, the impact on delay is multiplied. What is observed in analyses through the years is that if the demand-to-capacity ratio is below 0.8, delays are tolerable, as shown in Figure 3-5. As ratios get to 0.8, airports can handle delays as long as the ratio is not maintained for long periods so that there are opportunities to recover before another peak demand. Ratios consistently greater than 0.8 gen- erate delays due to capacity that tend to grow exponentially. This is true both on the ground and in the air. For example, the New York metropolitan area has so much airspace congestion that only a moderate disruption causes a big capacity/delay issue throughout the area. Other metrics that can be used include translating envi- ronmental constraints into delay measures, or communicat- ing delays as gallons of fuel used or emissions reductions. There also is a need for better data on actual travel vs. actual delays. Analysts are left to analyze the data in enough detail so they can estimate the amount that is attributable to delay, since some amount of expected delay is imbedded in the flight schedules. This may involve analyzing block times from many years ago when there was less congestion, but even those flights likely included some delay. Or, possibly if there will be analysis of enough flights, including those during lull times of the day, one can estimate nominal travel time (some of the FAA databases have taken this approach). Similarly, FAA esti- mates nominal taxi times in ASPM, then delays are calculated from those estimates. But obtaining unimpeded taxi times from actual flights currently is not possible, or even practical. However, having better data sources on actual unimpeded travel times would standardize these types of analyses and the resulting metrics. Congestion also may need to be defined in addition to capac- ity. There should be a way to explain the number of aircraft sitting on the airfield and their limiting impact (such as limiting the movement of other aircraft). There are good measures of delay for runway procedures, airspace infrastructure, and gate/ ramp layout. However, it is difficult to measure delays only for the taxiway structure, since many of the taxi delays are really a function of the runways or the ramp/gates. It is somewhat apparent to those knowledgeable in the industry that some air- ports are quite constrained by their taxiways (e.g., DCA) while others have few taxiway constraints (e.g., DEN), yet it is difficult to measure delays only due to the taxiways. Because the term delay is used for comparisons to sched- ule and nominal time, this causes confusion when trying to express delay savings to the public. There could be one term for delay that is a comparison to nominal/optimal time and another term for delay that is the comparison to flight sched- ule or extra time caused by airline operational issues. Although this project recognizes the advantages of adopting differ- ent terminology for operational delays or variances to airline schedules or simulation outputs, changing the way airlines or U.S.DOT use certain terms would be overly ambitious. There is the potential to create statistics based on not only dependability, but also predictability and consistency. These factors have significant implications for all of the stakeholders. Metrics should give an indication of what is really important to stakeholders.

Next: Appendix A - Delay Database Summary »
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TRB’s Airport Cooperative Research Program (ACRP) Report 104: Defining and Measuring Aircraft Delay and Airport Capacity Thresholds offers guidance to help airports understand, select, calculate, and report measures of delay and capacity. The report describes common metrics, identifies data sources, recommends metrics based on an airport’s needs, and suggests ways to potentially improve metrics.

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