Airport Business

APR 2016

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AIRPORT EFFICIENCY 26 airportbusiness April 2016 analytics use machine learning models and numerical analysis based on historical behav- iors and patterns to project future conditions. For example, if, from historical data, an air- port always changes its runway configuration when the wind changes to a certain direction, and based on the weather forecast, the wind at the airport will change to the direction after 2 p.m., then the model can predict a runway configuration change after 2 p.m. Runway configuration changes cause both approach patterns and taxi times to change. As a result, airlines, knowing such operational conditions ahead of time, can plan their routes and fuels accordingly to optimize their flights to the airport. By anticipating the operational conditions at an airport and conduct proactive actions to mitigate subsequent delay propagation, airlines and airports can significantly lower both direct and indirect operational costs and achieve bet- ter operational efficiency. The airport congestion prediction chart for SFO (on Page 24) shows both flight demand and predicted airport capacity values for each time period. On the top of the chart, a traffic light color system provides a quick glance into the congestion level at the airport. The green line indicates the congestion level at SFO and the scale is shown to the right of the y-axis. A mea- sure of 10 indicates that the airport is expected to be at its full capacity (100 percent utilization of the capacity). In Figure 1, the analytics pre- dicts that SFO will have departure flow con- gestion between 19:00 UTC and 22:00 UTC. Because the departure flight demand during this time period exceeds the predicted airport departure capacity (especially between 20:00 UTC and 21:00 UTC), taxi out delays are likely to occur. Airlines might choose to delay flights, hold flights at gate, fuel additional taxi out fuel or cancel flights to avoid extended taxi times. CALCULATING POTENTIAL SAVINGS In the abstract, the benefits of predictive ana- lytics are clear. Let's dig a little deeper to under - stand where savings are derived. Cost savings can be quantified across four categories: • FUEL BURN — Excess fuel carried due to uncertain surface operations can drive fuel burn inefficiency. • FLIGHT TIME — Runway queuing and taxi-out/taxi-in delays due to congestion can add up to significant operational costs when conditions are not anticipated. • INEFFICIENCY PROPAGATION — A single delay doesn't just impact that flight. Late arrival may lead to departure delay for the next flight mission; passengers may miss connections and crew timeout can delay flights downstream. • INTANGIBLES — Although difficult to quantify, brand erosion, decaying industry confidence and various other operational costs are incurred when operational efficien- cy is not optimized. An airline with 650 daily flights is expected to receive an average of $1.3M annual saving from fuel and flight time using predictive air- port analytics. EVALUATING PREDICTIVE SOLUTIONS Selecting the right predictive solution is critical in obtaining correct impact estimations. When looking at a predictive solution, it is critical to examine the following four attributes: • Superior weather forecast capabilities are a must. The best predictive models will fail if inaccurate weather data is fed into the sys- tem. Select a solution that has proven, global weather forecast capabilities. • Building an accurate model takes significant skill and robust set of data points. Look for solution providers who have put in the hard work to build sustainable algorithms and machine learning techniques. • No operation can afford to rework their entire operational framework. Look for a solution that is already integrated into existing systems. • Seek solutions that have taken sufficient time to validate and verify the accuracy of their predictive model. Quality solution pro- vides will be able to explain their testing methodologies. THREE CRITICAL BENEFITS OF PREDICTIVE ANALYTICS • REDUCED UNPLANNED FUEL BURN — With better taxi time, configuration and airport congestion predictions, carriers are able to burn less fuel during taxiing and carry less contingency fuel during the operations. As a result, carriers can reduce unplanned fuel cost. • REDUCE EXCESSIVE FLIGHT TIME — With better taxi time, configuration and airport congestion predictions, carriers are able to avoid unnecessary taxi out delay by taxiing toward the right departure runways. In addition, airlines are able to select the routes aiming toward the predicted arrival direction to avoid vectoring in the terminal airspace. For example, by acquiring better situational awareness at JFK airport, Delta saved an estimated 228 hours in excessive taxi time in just one quarter. • REDUCE DELAY PROPAGATION — Carriers can reduce initial flight delays by taking proactive actions using insights from predictive airport analytics, further miti- gating the downstream delay propagation impact. As adoption of this next wave of efficiency driving technology grows, airlines, airports and passengers alike will benefit. Early insight into congestion, runway configuration changes and taxi time at key airports will improve pro-active communications, optimize ground operations and enhance the f light planning process. Miller joined The Weather Com- pany in 1998 as a senior product manager for the Weather Pro prod- uct line, serving the broadcast and cable markets. He has also held the positons of director of content and new media for Intellicast.com and director of aviaton products. Since 2008, he has served as the The Weath- er Company 's vice president and general manager of the aviaton and government businesses. Miller over- sees the company's suite of aviaton solutons serving global civil, military, commercial, business and private aviaton markets as well as the company's Energy & Risk division. Mark D. Miller, Sr. Vice President & General Manager, Decision Support at The Weather Company, an IBM Business ABOUT THE AUTHOR Saving just one minute on 200 flights per day from the three major NYC airports could save $6.7 million.

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