Many operational rules and regulations were enacted years ago, before more recent technological innovations and the ability to harness Big Data analytics.
Because of this, the CAASD team is working with the FAA to look for opportunities to improve regulatory flexibility without any increase in air traveler risk. Simply put, the team wants to identify regulations that increase cost and inconvenience, without actually making anybody safer. Making changes to those areas promises to improve the efficiency of the entire system—not to mention the cost and convenience of air travel.
An early win in this area has to do with “separation standards” (rules governing how far apart aircraft need to be from one and other.) Based on analysis on data in HDP, the FAA has altered some of its separation standards and improved efficiency. By FAA estimates, enacting such changes at a single large airport is translating into nearly $20M in saved fuel and a reduction of nearly one-and-a-half years of cumulative passenger taxi and departure times per year.
CAASD is actively growing its data in multiple dimensions. Highly dense weather data is continually being transformed within HDP to help enable real-time design feedback for more efficient flight procedures. In addition, CAASD is expanding its Hadoop data lake to include operations from around the globe to help validate and tune the next generation of airborne collision avoidance systems. These systems will not only increase safety for operations today, but will also help pave the way for safe integration of new entrants into the NAS, including Unmanned Aircraft Systems (UAS).
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