ã€Global Network Technology Report】If you are an airport dispatcher, there are 1,700 dynamic landing and landing flights a day waiting for a parking space. Factors to be considered include whether or not the overnight flight is in accordance with the departure order of the next day. Is the time sufficient? And you only have about 300 parking bays to choose from, as well as the size. Some of them need to take an average of 15 minutes in the shuttle bus. Passengers want the plane to take off on time. What should you do?
This is the day-to-day work of dispatchers at the Capital International Airport, but soon they will usher in a new assistant - 24 hours online, with outstanding IQ. On December 20, Alibaba Cloud released the ET Aviation Brain at the Yunqi Conference and Beijing Summit. It allocated aircraft parking spaces using artificial intelligence methods such as operational optimization and machine learning. It is expected that 1,700 flights will be dispatched each day to help passengers to save 5,000 hours, greatly increasing Flight transfer efficiency, thereby reducing the delay rate.
As the largest airport in the country, Capital Airport has a passenger throughput of over 90 million passengers in 2016 and is one of the busiest airports in the world. In this giant passenger flow hub, huge passenger flow, aircraft flow and baggage flow are intertwined. How to ensure that each flight can efficiently transit, reduce delays, and help passengers to board a plane smoothly is an urgent problem to be solved. Now the Capital Airport is introducing innovative Alibaba Cloud artificial intelligence.
At the Capital Airport, there are an average of two planes landing every minute, with a daily flow of 1700 flights, and the corresponding parking space is about 300. The dispatcher will need to spend 2-3 hours a day to arrange the parking stand. They will memorize the aircraft type, the size of the parking stand, the position of the parking stand, the location of the runway, and other indicators. If there are unexpected conditions such as inclement weather, the flight rhythm will be disrupted. Reschedule as soon as possible.
The ET aviation brain wants to share the work pressure for airport dispatchers and improve the efficiency of airport turnover. In order to better arrange the 1700 flight stops, the aviation brain first learned the aircraft model, landing time, airport parking, runway, slide position, ferry capacity, quantity, and more than ten dimensions of basic knowledge. Create clever mathematical models, with specially designed intelligent algorithms, fully consider constraints and temporary adjustments.
Based on the cloud computing operating system independently developed by Alibaba Cloud, the ET Aviation Brain can refresh the parking space arrangement of 1,700 flights at Capital Airport in 50 seconds, make full use of the apron space, maximize the transfer efficiency of the aircraft, and reduce the number of parking spaces. Delays caused by irrational arrangements. At the same time, the utilization rate of the parking spaces on the Langqiao is increased by 10%, which means that there are 20,000 passengers who do not need to use the shuttle bus every day, which saves about 5,000 hours in total.
According to Ali Wanyun, chief scientist of machine intelligence, Wan Wanli said that the intelligent dispatching of airport parking places is just one of the functions of the ET aviation brain. The aviation brain also hopes to penetrate into other scenarios of aviation. Prior to this, Alibaba Cloud Tianchi platform had initiated the Smart Aviation AI Contest with Xiamen Airlines and Baiyun Airport and sent invitations to engineers worldwide to use intelligent algorithms to solve problems in aviation scenarios.
In the future, the ET Aviation Brain will continue to provide artificial intelligence solutions for flight intelligence recovery, airport ground staff scheduling, and airline route planning, creating smart aviation.
In fact, the Capital Airport is not the first scene in which Alibaba Cloud has intelligently deployed. As early as a year ago, when it was hungry, Aliyun had been used to dispatch 1.8 million riders.
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