Creating a seamless passenger experience through the Huawei Big Data Model

Could you give a brief overview of the Huawei Big Data Model and how it works?

The Huawei Big Data Model is akin to a library of themes, indicators, and algorithms. Data is stored in the library in a certain organisation mode. If we think of data as goods, the Huawei Big Data Model allows us to first aggregate the goods before we unload them onto the open space for sorting. Thereafter, the data is put on shelves where they can be easily picked up as needed.

In what ways can the Big Data Model be leveraged to improve the operations and maintenance (O&M) for airport operators?

Data-driven services facilitate more refined airport management and control in three areas:

1. Intelligent resource allocation
   Intelligent stand allocation within seconds is possible using the big data platform and artificial intelligence (AI) driven algorithms. This has improved operational efficiency and has driven down the time required for re-allocating stands for 1,000+ flights from four hours to only one minute. Using the big data platform spare capacity is created, and more flights can take off per day. Where an airport’s average flights per day stood at 10.24, when using the big data platform this meant the airport could fly 11 flights per day. With the Huawei big data platform, now four million passengers annually no longer require airside transfer buses. This is all down to automatic allocation and efficient scheduling and intelligent stand allocation, which runs over 60 allocation rules to help run intelligent check-in counters, intelligent stand allocations, boarding gate allocation and baggage carousel allocations.
2. Operational prediction
   Stand conflict prediction eliminates the need for aircraft to queue on taxiways and reduces safety hazards. The big data platform uses an algorithm model and real-time streams which are converged in a data warehouse. It also includes real-time error-free take-off and landing simulations which enable large-scale command. Simulation prediction and real-time display are accurate to 100 per cent.

3. Efficient ground service support
   Automatic, real-time data collection ensures efficient ground handling, as well as improved accuracy, efficiency, and manpower allocation. It enables the automatic collection of data from 18 flight support nodes. This system uses video analysis algorithms for nodes and an algorithm repository.

What benefits do passengers experience when the airport is using the Huawei Big Data Model? Can their journey become more personalised?

The Huawei Big data Model can improve the traveller experience in multiple scenarios. Big data can be used to tag passengers based on their preferences, behaviours, habits, and travel itineraries. If a passenger travels frequently at the same airport and has no poor record or contraband, big data identifies the passenger and saves time by fast-tracking them. If a passenger often waits in a VIP lounge and prefers to take a certain route, the airport service personnel can learn about such preferences through big data and provide personalised services. One ID technology enables passengers to enjoy self-service check-in and boarding, monitor their luggage in real time and view videos of their luggage when they are on the conveyor belt.

What operational efficiencies can airports expect to see when using big data? Can operations be predicted?

Airport operational efficiency can be improved by smart slot allocation and intelligent scheduling. Based on big data and AI technologies, Huawei’s smart slot allocation solution implements automatic and intelligent scheduling of slots, implementing intelligent scheduling with machine-based and manual-assisted scheduling. Airport operators can improve the utilisation rate of covered bridge seats by five per cent to 10 per cent. Moreover, operators can also enjoy higher predictability. For example, the Ground Traffic Centre (GTC) can predict the arrival/departure traffic of an airport in a certain period of time based on big data and arrange taxis or ride-hailing services in advance.

How does the Big Data Model and immediate vehicle taking improve the passenger flow? What time efficiencies can be created?

Ideally, the Ground Traffic Centre (GTC) can connect all buses, subways, taxis, and ride-hailing services to share comprehensive traffic data and achieve more efficient passenger flow through precise scheduling. Currently, not all of the integrated transportation scenarios are fully connected, e.g., taxi companies and online ride-hailing services. Airports provide the predicted traffic volume to taxi companies and online ride-hailing companies, so that passengers can take a ride after leaving the airport. The waiting time for a taxi will be no more than five minutes.

Can you explain how data-driven services facilitate more refined airport management and control?

A good example would be VTT (Variable Taxi Time). Previously, service-driven data is collected as the business goes on. Nowadays, data-driven services such as VTT, also known as flight ground taxi time, enable refined control. Previously, the time of taxiing from the runway to the aircraft’s seat was predicted based on the average time, and ground service personnel had to wait at the aircraft’s seat when the aircraft landed, wasting a lot of manpower and material resources. Flight taxiing data now accurately calculates how long each aircraft takes to taxi and to provide ground service to each aircraft.

Can you give any examples of airports which Huawei are working with to implement this model and the benefits that the airports have experienced?

The most successful case so far is the Shenzhen Airport. Through smart slot allocation, two million passengers are exempt from the ferry every year. Taxis queue for no more than five minutes after passengers exit the arrival hall. Pass through the pre-security check, ‘creditworthy’ passengers take the fast track to pass within one minute. Currently, we are working on precision advertising for this airport.

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