Airport 4.0: Technologies of the Smart Stand

Airport 4.0 - Technologies of the Smart Stand

Airport 4.0: Technologies of the Smart Stand

Reading Time: 4 minutes

Authors: Soufiane Daher of NACO and Christiaan Hen of Assaia.


While definitions vary, we consider a ‘smart stand’ to be an aircraft stand that uses different technologies to reach some level of awareness, coordination and/or automation.

Infrastructure Awareness

Sensors are key to smart stands as they observe their surroundings and create data that can be used for decision making or automation. Traditionally, sensors have been applied to the objects that needed to be tracked or measured – for instance, the use of GPS sensors to track vehicles. At airports however, there are not only many vehicles, but also equipment of interest may be owned by many different stakeholders. This makes the tracking of assets by individual sensors impractical, given the number of parties involved and the high cost it would incur.

An innovative alternative is to use existing camera infrastructure and Computer Vision software to capture data on what is happening at the apron. The camera acts as a single sensor to all objects in its field of view. Due to progress in Artificial Intelligence research, together with decreasing computing costs and the use of existing camera infrastructure, this can be a very cost-efficient method for generating a wealth of data.

Coordination: The Airport Brain

The large amount of real-time data generated requires analysis by smart decision support systems. These can either alert operators on situations of interest, or take action autonomously. The data can also be used to predict future outcomes enabling operators to act proactively. This means that deviations from normal operations can be identified even before they happen or at least as soon as they appear. As such, the cost of disrupted operations can be massively reduced. Furthermore, a system that observes and monitors all operations, enables airlines and ground handlers to move to management by exception rather than manually trying to observe all operations. This approach boosts employee productivity and lowers operating costs. At the same time, as the airlines’ operations become more predictable and less disrupted, the airport will be able to reduce operational buffers and handle more aircraft with the same amount of infrastructure.

Safety can be enhanced by detecting dangerous situations and alerting staff before incidents happen. A classic example is aircraft stands where ground service equipment is parked outside the designated area or where a bridge has not been fully retracted. As soon as the system is notified by the Airport Operational Database that an aircraft is inbound to that specific stand, an alert can be sent to an agent to resolve the dangerous situation and prevent a collision or prevent the aircraft from waiting (while burning fuel and emitting CO2) for the stand to become available. Where incidents cannot be prevented, they will at least always be identified. A rich incident dataset allows for root cause analysis and can act as an input to training programs and/or standard operating procedure changes.


Automation can take very simple and effective forms. One example is when a system alert automatically creates a job for an employee on duty instead of alerting their dispatcher first. Typically, these kinds of automations evolve over time. It is recommended to take a staged approach where in the initial phase, the human agent can judge the system’s recommendations before allowing it to create automated actions. If the system’s suggested actions are almost always correct however, decision-making can be done autonomously while re-assigning agents to other more critical tasks. Ultimately, one could envisage an autonomous airport where staff monitor system performance rather than the actual operation. While this remains many years away, it is important to lay the foundations for this future state while solving today’s challenges.

Another more physical form of automation is related to autonomous vehicles and objects, such as passenger bridges. The use of these autonomous systems relies on the quality of available data and the airport brain. Autonomous vehicles will typically have a very good understanding of their direct surroundings (through cameras and other sensors) but will rely on the airport brain to tell it where to go and what to do. An autonomous bridge informed by sensor technologies such as video analytics for instance could automatically retract shortly before aircraft departure and then pre-position before the next aircraft arrival.

Most of these technologies are not yet fully mature. Nevertheless, given the expected growth of the aviation industry post-COVID, these technologies will be required as there will simply not be enough human labour to handle aircraft as we do today. These kinds of autonomous systems also create safety benefits. A reduction in human resources required on the apron will also lead to a reduction in incidents with injuries or fatalities. Additionally, unlike typical human behaviour, systems can be programmed more rigidly to avoid unsafe situations.

Lastly, optimisation of the entire airport system (by the airport brain) and execution of tasks by autonomous systems, also means that there will be less inefficiency and, as a result, positive impact on sustainability. Today, handlers often come to an aircraft whenever it fits best into their own operation. Loaders, caterers, fuellers and cleaners, are often not synchronised and may all arrive at the same time. This means that some of these service providers have to wait while running their engines, leading to unnecessary costs and CO2 emissions.

A smart turnaround would ‘call’ each service provider to the aircraft at the optimal moment. This has the benefit of optimising single turnarounds as well as all simultaneous turnarounds at that moment in time. One further benefit is that the space required for these operations may be reduced, consequently allowing airports to optimise usage of their aprons over time.