Expertise: Aviation & Transportation

IATA predicts that as a result of the COVID-19 pandemic and due to the decline in demand for air travel, the aviation industry will lose $118 billion this year.

The aviation industry faced new health and aircraft operation requirements, which in turn necessitated the implementation of various organizational and technical measures.

According to the SITA 2020 Air Transport IT Insights study, the industry’s top priority was to accelerate the investment in contactless passenger data processing using mobile technology, remote and cloud-based IT services, computer vision-based IT systems for queue monitoring, controlling compliance with COVID-19 sanitary requirements (mask-wearing and social distancing), face recognition of VIP passengers and business lounge passengers, the development of systems to control and monitor aircraft movement in the airport, the introduction of new ground service systems, and more.

In 2021-2025, data analytics, artificial intelligence, and machine learning will be increasingly used by airports to upgrade operational efficiency levels and enhance the overall airport experience.

To help airport operators achieve Airport 4.0 standards, Softarex Technologies provides smart software solutions that can be used in different areas of the airport business. Below we describe the most important IT systems that are being actively implemented and developed in the aviation industry.

Aviation and Transportation

Maintenance

The use of Big Data, Embedded, and IoT technologies allows airlines and MRO (maintenance and repair) operators to systematize and analyze information about the condition of aircraft. It enables the airline to detect worn-out parts and replace them long before they break down. This significantly reduces aircraft maintenance downtime and improves aircraft utilization efficiency. The development of such systems is a future objective. Today, the market offers fleet maintenance systems that transmit information about the aircraft’s technical condition from the aircraft to the ground services. This helps quickly repair and maintain the aircraft.

The optimization of HR costs

The optimization of HR costs equires the development and implementation of best practices in HR management, special crew planning programs, and systems for crew mental health analysis. The implementation of HR management systems helps optimize HR costs. Crew planning remains one of the most acute problems in civil aviation, as the crew capacity should strictly correspond to the number of flights performed and take into account the health status and availability of the crew members. Various emotion analysis algorithms based on computer vision technology can be used to analyze the mental health of the crew.

Business processes are accelerated

Business processes are accelerated by automating the processes of interaction and mutual settlement of airlines with suppliers using Blockchain and smart contracts technologies. The interaction of airlines with passengers, passengers with each other and passengers with airports allows for a significant increase in revenue in airport retail networks.

AI-powered airport management systems

AI-powered airport management systems are also widely used in the aviation industry. The use of advanced analytics, modeling and simulation allows creating systems that can accurately forecast and plan resources to meet the airport KPIs and enable effective management of passenger flows and airport resources. Such systems will use data sets of flight, terminal and passenger information together with customized machine-learning algorithms to facilitate real-time decision-making in major airport performance areas:

Apron management — improving airport efficiency by streamlining aircraft turnarounds. The airport apron, flight line, or ramp is the area of an airport where aircraft are parked, unloaded or loaded, refilled, or boarded. Although the use of the apron is covered by various regulations, for example the lighting of vehicles, it is typically more accessible to users than the runway or taxiway.
Resource and staff optimization — determining and optimizing the staff capacity.
Delay and passenger flow management — optimizing airport operations by forecasting passenger flow and creating smoother and more predictable service.

Automated airport security systems

Automated airport security systems based on cloud computing and computer vision help airport security officers maintain the highest possible security levels without diverting staff from other areas. Our experts can implement the following features:

Facial recognition and facial identification
Facial search in a video stream and face search in photo databases
Multi-threaded search from many video sources and cameras
Monitor the behavior of large groups of people
Keep detailed records of the flow of people through certain areas
Predict and quickly respond to crowds or isolated situations
Emotions recognition using integral and local recognition methods: PCA, ICA, FLD, LFA, Fisher effects, hidden Markov models, and cluster analysis. Facial Actions Code System (FACS) and neural networks were implemented in the system. Extraction of deformations and movements methods: Point Distributions Model (PDM) system was implemented. Geometrical characteristics and appearance characteristics methods to identify a specific set of emotions were used.

The IT airport systems described above can be implemented only using such technologies as:

Cloud computing
Mobile technology
Big Data and Data analysis
Neural networks and Machine learning
Computer vision
Blockchain technology

These technologies allow developing various systems to address the challenges of aviation and airport management. The engineers of Softarex Technologies have the required knowledge, skills, and experience to implement projects in the aviation industry.

Meet our
experts

Meet our experts 1/2

Maksim

System Architect and Delivery Manager

In over 10 years of my professional career, I have developed, architected, and managed dozens of projects for the Manufacturing & Energy, Restaurant & Hospitality, Aviation, and IT industries. I have hands-on experience with Mobile technology, Computer vision, Machine learning, Predictive modeling, Cloud computing, and SaaS. I managed the development of a Computer vision-based kitchen management system for a large restaurant chain. We did a great job on this project, and I ‌consider it the highlight of my career.

Yauheni

Lead Data Science and Computer Vision Engineer

With MA and PhD degrees coupled by multi-year scientific research experience, I am responsible for the development of knowledge-intensive solutions based on Computer vision, Predictive modeling, Deep learning and Machine learning technologies. My responsibilities include planning and managing research, designing software architecture, developing project technical documentation and integrating research results into final products. My team and I always strive to apply the best practices of Computer vision and Machine learning to projects.