Researchers from Arizona State University in the USA have developed an air traffic management software platform that integrates data from several different sources with AI to help mitigate the risk of human errors
The new software PARAATM (Prognostic Analysis and Reliability Assessment for Air Traffic Management) integrates artificial intelligence as well as radar and GPS signaling.
The team has released the software to the research community to expand their toolkit and process more complex large-scale data analytics scenarios. The researchers hope PARAATM, which will be adopted for domestic air travel in the USA over the upcoming decade, will significantly benefit the aviation sector over the next decade.
Yongming Liu, professor of mechanical and aerospace engineering in the School for Engineering of Matter, Transport and Energy, part of the Ira A. Fulton Schools of Engineering at Arizona State University, and director of the Center for Complex System Safety, received funding from the NASA University Leadership Initiative to create the novel air traffic management (ATM) software platform.
“We are among the first few groups to have access to this very large database shared by NASA,” Liu said. “By using the data to make predictions and action planning to mitigate the risk, it has turned out to be very successful.”
A cornerstone of their platform is to manage the human factor errors that compound risk, particularly among human air traffic controllers. Nancy Cooke, a professor in human systems engineering for The Polytechnic School, part of the Fulton Schools of Engineering, said that focusing on the impact of human behavior is vital to mitigate risk as the industry undergoes these major shifts.
“There are humans involved all across the flight experience, all the way from passengers to pilots, air traffic controllers and flight attendants,” Cooke said. “Looking at the capabilities and limitations of humans should be core considerations of what we design.”
With data provided by NASA, the team developed a platform to collect and analyze data to optimize flights. Once the aircraft is 200 miles (320km) away from the destination, their software can begin planning landing time to arrive safer and faster. The software has also been able to predict potential issues more than a minute in advance, maximizing response time to troubleshoot.
The future of flying
The team understands that the future of aviation safety relies on training qualified students as much as advancing technologies.
“We are shaping the education of the next generation by understanding the current one, using education to impact the future operation,” Liu said.
Qihang Xu, a researcher on the project, said that focusing on data sources to improve air traffic management has given him insight into the fundamentals of aviation and machine learning.
“The hands-on experience I gained from this project, especially in applying book knowledge to real-world scenarios, has shaped my research approach and allowed me to apply theoretical knowledge in practical situations,” he said.
The availability of new technology and data sources promises the possibility of reducing aviation gridlock in the sky and at airports, cutting weather-related delays and enabling air traffic controllers and pilots to see the same real-time display of air traffic for the first time. Additionally, modernizing the USA’s complex air transportation network helps ensure efficient fuel usage by airlines, reduced aircraft emissions and increased access to airports by the general aviation community.
“Airspace is going to get very complex,” Cooke says. “The whole way that the air traffic control system works is supposed to change under the next generation, but safety is always going to be the most important thing.”